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221 Commits

Author SHA1 Message Date
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
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
UncleCode
c0fd36982d Update all documentation to import extraction strategies directly from crawl4ai. 2025-06-10 18:08:27 +08:00
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
591f55edc7 refactor(browser): rename methods and update type hints in BrowserHub for clarity 2025-04-06 18:22:05 +08:00
UncleCode
e1d9e2489c refactor(docs): update import statement in quickstart.py for improved clarity 2025-04-05 23:12:06 +08:00
UncleCode
b1693b1c21 Remove old quickstart files 2025-04-05 23:10:25 +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
UncleCode
14894b4d70 feat(config): set DefaultMarkdownGenerator as the default markdown generator in CrawlerRunConfig
feat(logger): add color mapping for log message formatting options
2025-04-03 20:34:19 +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
UncleCode
86df20234b fix(crawler): handle exceptions in get_page call to ensure page retrieval 2025-04-02 21:25:24 +08:00
UncleCode
179921a131 fix(crawler): update get_page call to include additional return value 2025-04-02 19:01:30 +08:00
Aravind Karnam
9e16a4bb26 Merge next and resolve conflicts 2025-04-02 12:18:23 +05:30
UncleCode
c5cac2b459 feat(browser): add BrowserHub for centralized browser management and resource sharing 2025-04-01 20:35:02 +08:00
UncleCode
555455d710 feat(browser): implement browser pooling and page pre-warming
Adds a new BrowserManager implementation with browser pooling and page pre-warming capabilities:
- Adds support for managing multiple browser instances per configuration
- Implements page pre-warming for improved performance
- Adds configurable behavior for when no browsers are available
- Includes comprehensive status reporting and monitoring
- Maintains backward compatibility with existing API
- Adds demo script showcasing new features

BREAKING CHANGE: BrowserManager API now returns a strategy instance along with page and context
2025-03-31 21:55:07 +08:00
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
UncleCode
bb02398086 refactor(browser): improve browser strategy architecture and lifecycle management
Major refactoring of browser strategy implementations to improve code organization and reliability:
- Move CrawlResultContainer and RunManyReturn types from async_webcrawler to models.py
- Simplify browser lifecycle management in AsyncWebCrawler
- Standardize browser strategy interface with _generate_page method
- Improve headless mode handling and browser args construction
- Clean up Docker and Playwright strategy implementations
- Fix session management and context handling across strategies

BREAKING CHANGE: Browser strategy interface has changed with new _generate_page method requirement
2025-03-30 20:58:39 +08:00
UncleCode
3ff7eec8f3 refactor(browser): consolidate browser strategy implementations
Moves common browser functionality into BaseBrowserStrategy class to reduce code duplication and improve maintainability. Key changes:
- Adds shared browser argument building and session management to base class
- Standardizes storage state handling across strategies
- Improves process cleanup and error handling
- Consolidates CDP URL management and container lifecycle

BREAKING CHANGE: Changes browser_mode="custom" to "cdp" for consistency
2025-03-28 22:47:28 +08:00
Aravind Karnam
d8cbeff386 fix: https://github.com/unclecode/crawl4ai/issues/842 2025-03-28 19:31:05 +05:30
UncleCode
64f20ab44a refactor(docker): update Dockerfile and browser strategy to use Chromium 2025-03-28 15:59:02 +08:00
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
UncleCode
c635f6b9a2 refactor(browser): reorganize browser strategies and improve Docker implementation
Reorganize browser strategy code into separate modules for better maintainability and separation of concerns. Improve Docker implementation with:
- Add Alpine and Debian-based Dockerfiles for better container options
- Enhance Docker registry to share configuration with BuiltinBrowserStrategy
- Add CPU and memory limits to container configuration
- Improve error handling and logging
- Update documentation and examples

BREAKING CHANGE: DockerConfig, DockerRegistry, and DockerUtils have been moved to new locations and their APIs have been updated.
2025-03-27 21:35:13 +08:00
Aravind Karnam
7be5427283 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-27 12:29:32 +05:30
UncleCode
7f93e88379 refactor(tests): remove unused imports in test_docker_browser.py 2025-03-26 15:19:29 +08:00
UncleCode
40d4dd36c9 chore(version): bump version to 0.5.0.post8 and update post-installation setup 2025-03-25 21:56:49 +08:00
UncleCode
d8f38f2298 chore(version): bump version to 0.5.0.post7 2025-03-25 21:47:19 +08:00
UncleCode
5c88d1310d feat(cli): add output file option and integrate LXML web scraping strategy 2025-03-25 21:38:24 +08:00
UncleCode
4a20d7f7c2 feat(cli): add quick JSON extraction and global config management
Adds new features to improve user experience and configuration:
- Quick JSON extraction with -j flag for direct LLM-based structured data extraction
- Global configuration management with 'crwl config' commands
- Enhanced LLM extraction with better JSON handling and error management
- New user settings for default behaviors (LLM provider, browser settings, etc.)

Breaking changes: None
2025-03-25 20:30:25 +08:00
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
6405cf0a6f Merge branch 'vr0.5.0.post5' into next 2025-03-25 14:51:29 +08:00
UncleCode
6eed4adc65 Merge branch 'vr0.5.0.post5' 2025-03-25 12:24:07 +08:00
UncleCode
bdd9db579a chore(version): bump version to 0.5.0.post6
refactor(cli): remove unused import from FastAPI
2025-03-25 12:01:36 +08:00
UncleCode
1107fa1d62 feat(cli): enhance markdown generation with default content filters
Add DefaultMarkdownGenerator integration and automatic content filtering for markdown output formats. When using 'markdown-fit' or 'md-fit' output formats, automatically apply PruningContentFilter with default settings if no filter config is provided.

This change improves the user experience by providing sensible defaults for markdown generation while maintaining the ability to customize filtering behavior.
2025-03-25 11:56:00 +08:00
Aravind Karnam
efa73257c5 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-24 21:57:29 +05:30
UncleCode
8c08521301 feat(browser): add Docker-based browser automation strategy
Implements a new browser strategy that runs Chrome in Docker containers,
providing better isolation and cross-platform consistency. Features include:
- Connect and launch modes for different container configurations
- Persistent storage support for maintaining browser state
- Container registry for efficient reuse
- Comprehensive test suite for Docker browser functionality

This addition allows users to run browser automation workloads in isolated
containers, improving security and resource management.
2025-03-24 21:36:58 +08:00
UncleCode
462d5765e2 fix(browser): improve storage state persistence in CDP strategy
Enhance storage state persistence mechanism in CDP browser strategy by:
- Explicitly saving storage state for each browser context
- Using proper file path for storage state
- Removing unnecessary sleep delay

Also includes test improvements:
- Simplified test configurations in playwright tests
- Temporarily disabled some CDP tests
2025-03-23 21:06:41 +08:00
UncleCode
6eeb2e4076 feat(browser): enhance browser context creation with user data directory support and improved storage state handling 2025-03-23 19:07:13 +08:00
UncleCode
0094cac675 refactor(browser): improve parallel crawling and browser management
Remove PagePoolConfig in favor of direct page management in browser strategies.
Add get_pages() method for efficient parallel page creation.
Improve storage state handling and persistence.
Add comprehensive parallel crawling tests and performance analysis.

BREAKING CHANGE: Removed PagePoolConfig class and related functionality.
2025-03-23 18:53:24 +08:00
UncleCode
4ab0893ffb feat(browser): implement modular browser management system
Adds a new browser management system with strategy pattern implementation:
- Introduces BrowserManager class with strategy pattern support
- Adds PlaywrightBrowserStrategy, CDPBrowserStrategy, and BuiltinBrowserStrategy
- Implements BrowserProfileManager for profile management
- Adds PagePoolConfig for browser page pooling
- Includes comprehensive test suite for all browser strategies

BREAKING CHANGE: Browser management has been moved to browser/ module. Direct usage of browser_manager.py and browser_profiler.py is deprecated.
2025-03-21 22:50:00 +08:00
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
UncleCode
6432ff1257 feat(browser): add builtin browser management system
Implements a persistent browser management system that allows running a single shared browser instance
that can be reused across multiple crawler sessions. Key changes include:

- Added browser_mode config option with 'builtin', 'dedicated', and 'custom' modes
- Implemented builtin browser management in BrowserProfiler
- Added CLI commands for managing builtin browser (start, stop, status, restart, view)
- Modified browser process handling to support detached processes
- Added automatic builtin browser setup during package installation

BREAKING CHANGE: The browser_mode config option changes how browser instances are managed
2025-03-20 12:13:59 +08:00
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
UncleCode
5358ac0fc2 refactor: clean up imports and improve JSON schema generation instructions 2025-03-18 18:53:34 +08:00
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
UncleCode
a24799918c feat(llm): add additional LLM configuration parameters
Extend LLMConfig class to support more fine-grained control over LLM behavior by adding:
- temperature control
- max tokens limit
- top_p sampling
- frequency and presence penalties
- stop sequences
- number of completions

These parameters allow for better customization of LLM responses.
2025-03-14 21:36:23 +08:00
UncleCode
a31d7b86be feat(changelog): update CHANGELOG for version 0.5.0.post5 with new features, changes, fixes, and breaking changes 2025-03-14 15:26:37 +08:00
UncleCode
7884a98be7 feat(crawler): add experimental parameters support and optimize browser handling
Add experimental parameters dictionary to CrawlerRunConfig to support beta features
Make CSP nonce headers optional via experimental config
Remove default cookie injection
Clean up browser context creation code
Improve code formatting in API handler

BREAKING CHANGE: Default cookie injection has been removed from page initialization
2025-03-14 14:39:24 +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
UncleCode
6e3c048328 feat(api): refactor crawl request handling to streamline single and multiple URL processing 2025-03-13 22:30:38 +08:00
UncleCode
b750542e6d feat(crawler): optimize single URL handling and add performance comparison
Add special handling for single URL requests in Docker API to use arun() instead of arun_many()
Add new example script demonstrating performance differences between sequential and parallel crawling
Update cache mode from aggressive to bypass in examples and tests
Remove unused dependencies (zstandard, msgpack)

BREAKING CHANGE: Changed default cache_mode from aggressive to bypass in examples
2025-03-13 22:15:15 +08:00
Aravind Karnam
cbb8755972 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-13 10:42:22 +05:30
UncleCode
dc36997a08 feat(schema): improve HTML preprocessing for schema generation
Add new preprocess_html_for_schema utility function to better handle HTML cleaning
for schema generation. This replaces the previous optimize_html function in the
GoogleSearchCrawler and includes smarter attribute handling and pattern detection.

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

This change improves schema generation reliability while reducing noise in the
processed HTML.
2025-03-12 22:40:46 +08:00
UncleCode
1630fbdafe feat(monitor): add real-time crawler monitoring system with memory management
Implements a comprehensive monitoring and visualization system for tracking web crawler operations in real-time. The system includes:
- Terminal-based dashboard with rich UI for displaying task statuses
- Memory pressure monitoring and adaptive dispatch control
- Queue statistics and performance metrics tracking
- Detailed task progress visualization
- Stress testing framework for memory management

This addition helps operators track crawler performance and manage memory usage more effectively.
2025-03-12 19:05:24 +08:00
dvschuyl
341b7a5f2a 🐛 Truncate width to integer string in parse_srcset 2025-03-11 11:05:14 +01:00
UncleCode
9547bada3a feat(content): add target_elements parameter for selective content extraction
Adds new target_elements parameter to CrawlerRunConfig that allows more flexible content selection than css_selector. This enables focusing markdown generation and data extraction on specific elements while still processing the entire page for links and media.

Key changes:
- Added target_elements list parameter to CrawlerRunConfig
- Modified WebScrapingStrategy and LXMLWebScrapingStrategy to handle target_elements
- Updated documentation with examples and comparison between css_selector and target_elements
- Fixed table extraction in content_scraping_strategy.py

BREAKING CHANGE: Table extraction logic has been modified to better handle thead/tbody structures
2025-03-10 18:54:51 +08:00
UncleCode
9d69fce834 feat(scraping): add smart table extraction and analysis capabilities
Add comprehensive table detection and extraction functionality to the web scraping system:
- Implement intelligent table detection algorithm with scoring system
- Add table extraction with support for headers, rows, captions
- Update models to include tables in Media class
- Add table_score_threshold configuration option
- Add documentation and examples for table extraction
- Include crypto analysis example demonstrating table usage

This change enables users to extract structured data from HTML tables while intelligently filtering out layout tables.
2025-03-09 21:31:33 +08:00
UncleCode
c6a605ccce feat(filters): add reverse option to URLPatternFilter
Adds a new 'reverse' parameter to URLPatternFilter that allows inverting the filter's logic. When reverse=True, URLs that would normally match are rejected and vice versa.

Also removes unused 'scraped_html' from WebScrapingStrategy output to reduce memory usage.

BREAKING CHANGE: WebScrapingStrategy no longer returns 'scraped_html' in its output dictionary
2025-03-08 18:54:41 +08:00
UncleCode
4aeb7ef9ad refactor(proxy): consolidate proxy configuration handling
Moves ProxyConfig from configs/ directory into proxy_strategy.py to improve code organization and reduce fragmentation. Updates all imports and type hints to reflect the new location.

Key changes:
- Moved ProxyConfig class from configs/proxy_config.py to proxy_strategy.py
- Updated type hints in async_configs.py to support ProxyConfig
- Fixed proxy configuration handling in browser_manager.py
- Updated documentation and examples to use new import path

BREAKING CHANGE: ProxyConfig import path has changed from crawl4ai.configs to crawl4ai.proxy_strategy
2025-03-07 23:14:11 +08:00
UncleCode
a68cbb232b feat(browser): add standalone CDP browser launch and lxml extraction strategy
Add new features to enhance browser automation and HTML extraction:
- Add CDP browser launch capability with customizable ports and profiles
- Implement JsonLxmlExtractionStrategy for faster HTML parsing
- Add CLI command 'crwl cdp' for launching standalone CDP browsers
- Support connecting to external CDP browsers via URL
- Optimize selector caching and context-sensitive queries

BREAKING CHANGE: LLMConfig import path changed from crawl4ai.types to crawl4ai
2025-03-07 20:55:56 +08:00
UncleCode
e1b3bfe6fb Merge branch 'vr0.5.0.post4' 2025-03-06 22:46:44 +08:00
UncleCode
f78c46446b feat(deep-crawling): improve URL normalization and domain filtering
Enhance URL handling in deep crawling with:
- New URL normalization functions for consistent URL formats
- Improved domain filtering with subdomain support
- Added URLPatternFilter to public API
- Better URL deduplication in BFS strategy

These changes improve crawling accuracy and reduce duplicate visits.
2025-03-06 22:45:57 +08:00
UncleCode
1b72880007 chore(version): bump version to 0.5.0.post3 2025-03-06 20:32:32 +08:00
UncleCode
29f7915b79 fix(models): support float timestamps in CrawlStats
Modify CrawlStats class to handle both datetime and float timestamp formats for start_time and end_time fields. This change improves compatibility with different time formats while maintaining existing functionality.

Other minor changes:
- Add datetime import in async_dispatcher
- Update JsonElementExtractionStrategy kwargs handling

No breaking changes.
2025-03-06 20:30:57 +08:00
UncleCode
2327db6fdc refactor(crawler): introduce CrawlResultContainer and simplify interfaces
Introduces a new generic CrawlResultContainer class to standardize return types and
improve type safety. Removes legacy parameter handling and simplifies method signatures.
This change makes the API more consistent and easier to maintain.

BREAKING CHANGE: Synchronous crawler methods now always return CrawlResultContainer
instead of raw CrawlResult or List[CrawlResult]. Legacy parameters have been removed
from method signatures.
2025-03-05 22:23:08 +08:00
UncleCode
fd02dc782d Merge branch 'main' of https://github.com/unclecode/crawl4ai 2025-03-05 17:15:48 +08:00
UncleCode
3a234ec950 fix(auth): make JWT authentication optional with fallback
Modify authentication system to gracefully handle cases where JWT is not enabled or token is missing. This includes:
- Making HTTPBearer auto_error=False to prevent automatic 403 errors
- Updating token dependency to return None when JWT is disabled
- Fixing model deserialization in CrawlResult
- Updating documentation links
- Cleaning up imports

BREAKING CHANGE: Authentication behavior changed to be more permissive when JWT is disabled
2025-03-05 17:14:42 +08:00
UncleCode
9e89d27fcd chore(version): bump version to 0.5.0.post2 2025-03-05 14:18:29 +08:00
UncleCode
b3ec7ce960 Merge branch 'vr0.5.0.post1' into next 2025-03-05 14:17:19 +08:00
UncleCode
baee4949d3 refactor(llm): rename LlmConfig to LLMConfig for consistency
Rename LlmConfig to LLMConfig across the codebase to follow consistent naming conventions.
Update all imports and usages to use the new name.
Update documentation and examples to reflect the change.

BREAKING CHANGE: LlmConfig has been renamed to LLMConfig. Users need to update their imports and usage.
2025-03-05 14:17:04 +08:00
UncleCode
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
UncleCode
9c58e4ce2e fix(docs): correct section numbering in deepcrawl_example.py tutorial 2025-03-04 20:57:33 +08:00
UncleCode
df6a6d5f4f refactor(docs): reorganize tutorial sections and update wrap-up example 2025-03-04 20:55:09 +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
395 changed files with 150327 additions and 5001 deletions

View File

@@ -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
}

35
.github/workflows/main.yml vendored Normal file
View File

@@ -0,0 +1,35 @@
name: Discord GitHub Notifications
on:
issues:
types: [opened]
issue_comment:
types: [created]
pull_request:
types: [opened]
discussion:
types: [created]
jobs:
notify-discord:
runs-on: ubuntu-latest
steps:
- name: Set webhook based on event type
id: set-webhook
run: |
if [ "${{ github.event_name }}" == "discussion" ]; then
echo "webhook=${{ secrets.DISCORD_DISCUSSIONS_WEBHOOK }}" >> $GITHUB_OUTPUT
else
echo "webhook=${{ 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) ||
format('💬 New discussion started: **{0}** by {1} - {2}', github.event.discussion.title, github.event.discussion.user.login, github.event.discussion.html_url) }}

13
.gitignore vendored
View File

@@ -255,3 +255,16 @@ continue_config.json
.llm.env
.private/
CLAUDE_MONITOR.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 +5,195 @@ 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
- **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
- *(crawler)* Add experimental parameters dictionary to CrawlerRunConfig to support beta features
- *(tables)* Add comprehensive table detection and extraction functionality with scoring system
- *(monitor)* Add real-time crawler monitoring system with memory management
- *(content)* Add target_elements parameter for selective content extraction
- *(browser)* Add standalone CDP browser launch capability
- *(schema)* Add preprocess_html_for_schema utility for better HTML cleaning
- *(api)* Add special handling for single URL requests in Docker API
### Changed
- *(filters)* Add reverse option to URLPatternFilter for inverting filter logic
- *(browser)* Make CSP nonce headers optional via experimental config
- *(browser)* Remove default cookie injection from page initialization
- *(crawler)* Optimize response handling for single-URL processing
- *(api)* Refactor crawl request handling to streamline processing
- *(config)* Update default provider to gpt-4o
- *(cache)* Change default cache_mode from aggressive to bypass in examples
### Fixed
- *(browser)* Clean up browser context creation code
- *(api)* Improve code formatting in API handler
### Breaking Changes
- WebScrapingStrategy no longer returns 'scraped_html' in its output dictionary
- Table extraction logic has been modified to better handle thead/tbody structures
- Default cookie injection has been removed from page initialization
## Version 0.5.0 (2025-03-02)
### 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=0.6.0
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
View File

@@ -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

146
README.md
View File

@@ -21,9 +21,9 @@
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 v0.5.0](#-recent-updates)
[✨ Check out latest update v0.6.0](#-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)
🎉 **Version 0.6.0 is now available!** This release candidate introduces World-aware Crawling with geolocation and locale settings, Table-to-DataFrame extraction, Browser pooling with pre-warming, Network and console traffic capture, MCP integration for AI tools, and a completely revamped Docker deployment! [Read the release notes →](https://docs.crawl4ai.com/blog)
<details>
<summary>🤓 <strong>My Personal Story</strong></summary>
@@ -253,24 +253,29 @@ 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:0.6.0-rN # Use your favorite revision number
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.6.0-rN # Use your favorite revision number
Our new Docker implementation will bring:
- Improved performance and resource efficiency
- Streamlined deployment process
- Better integration with Crawl4AI features
- Enhanced scalability options
# Visit the playground at http://localhost:11235/playground
```
Stay connected with our [GitHub repository](https://github.com/unclecode/crawl4ai) for updates!
For complete documentation, see our [Docker Deployment Guide](https://docs.crawl4ai.com/core/docker-deployment/).
</details>
@@ -347,7 +352,7 @@ 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():
@@ -420,8 +425,8 @@ if __name__ == "__main__":
```python
import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LlmConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai import LLMExtractionStrategy
from pydantic import BaseModel, Field
class OpenAIModelFee(BaseModel):
@@ -436,7 +441,7 @@ async def main():
extraction_strategy=LLMExtractionStrategy(
# Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
# provider="ollama/qwen2", api_token="no-token",
llmConfig = LlmConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')),
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.
@@ -500,31 +505,92 @@ async def test_news_crawl():
## ✨ Recent Updates
### Version 0.5.0 Major Release Highlights
### Version 0.6.0 Release Highlights
- **🚀 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`
- **🌎 World-aware Crawling**: Set geolocation, language, and timezone for authentic locale-specific content:
```python
crun_cfg = CrawlerRunConfig(
url="https://browserleaks.com/geo", # test page that shows your location
locale="en-US", # Accept-Language & UI locale
timezone_id="America/Los_Angeles", # JS Date()/Intl timezone
geolocation=GeolocationConfig( # override GPS coords
latitude=34.0522,
longitude=-118.2437,
accuracy=10.0,
)
)
```
- **📊 Table-to-DataFrame Extraction**: Extract HTML tables directly to CSV or pandas DataFrames:
```python
crawler = AsyncWebCrawler(config=browser_config)
await crawler.start()
try:
# Set up scraping parameters
crawl_config = CrawlerRunConfig(
table_score_threshold=8, # Strict table detection
)
# Execute market data extraction
results: List[CrawlResult] = await crawler.arun(
url="https://coinmarketcap.com/?page=1", config=crawl_config
)
# Process results
raw_df = pd.DataFrame()
for result in results:
if result.success and result.media["tables"]:
raw_df = pd.DataFrame(
result.media["tables"][0]["rows"],
columns=result.media["tables"][0]["headers"],
)
break
print(raw_df.head())
finally:
await crawler.stop()
```
- **🚀 Browser Pooling**: Pages launch hot with pre-warmed browser instances for lower latency and memory usage
- **🕸️ Network and Console Capture**: Full traffic logs and MHTML snapshots for debugging:
```python
crawler_config = CrawlerRunConfig(
capture_network=True,
capture_console=True,
mhtml=True
)
```
- **🔌 MCP Integration**: Connect to AI tools like Claude Code through the Model Context Protocol
```bash
# Add Crawl4AI to Claude Code
claude mcp add --transport sse c4ai-sse http://localhost:11235/mcp/sse
```
- **🖥️ Interactive Playground**: Test configurations and generate API requests with the built-in web interface at `http://localhost:11235//playground`
- **🐳 Revamped Docker Deployment**: Streamlined multi-architecture Docker image with improved resource efficiency
- **📱 Multi-stage Build System**: Optimized Dockerfile with platform-specific performance enhancements
Read the full details in our [0.6.0 Release Notes](https://docs.crawl4ai.com/blog/releases/0.6.0.html) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
### Previous Version: 0.5.0 Major Release Highlights
- **🚀 Deep Crawling System**: Explore websites beyond initial URLs with BFS, DFS, and BestFirst strategies
- **⚡ Memory-Adaptive Dispatcher**: Dynamically adjusts concurrency based on system memory
- **🔄 Multiple Crawling Strategies**: Browser-based and lightweight HTTP-only crawlers
- **💻 Command-Line Interface**: New `crwl` CLI provides convenient terminal access
- **👤 Browser Profiler**: Create and manage persistent browser profiles
- **🧠 Crawl4AI Coding Assistant**: AI-powered coding assistant
- **🏎️ LXML Scraping Mode**: Fast HTML parsing using the `lxml` library
- **🌐 Proxy Rotation**: Built-in support for proxy switching
- **🤖 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
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).
Read the full details in our [0.5.0 Release Notes](https://docs.crawl4ai.com/blog/releases/0.5.0.html).
## Version Numbering in Crawl4AI
@@ -540,7 +606,7 @@ 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):

View File

@@ -2,7 +2,9 @@
import warnings
from .async_webcrawler import AsyncWebCrawler, CacheMode
from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig
# MODIFIED: Add SeedingConfig and VirtualScrollConfig here
from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig, ProxyConfig, GeolocationConfig, SeedingConfig, VirtualScrollConfig
from .content_scraping_strategy import (
ContentScrapingStrategy,
WebScrapingStrategy,
@@ -22,6 +24,8 @@ from .extraction_strategy import (
CosineStrategy,
JsonCssExtractionStrategy,
JsonXPathExtractionStrategy,
JsonLxmlExtractionStrategy,
RegexExtractionStrategy
)
from .chunking_strategy import ChunkingStrategy, RegexChunking
from .markdown_generation_strategy import DefaultMarkdownGenerator
@@ -31,13 +35,13 @@ from .content_filter_strategy import (
LLMContentFilter,
RelevantContentFilter,
)
from .models import CrawlResult, MarkdownGenerationResult
from .models import CrawlResult, MarkdownGenerationResult, DisplayMode
from .components.crawler_monitor import CrawlerMonitor
from .link_preview import LinkPreview
from .async_dispatcher import (
MemoryAdaptiveDispatcher,
SemaphoreDispatcher,
RateLimiter,
CrawlerMonitor,
DisplayMode,
BaseDispatcher,
)
from .docker_client import Crawl4aiDockerClient
@@ -47,8 +51,9 @@ from .deep_crawling import (
DeepCrawlStrategy,
BFSDeepCrawlStrategy,
FilterChain,
ContentTypeFilter,
URLPatternFilter,
DomainFilter,
ContentTypeFilter,
URLFilter,
FilterStats,
SEOFilter,
@@ -62,17 +67,42 @@ from .deep_crawling import (
DFSDeepCrawlStrategy,
DeepCrawlDecorator,
)
# NEW: Import AsyncUrlSeeder
from .async_url_seeder import AsyncUrlSeeder
# C4A Script Language Support
from .script import (
compile as c4a_compile,
validate as c4a_validate,
compile_file as c4a_compile_file,
CompilationResult,
ValidationResult,
ErrorDetail
)
from .utils import (
start_colab_display_server,
setup_colab_environment
)
__all__ = [
"AsyncLoggerBase",
"AsyncLogger",
"AsyncWebCrawler",
"BrowserProfiler",
"LLMConfig",
"GeolocationConfig",
# NEW: Add SeedingConfig and VirtualScrollConfig
"SeedingConfig",
"VirtualScrollConfig",
# NEW: Add AsyncUrlSeeder
"AsyncUrlSeeder",
"DeepCrawlStrategy",
"BFSDeepCrawlStrategy",
"BestFirstCrawlingStrategy",
"DFSDeepCrawlStrategy",
"FilterChain",
"URLPatternFilter",
"ContentTypeFilter",
"DomainFilter",
"FilterStats",
@@ -99,6 +129,8 @@ __all__ = [
"CosineStrategy",
"JsonCssExtractionStrategy",
"JsonXPathExtractionStrategy",
"JsonLxmlExtractionStrategy",
"RegexExtractionStrategy",
"ChunkingStrategy",
"RegexChunking",
"DefaultMarkdownGenerator",
@@ -111,11 +143,22 @@ __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",
]
@@ -144,4 +187,4 @@ __all__ = [
# Disable all Pydantic warnings
warnings.filterwarnings("ignore", module="pydantic")
# pydantic_warnings.filter_warnings()
# pydantic_warnings.filter_warnings()

View File

@@ -1,2 +1,3 @@
# crawl4ai/_version.py
__version__ = "0.5.0.post1"
__version__ = "0.6.3"

File diff suppressed because it is too large Load Diff

View File

@@ -24,7 +24,7 @@ from .browser_manager import BrowserManager
import aiofiles
import aiohttp
import cchardet
import chardet
from aiohttp.client import ClientTimeout
from urllib.parse import urlparse
from types import MappingProxyType
@@ -130,6 +130,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
Close the browser and clean up resources.
"""
await self.browser_manager.close()
# Explicitly reset the static Playwright instance
BrowserManager._playwright_instance = None
async def kill_session(self, session_id: str):
"""
@@ -409,7 +411,11 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
user_agent = kwargs.get("user_agent", self.user_agent)
# Use browser_manager to get a fresh page & context assigned to this session_id
page, context = await self.browser_manager.get_page(session_id, user_agent)
page, context = await self.browser_manager.get_page(CrawlerRunConfig(
session_id=session_id,
user_agent=user_agent,
**kwargs,
))
return session_id
async def crawl(
@@ -435,7 +441,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
status_code = 200 # Default for local/raw HTML
screenshot_data = None
if url.startswith(("http://", "https://")):
if url.startswith(("http://", "https://", "view-source:")):
return await self._crawl_web(url, config)
elif url.startswith("file://"):
@@ -447,12 +453,17 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
html = f.read()
if config.screenshot:
screenshot_data = await self._generate_screenshot_from_html(html)
if config.capture_console_messages:
page, context = await self.browser_manager.get_page(crawlerRunConfig=config)
captured_console = await self._capture_console_messages(page, url)
return AsyncCrawlResponse(
html=html,
response_headers=response_headers,
status_code=status_code,
screenshot=screenshot_data,
get_delayed_content=None,
console_messages=captured_console,
)
elif url.startswith("raw:") or url.startswith("raw://"):
@@ -478,6 +489,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
) -> AsyncCrawlResponse:
"""
Internal method to crawl web URLs with the specified configuration.
Includes optional network and console capturing.
Args:
url (str): The web URL to crawl
@@ -494,6 +506,10 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Reset downloaded files list for new crawl
self._downloaded_files = []
# Initialize capture lists
captured_requests = []
captured_console = []
# Handle user agent with magic mode
user_agent_to_override = config.user_agent
@@ -507,10 +523,12 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Get page for session
page, context = await self.browser_manager.get_page(crawlerRunConfig=config)
# await page.goto(URL)
# Add default cookie
await context.add_cookies(
[{"name": "cookiesEnabled", "value": "true", "url": url}]
)
# await context.add_cookies(
# [{"name": "cookiesEnabled", "value": "true", "url": url}]
# )
# Handle navigator overrides
if config.override_navigator or config.simulate_user or config.magic:
@@ -519,23 +537,169 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Call hook after page creation
await self.execute_hook("on_page_context_created", page, context=context, config=config)
# Network Request Capturing
if config.capture_network_requests:
async def handle_request_capture(request):
try:
post_data_str = None
try:
# Be cautious with large post data
post_data = request.post_data_buffer
if post_data:
# Attempt to decode, fallback to base64 or size indication
try:
post_data_str = post_data.decode('utf-8', errors='replace')
except UnicodeDecodeError:
post_data_str = f"[Binary data: {len(post_data)} bytes]"
except Exception:
post_data_str = "[Error retrieving post data]"
captured_requests.append({
"event_type": "request",
"url": request.url,
"method": request.method,
"headers": dict(request.headers), # Convert Header dict
"post_data": post_data_str,
"resource_type": request.resource_type,
"is_navigation_request": request.is_navigation_request(),
"timestamp": time.time()
})
except Exception as e:
if self.logger:
self.logger.warning(f"Error capturing request details for {request.url}: {e}", tag="CAPTURE")
captured_requests.append({"event_type": "request_capture_error", "url": request.url, "error": str(e), "timestamp": time.time()})
async def handle_response_capture(response):
try:
try:
# body = await response.body()
# json_body = await response.json()
text_body = await response.text()
except Exception as e:
body = None
# json_body = None
# text_body = None
captured_requests.append({
"event_type": "response",
"url": response.url,
"status": response.status,
"status_text": response.status_text,
"headers": dict(response.headers), # Convert Header dict
"from_service_worker": response.from_service_worker,
"request_timing": response.request.timing, # Detailed timing info
"timestamp": time.time(),
"body" : {
# "raw": body,
# "json": json_body,
"text": text_body
}
})
except Exception as e:
if self.logger:
self.logger.warning(f"Error capturing response details for {response.url}: {e}", tag="CAPTURE")
captured_requests.append({"event_type": "response_capture_error", "url": response.url, "error": str(e), "timestamp": time.time()})
async def handle_request_failed_capture(request):
try:
captured_requests.append({
"event_type": "request_failed",
"url": request.url,
"method": request.method,
"resource_type": request.resource_type,
"failure_text": str(request.failure) if request.failure else "Unknown failure",
"timestamp": time.time()
})
except Exception as e:
if self.logger:
self.logger.warning(f"Error capturing request failed details for {request.url}: {e}", tag="CAPTURE")
captured_requests.append({"event_type": "request_failed_capture_error", "url": request.url, "error": str(e), "timestamp": time.time()})
page.on("request", handle_request_capture)
page.on("response", handle_response_capture)
page.on("requestfailed", handle_request_failed_capture)
# Console Message Capturing
if config.capture_console_messages:
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
# Basic console message with minimal content
entry = {
"type": message_type,
"text": message_text,
"timestamp": time.time()
}
captured_console.append(entry)
except Exception as e:
if self.logger:
self.logger.warning(f"Error capturing console message: {e}", tag="CAPTURE")
# Still add something to the list even on error
captured_console.append({
"type": "console_capture_error",
"error": str(e),
"timestamp": time.time()
})
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:
if self.logger:
self.logger.warning(f"Error capturing page error: {e}", tag="CAPTURE")
captured_console.append({
"type": "pageerror_capture_error",
"error": str(e),
"timestamp": time.time()
})
# Add event listeners directly
page.on("console", handle_console_capture)
page.on("pageerror", handle_pageerror_capture)
# Set up console logging if requested
if config.log_console:
def log_consol(
msg, console_log_type="debug"
): # Corrected the parameter syntax
if console_log_type == "error":
self.logger.error(
message=f"Console error: {msg}", # Use f-string for variable interpolation
tag="CONSOLE",
params={"msg": msg.text},
tag="CONSOLE"
)
elif console_log_type == "debug":
self.logger.debug(
message=f"Console: {msg}", # Use f-string for variable interpolation
tag="CONSOLE",
params={"msg": msg.text},
tag="CONSOLE"
)
page.on("console", log_consol)
@@ -562,14 +726,15 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
try:
# Generate a unique nonce for this request
nonce = hashlib.sha256(os.urandom(32)).hexdigest()
if config.experimental.get("use_csp_nonce", False):
nonce = hashlib.sha256(os.urandom(32)).hexdigest()
# Add CSP headers to the request
await page.set_extra_http_headers(
{
"Content-Security-Policy": f"default-src 'self'; script-src 'self' 'nonce-{nonce}' 'strict-dynamic'"
}
)
# Add CSP headers to the request
await page.set_extra_http_headers(
{
"Content-Security-Policy": f"default-src 'self'; script-src 'self' 'nonce-{nonce}' 'strict-dynamic'"
}
)
response = await page.goto(
url, wait_until=config.wait_until, timeout=config.page_timeout
@@ -619,7 +784,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
except Error:
visibility_info = await self.check_visibility(page)
if self.config.verbose:
if self.browser_config.config.verbose:
self.logger.debug(
message="Body visibility info: {info}",
tag="DEBUG",
@@ -733,6 +898,10 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
if config.scan_full_page:
await self._handle_full_page_scan(page, config.scroll_delay)
# Handle virtual scroll if configured
if config.virtual_scroll_config:
await self._handle_virtual_scroll(page, config.virtual_scroll_config)
# Execute JavaScript if provided
# if config.js_code:
# if isinstance(config.js_code, str):
@@ -767,12 +936,15 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Handle wait_for condition
# Todo: Decide how to handle this
if not config.wait_for and config.css_selector and False:
# if not config.wait_for and config.css_selector:
config.wait_for = f"css:{config.css_selector}"
if config.wait_for:
try:
# Use wait_for_timeout if specified, otherwise fall back to page_timeout
timeout = config.wait_for_timeout if config.wait_for_timeout is not None else config.page_timeout
await self.smart_wait(
page, config.wait_for, timeout=config.page_timeout
page, config.wait_for, timeout=timeout
)
except Exception as e:
raise RuntimeError(f"Wait condition failed: {str(e)}")
@@ -806,20 +978,48 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
if config.remove_overlay_elements:
await self.remove_overlay_elements(page)
# Get final HTML content
html = await page.content()
if config.css_selector:
try:
# Handle comma-separated selectors by splitting them
selectors = [s.strip() for s in config.css_selector.split(',')]
html_parts = []
for selector in selectors:
try:
content = await page.evaluate(
f"""Array.from(document.querySelectorAll("{selector}"))
.map(el => el.outerHTML)
.join('')"""
)
html_parts.append(content)
except Error as e:
print(f"Warning: Could not get content for selector '{selector}': {str(e)}")
# Wrap in a div to create a valid HTML structure
html = f"<div class='crawl4ai-result'>\n" + "\n".join(html_parts) + "\n</div>"
except Error as e:
raise RuntimeError(f"Failed to extract HTML content: {str(e)}")
else:
html = await page.content()
# # Get final HTML content
# html = await page.content()
await self.execute_hook(
"before_return_html", page=page, html=html, context=context, config=config
)
# Handle PDF and screenshot generation
# Handle PDF, MHTML and screenshot generation
start_export_time = time.perf_counter()
pdf_data = None
screenshot_data = None
mhtml_data = None
if config.pdf:
pdf_data = await self.export_pdf(page)
if config.capture_mhtml:
mhtml_data = await self.capture_mhtml(page)
if config.screenshot:
if config.screenshot_wait_for:
await asyncio.sleep(config.screenshot_wait_for)
@@ -827,9 +1027,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
page, screenshot_height_threshold=config.screenshot_height_threshold
)
if screenshot_data or pdf_data:
if screenshot_data or pdf_data or mhtml_data:
self.logger.info(
message="Exporting PDF and taking screenshot took {duration:.2f}s",
message="Exporting media (PDF/MHTML/screenshot) took {duration:.2f}s",
tag="EXPORT",
params={"duration": time.perf_counter() - start_export_time},
)
@@ -852,12 +1052,16 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
status_code=status_code,
screenshot=screenshot_data,
pdf_data=pdf_data,
mhtml_data=mhtml_data,
get_delayed_content=get_delayed_content,
ssl_certificate=ssl_cert,
downloaded_files=(
self._downloaded_files if self._downloaded_files else None
),
redirected_url=redirected_url,
# Include captured data if enabled
network_requests=captured_requests if config.capture_network_requests else None,
console_messages=captured_console if config.capture_console_messages else None,
)
except Exception as e:
@@ -865,7 +1069,23 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
finally:
# If no session_id is given we should close the page
if not config.session_id:
all_contexts = page.context.browser.contexts
total_pages = sum(len(context.pages) for context in all_contexts)
if config.session_id:
pass
elif total_pages <= 1 and (self.browser_config.use_managed_browser or self.browser_config.headless):
pass
else:
# Detach listeners before closing to prevent potential errors during close
if config.capture_network_requests:
page.remove_listener("request", handle_request_capture)
page.remove_listener("response", handle_response_capture)
page.remove_listener("requestfailed", handle_request_failed_capture)
if config.capture_console_messages:
page.remove_listener("console", handle_console_capture)
page.remove_listener("pageerror", handle_pageerror_capture)
# Close the page
await page.close()
async def _handle_full_page_scan(self, page: Page, scroll_delay: float = 0.1):
@@ -933,6 +1153,177 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await self.safe_scroll(page, 0, total_height)
async def _handle_virtual_scroll(self, page: Page, config: "VirtualScrollConfig"):
"""
Handle virtual scroll containers (e.g., Twitter-like feeds) by capturing
content at different scroll positions and merging unique elements.
Following the design:
1. Get container HTML
2. Scroll by container height
3. Wait and check if container HTML changed
4. Three cases:
- No change: continue scrolling
- New items added (appended): continue (items already in page)
- Items replaced: capture HTML chunk and add to list
5. After N scrolls, merge chunks if any were captured
Args:
page: The Playwright page object
config: Virtual scroll configuration
"""
try:
# Import VirtualScrollConfig to avoid circular import
from .async_configs import VirtualScrollConfig
# Ensure config is a VirtualScrollConfig instance
if isinstance(config, dict):
config = VirtualScrollConfig.from_dict(config)
self.logger.info(
message="Starting virtual scroll capture for container: {selector}",
tag="VSCROLL",
params={"selector": config.container_selector}
)
# JavaScript function to handle virtual scroll capture
virtual_scroll_js = """
async (config) => {
const container = document.querySelector(config.container_selector);
if (!container) {
throw new Error(`Container not found: ${config.container_selector}`);
}
// List to store HTML chunks when content is replaced
const htmlChunks = [];
let previousHTML = container.innerHTML;
let scrollCount = 0;
// Determine scroll amount
let scrollAmount;
if (typeof config.scroll_by === 'number') {
scrollAmount = config.scroll_by;
} else if (config.scroll_by === 'page_height') {
scrollAmount = window.innerHeight;
} else { // container_height
scrollAmount = container.offsetHeight;
}
// Perform scrolling
while (scrollCount < config.scroll_count) {
// Scroll the container
container.scrollTop += scrollAmount;
// Wait for content to potentially load
await new Promise(resolve => setTimeout(resolve, config.wait_after_scroll * 1000));
// Get current HTML
const currentHTML = container.innerHTML;
// Determine what changed
if (currentHTML === previousHTML) {
// Case 0: No change - continue scrolling
console.log(`Scroll ${scrollCount + 1}: No change in content`);
} else if (currentHTML.startsWith(previousHTML)) {
// Case 1: New items appended - content already in page
console.log(`Scroll ${scrollCount + 1}: New items appended`);
} else {
// Case 2: Items replaced - capture the previous HTML
console.log(`Scroll ${scrollCount + 1}: Content replaced, capturing chunk`);
htmlChunks.push(previousHTML);
}
// Update previous HTML for next iteration
previousHTML = currentHTML;
scrollCount++;
// Check if we've reached the end
if (container.scrollTop + container.clientHeight >= container.scrollHeight - 10) {
console.log(`Reached end of scrollable content at scroll ${scrollCount}`);
// Capture final chunk if content was replaced
if (htmlChunks.length > 0) {
htmlChunks.push(currentHTML);
}
break;
}
}
// If we have chunks (case 2 occurred), merge them
if (htmlChunks.length > 0) {
console.log(`Merging ${htmlChunks.length} HTML chunks`);
// Parse all chunks to extract unique elements
const tempDiv = document.createElement('div');
const seenTexts = new Set();
const uniqueElements = [];
// Process each chunk
for (const chunk of htmlChunks) {
tempDiv.innerHTML = chunk;
const elements = tempDiv.children;
for (let i = 0; i < elements.length; i++) {
const element = elements[i];
// Normalize text for deduplication
const normalizedText = element.innerText
.toLowerCase()
.replace(/[\\s\\W]/g, ''); // Remove spaces and symbols
if (!seenTexts.has(normalizedText)) {
seenTexts.add(normalizedText);
uniqueElements.push(element.outerHTML);
}
}
}
// Replace container content with merged unique elements
container.innerHTML = uniqueElements.join('\\n');
console.log(`Merged ${uniqueElements.length} unique elements from ${htmlChunks.length} chunks`);
return {
success: true,
chunksCount: htmlChunks.length,
uniqueCount: uniqueElements.length,
replaced: true
};
} else {
console.log('No content replacement detected, all content remains in page');
return {
success: true,
chunksCount: 0,
uniqueCount: 0,
replaced: false
};
}
}
"""
# Execute virtual scroll capture
result = await page.evaluate(virtual_scroll_js, config.to_dict())
if result.get("replaced", False):
self.logger.success(
message="Virtual scroll completed. Merged {unique} unique elements from {chunks} chunks",
tag="VSCROLL",
params={
"unique": result.get("uniqueCount", 0),
"chunks": result.get("chunksCount", 0)
}
)
else:
self.logger.info(
message="Virtual scroll completed. Content was appended, no merging needed",
tag="VSCROLL"
)
except Exception as e:
self.logger.error(
message="Virtual scroll capture failed: {error}",
tag="VSCROLL",
params={"error": str(e)}
)
# Continue with normal flow even if virtual scroll fails
async def _handle_download(self, download):
"""
Handle file downloads.
@@ -1028,7 +1419,107 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
"""
pdf_data = await page.pdf(print_background=True)
return pdf_data
async def capture_mhtml(self, page: Page) -> Optional[str]:
"""
Captures the current page as MHTML using CDP.
MHTML (MIME HTML) is a web page archive format that combines the HTML content
with its resources (images, CSS, etc.) into a single MIME-encoded file.
Args:
page (Page): The Playwright page object
Returns:
Optional[str]: The MHTML content as a string, or None if there was an error
"""
try:
# Ensure the page is fully loaded before capturing
try:
# Wait for DOM content and network to be idle
await page.wait_for_load_state("domcontentloaded", timeout=5000)
await page.wait_for_load_state("networkidle", timeout=5000)
# Give a little extra time for JavaScript execution
await page.wait_for_timeout(1000)
# Wait for any animations to complete
await page.evaluate("""
() => new Promise(resolve => {
// First requestAnimationFrame gets scheduled after the next repaint
requestAnimationFrame(() => {
// Second requestAnimationFrame gets called after all animations complete
requestAnimationFrame(resolve);
});
})
""")
except Error as e:
if self.logger:
self.logger.warning(
message="Wait for load state timed out: {error}",
tag="MHTML",
params={"error": str(e)},
)
# Create a new CDP session
cdp_session = await page.context.new_cdp_session(page)
# Call Page.captureSnapshot with format "mhtml"
result = await cdp_session.send("Page.captureSnapshot", {"format": "mhtml"})
# The result contains a 'data' field with the MHTML content
mhtml_content = result.get("data")
# Detach the CDP session to clean up resources
await cdp_session.detach()
return mhtml_content
except Exception as e:
# Log the error but don't raise it - we'll just return None for the MHTML
if self.logger:
self.logger.error(
message="Failed to capture MHTML: {error}",
tag="MHTML",
params={"error": str(e)},
)
return None
async def _capture_console_messages(
self, page: Page, file_path: str
) -> List[Dict[str, Union[str, float]]]:
"""
Captures console messages from the page.
Args:
page (Page): The Playwright page object
Returns:
List[Dict[str, Union[str, float]]]: A list of captured console messages
"""
captured_console = []
def handle_console_message(msg):
try:
message_type = msg.type
message_text = msg.text
entry = {
"type": message_type,
"text": message_text,
"timestamp": time.time(),
}
captured_console.append(entry)
except Exception as e:
if self.logger:
self.logger.warning(
f"Error capturing console message: {e}", tag="CAPTURE"
)
page.on("console", handle_console_message)
await page.goto(file_path)
return captured_console
async def take_screenshot(self, page, **kwargs) -> str:
"""
Take a screenshot of the current page.
@@ -1160,8 +1651,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
buffered = BytesIO()
img.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
finally:
await page.close()
# finally:
# await page.close()
async def take_screenshot_naive(self, page: Page) -> str:
"""
@@ -1194,8 +1685,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
buffered = BytesIO()
img.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
finally:
await page.close()
# finally:
# await page.close()
async def export_storage_state(self, path: str = None) -> dict:
"""
@@ -1685,7 +2176,7 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
await self.start()
yield self._session
finally:
await self.close()
pass
def set_hook(self, hook_type: str, hook_func: Callable) -> None:
if hook_type in self.hooks:
@@ -1801,7 +2292,7 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
encoding = response.charset
if not encoding:
encoding = cchardet.detect(content.tobytes())['encoding'] or 'utf-8'
encoding = chardet.detect(content.tobytes())['encoding'] or 'utf-8'
result = AsyncCrawlResponse(
html=content.tobytes().decode(encoding, errors='replace'),

View File

@@ -4,19 +4,14 @@ import aiosqlite
import asyncio
from typing import Optional, Dict
from contextlib import asynccontextmanager
import logging
import json # Added for serialization/deserialization
from .utils import ensure_content_dirs, generate_content_hash
import json
from .models import CrawlResult, MarkdownGenerationResult, StringCompatibleMarkdown
import aiofiles
from .utils import VersionManager
from .async_logger import AsyncLogger
from .utils import get_error_context, create_box_message
# Set up logging
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)
# logger.setLevel(logging.INFO)
from .utils import ensure_content_dirs, generate_content_hash
from .utils import VersionManager
from .utils import get_error_context, create_box_message
base_directory = DB_PATH = os.path.join(
os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
@@ -176,7 +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
@@ -194,7 +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

@@ -4,17 +4,15 @@ from .models import (
CrawlResult,
CrawlerTaskResult,
CrawlStatus,
DisplayMode,
CrawlStats,
DomainState,
)
from rich.live import Live
from rich.table import Table
from rich.console import Console
from rich import box
from datetime import timedelta
from .components.crawler_monitor import CrawlerMonitor
from .types import AsyncWebCrawler
from collections.abc import AsyncGenerator
import time
import psutil
import asyncio
@@ -24,8 +22,6 @@ from urllib.parse import urlparse
import random
from abc import ABC, abstractmethod
from math import inf as infinity
class RateLimiter:
def __init__(
@@ -87,201 +83,6 @@ class RateLimiter:
return True
class CrawlerMonitor:
def __init__(
self,
max_visible_rows: int = 15,
display_mode: DisplayMode = DisplayMode.DETAILED,
):
self.console = Console()
self.max_visible_rows = max_visible_rows
self.display_mode = display_mode
self.stats: Dict[str, CrawlStats] = {}
self.process = psutil.Process()
self.start_time = time.time()
self.live = Live(self._create_table(), refresh_per_second=2)
def start(self):
self.live.start()
def stop(self):
self.live.stop()
def add_task(self, task_id: str, url: str):
self.stats[task_id] = CrawlStats(
task_id=task_id, url=url, status=CrawlStatus.QUEUED
)
self.live.update(self._create_table())
def update_task(self, task_id: str, **kwargs):
if task_id in self.stats:
for key, value in kwargs.items():
setattr(self.stats[task_id], key, value)
self.live.update(self._create_table())
def _create_aggregated_table(self) -> Table:
"""Creates a compact table showing only aggregated statistics"""
table = Table(
box=box.ROUNDED,
title="Crawler Status Overview",
title_style="bold magenta",
header_style="bold blue",
show_lines=True,
)
# Calculate statistics
total_tasks = len(self.stats)
queued = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.QUEUED
)
in_progress = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
)
completed = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
)
failed = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
)
# Memory statistics
current_memory = self.process.memory_info().rss / (1024 * 1024)
total_task_memory = sum(stat.memory_usage for stat in self.stats.values())
peak_memory = max(
(stat.peak_memory for stat in self.stats.values()), default=0.0
)
# Duration
duration = time.time() - self.start_time
# Create status row
table.add_column("Status", style="bold cyan")
table.add_column("Count", justify="right")
table.add_column("Percentage", justify="right")
table.add_row("Total Tasks", str(total_tasks), "100%")
table.add_row(
"[yellow]In Queue[/yellow]",
str(queued),
f"{(queued / total_tasks * 100):.1f}%" if total_tasks > 0 else "0%",
)
table.add_row(
"[blue]In Progress[/blue]",
str(in_progress),
f"{(in_progress / total_tasks * 100):.1f}%" if total_tasks > 0 else "0%",
)
table.add_row(
"[green]Completed[/green]",
str(completed),
f"{(completed / total_tasks * 100):.1f}%" if total_tasks > 0 else "0%",
)
table.add_row(
"[red]Failed[/red]",
str(failed),
f"{(failed / total_tasks * 100):.1f}%" if total_tasks > 0 else "0%",
)
# Add memory information
table.add_section()
table.add_row(
"[magenta]Current Memory[/magenta]", f"{current_memory:.1f} MB", ""
)
table.add_row(
"[magenta]Total Task Memory[/magenta]", f"{total_task_memory:.1f} MB", ""
)
table.add_row(
"[magenta]Peak Task Memory[/magenta]", f"{peak_memory:.1f} MB", ""
)
table.add_row(
"[yellow]Runtime[/yellow]",
str(timedelta(seconds=int(duration))),
"",
)
return table
def _create_detailed_table(self) -> Table:
table = Table(
box=box.ROUNDED,
title="Crawler Performance Monitor",
title_style="bold magenta",
header_style="bold blue",
)
# Add columns
table.add_column("Task ID", style="cyan", no_wrap=True)
table.add_column("URL", style="cyan", no_wrap=True)
table.add_column("Status", style="bold")
table.add_column("Memory (MB)", justify="right")
table.add_column("Peak (MB)", justify="right")
table.add_column("Duration", justify="right")
table.add_column("Info", style="italic")
# Add summary row
total_memory = sum(stat.memory_usage for stat in self.stats.values())
active_count = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
)
completed_count = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
)
failed_count = sum(
1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
)
table.add_row(
"[bold yellow]SUMMARY",
f"Total: {len(self.stats)}",
f"Active: {active_count}",
f"{total_memory:.1f}",
f"{self.process.memory_info().rss / (1024 * 1024):.1f}",
str(
timedelta(
seconds=int(time.time() - self.start_time)
)
),
f"{completed_count}{failed_count}",
style="bold",
)
table.add_section()
# Add rows for each task
visible_stats = sorted(
self.stats.values(),
key=lambda x: (
x.status != CrawlStatus.IN_PROGRESS,
x.status != CrawlStatus.QUEUED,
x.end_time or infinity,
),
)[: self.max_visible_rows]
for stat in visible_stats:
status_style = {
CrawlStatus.QUEUED: "white",
CrawlStatus.IN_PROGRESS: "yellow",
CrawlStatus.COMPLETED: "green",
CrawlStatus.FAILED: "red",
}[stat.status]
table.add_row(
stat.task_id[:8], # Show first 8 chars of task ID
stat.url[:40] + "..." if len(stat.url) > 40 else stat.url,
f"[{status_style}]{stat.status.value}[/{status_style}]",
f"{stat.memory_usage:.1f}",
f"{stat.peak_memory:.1f}",
stat.duration,
stat.error_message[:40] if stat.error_message else "",
)
return table
def _create_table(self) -> Table:
"""Creates the appropriate table based on display mode"""
if self.display_mode == DisplayMode.AGGREGATED:
return self._create_aggregated_table()
return self._create_detailed_table()
class BaseDispatcher(ABC):
def __init__(
@@ -309,7 +110,7 @@ class BaseDispatcher(ABC):
async def run_urls(
self,
urls: List[str],
crawler: "AsyncWebCrawler", # noqa: F821
crawler: AsyncWebCrawler, # noqa: F821
config: CrawlerRunConfig,
monitor: Optional[CrawlerMonitor] = None,
) -> List[CrawlerTaskResult]:
@@ -320,71 +121,164 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
def __init__(
self,
memory_threshold_percent: float = 90.0,
critical_threshold_percent: float = 95.0, # New critical threshold
recovery_threshold_percent: float = 85.0, # New recovery threshold
check_interval: float = 1.0,
max_session_permit: int = 20,
memory_wait_timeout: float = 300.0, # 5 minutes default timeout
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,
):
super().__init__(rate_limiter, monitor)
self.memory_threshold_percent = memory_threshold_percent
self.critical_threshold_percent = critical_threshold_percent
self.recovery_threshold_percent = recovery_threshold_percent
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() # Queue for storing results
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
# Enter memory pressure mode if we cross the threshold
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:
if self.monitor:
self.monitor.update_memory_status("CRITICAL")
# We could implement additional memory-saving measures here
await asyncio.sleep(self.check_interval)
def _get_priority_score(self, wait_time: float, retry_count: int) -> float:
"""Calculate priority score (lower is higher priority)
- URLs waiting longer than fairness_timeout get higher priority
- More retry attempts decreases priority
"""
if wait_time > self.fairness_timeout:
# High priority for long-waiting URLs
return -wait_time
# Standard priority based on retries
return retry_count
async def crawl_url(
self,
url: str,
config: CrawlerRunConfig,
task_id: str,
retry_count: int = 0,
) -> CrawlerTaskResult:
start_time = time.time()
error_message = ""
memory_usage = peak_memory = 0.0
# Get starting memory for accurate measurement
process = psutil.Process()
start_memory = process.memory_info().rss / (1024 * 1024)
try:
if self.monitor:
self.monitor.update_task(
task_id, status=CrawlStatus.IN_PROGRESS, start_time=start_time
task_id,
status=CrawlStatus.IN_PROGRESS,
start_time=start_time,
retry_count=retry_count
)
self.concurrent_sessions += 1
if self.rate_limiter:
await self.rate_limiter.wait_if_needed(url)
process = psutil.Process()
start_memory = process.memory_info().rss / (1024 * 1024)
# Check if we're in critical memory state
if self.current_memory_percent >= self.critical_threshold_percent:
# Requeue this task with increased priority and retry count
enqueue_time = time.time()
priority = self._get_priority_score(enqueue_time - start_time, retry_count + 1)
await self.task_queue.put((priority, (url, task_id, retry_count + 1, enqueue_time)))
# Update monitoring
if self.monitor:
self.monitor.update_task(
task_id,
status=CrawlStatus.QUEUED,
error_message="Requeued due to critical memory pressure"
)
# Return placeholder result with requeued status
return CrawlerTaskResult(
task_id=task_id,
url=url,
result=CrawlResult(
url=url, html="", metadata={"status": "requeued"},
success=False, error_message="Requeued due to critical memory pressure"
),
memory_usage=0,
peak_memory=0,
start_time=start_time,
end_time=time.time(),
error_message="Requeued due to critical memory pressure",
retry_count=retry_count + 1
)
# Execute the crawl
result = await self.crawler.arun(url, config=config, session_id=task_id)
# Measure memory usage
end_memory = process.memory_info().rss / (1024 * 1024)
memory_usage = peak_memory = end_memory - start_memory
# Handle rate limiting
if self.rate_limiter and result.status_code:
if not self.rate_limiter.update_delay(url, result.status_code):
error_message = f"Rate limit retry count exceeded for domain {urlparse(url).netloc}"
if self.monitor:
self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
result = CrawlerTaskResult(
task_id=task_id,
url=url,
result=result,
memory_usage=memory_usage,
peak_memory=peak_memory,
start_time=start_time,
end_time=time.time(),
error_message=error_message,
)
await self.result_queue.put(result)
return result
# Update status based on result
if not result.success:
error_message = result.error_message
if self.monitor:
self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
elif self.monitor:
self.monitor.update_task(task_id, status=CrawlStatus.COMPLETED)
except Exception as e:
error_message = str(e)
if self.monitor:
@@ -392,7 +286,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
result = CrawlResult(
url=url, html="", metadata={}, success=False, error_message=str(e)
)
finally:
end_time = time.time()
if self.monitor:
@@ -402,9 +296,10 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
memory_usage=memory_usage,
peak_memory=peak_memory,
error_message=error_message,
retry_count=retry_count
)
self.concurrent_sessions -= 1
return CrawlerTaskResult(
task_id=task_id,
url=url,
@@ -414,116 +309,253 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
start_time=start_time,
end_time=end_time,
error_message=error_message,
retry_count=retry_count
)
async def run_urls(
self,
urls: List[str],
crawler: "AsyncWebCrawler", # noqa: F821
crawler: AsyncWebCrawler,
config: CrawlerRunConfig,
) -> List[CrawlerTaskResult]:
self.crawler = crawler
# Start the memory monitor task
memory_monitor = asyncio.create_task(self._memory_monitor_task())
if self.monitor:
self.monitor.start()
results = []
try:
pending_tasks = []
active_tasks = []
task_queue = []
for url in urls:
task_id = str(uuid.uuid4())
if self.monitor:
self.monitor.add_task(task_id, url)
task_queue.append((url, task_id))
while task_queue or active_tasks:
wait_start_time = time.time()
while len(active_tasks) < self.max_session_permit and task_queue:
if psutil.virtual_memory().percent >= self.memory_threshold_percent:
# Check if we've exceeded the timeout
if time.time() - wait_start_time > self.memory_wait_timeout:
raise MemoryError(
f"Memory usage above threshold ({self.memory_threshold_percent}%) for more than {self.memory_wait_timeout} seconds"
)
await asyncio.sleep(self.check_interval)
continue
url, task_id = task_queue.pop(0)
task = asyncio.create_task(self.crawl_url(url, config, task_id))
active_tasks.append(task)
if not active_tasks:
await asyncio.sleep(self.check_interval)
continue
done, pending = await asyncio.wait(
active_tasks, return_when=asyncio.FIRST_COMPLETED
)
pending_tasks.extend(done)
active_tasks = list(pending)
return await asyncio.gather(*pending_tasks)
finally:
if self.monitor:
self.monitor.stop()
async def run_urls_stream(
self,
urls: List[str],
crawler: "AsyncWebCrawler", # noqa: F821
config: CrawlerRunConfig,
) -> AsyncGenerator[CrawlerTaskResult, None]:
self.crawler = crawler
if self.monitor:
self.monitor.start()
try:
active_tasks = []
task_queue = []
completed_count = 0
total_urls = len(urls)
# Initialize task queue
for url in urls:
task_id = str(uuid.uuid4())
if self.monitor:
self.monitor.add_task(task_id, url)
task_queue.append((url, task_id))
# 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())))
while completed_count < total_urls:
# Start new tasks if memory permits
while len(active_tasks) < self.max_session_permit and task_queue:
if psutil.virtual_memory().percent >= self.memory_threshold_percent:
await asyncio.sleep(self.check_interval)
continue
active_tasks = []
url, task_id = task_queue.pop(0)
task = asyncio.create_task(self.crawl_url(url, config, task_id))
active_tasks.append(task)
# 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 not active_tasks and not task_queue:
break
# Wait for any task to complete and yield results
# If memory pressure is low, start new tasks
if not self.memory_pressure_mode and len(active_tasks) < self.max_session_permit:
try:
# Try to get a task with timeout to avoid blocking indefinitely
priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
self.task_queue.get(), timeout=0.1
)
# Create and start the task
task = asyncio.create_task(
self.crawl_url(url, config, task_id, retry_count)
)
active_tasks.append(task)
# Update waiting time in monitor
if self.monitor:
wait_time = time.time() - enqueue_time
self.monitor.update_task(
task_id,
wait_time=wait_time,
status=CrawlStatus.IN_PROGRESS
)
except asyncio.TimeoutError:
# No tasks in queue, that's fine
pass
# Wait for completion even if queue is starved
if active_tasks:
done, pending = await asyncio.wait(
active_tasks, timeout=0.1, return_when=asyncio.FIRST_COMPLETED
)
# Process completed tasks
for completed_task in done:
result = await completed_task
completed_count += 1
yield result
results.append(result)
# Update active tasks list
active_tasks = list(pending)
else:
await asyncio.sleep(self.check_interval)
# If no active tasks but still waiting, sleep briefly
await asyncio.sleep(self.check_interval / 2)
# Update priorities for waiting tasks if needed
await self._update_queue_priorities()
return results
except Exception as e:
if self.monitor:
self.monitor.update_memory_status(f"QUEUE_ERROR: {str(e)}")
finally:
# Clean up
memory_monitor.cancel()
if self.monitor:
self.monitor.stop()
async def _update_queue_priorities(self):
"""Periodically update priorities of items in the queue to prevent starvation"""
# Skip if queue is empty
if self.task_queue.empty():
return
# Use a drain-and-refill approach to update all priorities
temp_items = []
# Drain the queue (with a safety timeout to prevent blocking)
try:
drain_start = time.time()
while not self.task_queue.empty() and time.time() - drain_start < 5.0: # 5 second safety timeout
try:
# Get item from queue with timeout
priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
self.task_queue.get(), timeout=0.1
)
# Calculate new priority based on current wait time
current_time = time.time()
wait_time = current_time - enqueue_time
new_priority = self._get_priority_score(wait_time, retry_count)
# Store with updated priority
temp_items.append((new_priority, (url, task_id, retry_count, enqueue_time)))
# Update monitoring stats for this task
if self.monitor and task_id in self.monitor.stats:
self.monitor.update_task(task_id, wait_time=wait_time)
except asyncio.TimeoutError:
# Queue might be empty or very slow
break
except Exception as e:
# If anything goes wrong, make sure we refill the queue with what we've got
self.monitor.update_memory_status(f"QUEUE_ERROR: {str(e)}")
# Calculate queue statistics
if temp_items and self.monitor:
total_queued = len(temp_items)
wait_times = [item[1][3] for item in temp_items]
highest_wait_time = time.time() - min(wait_times) if wait_times else 0
avg_wait_time = sum(time.time() - t for t in wait_times) / len(wait_times) if wait_times else 0
# Update queue statistics in monitor
self.monitor.update_queue_statistics(
total_queued=total_queued,
highest_wait_time=highest_wait_time,
avg_wait_time=avg_wait_time
)
# Sort by priority (lowest number = highest priority)
temp_items.sort(key=lambda x: x[0])
# Refill the queue with updated priorities
for item in temp_items:
await self.task_queue.put(item)
async def run_urls_stream(
self,
urls: List[str],
crawler: AsyncWebCrawler,
config: CrawlerRunConfig,
) -> AsyncGenerator[CrawlerTaskResult, None]:
self.crawler = crawler
# Start the memory monitor task
memory_monitor = asyncio.create_task(self._memory_monitor_task())
if self.monitor:
self.monitor.start()
try:
# Initialize task queue
for url in urls:
task_id = str(uuid.uuid4())
if self.monitor:
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 = []
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:
# Try to get a task with timeout
priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
self.task_queue.get(), timeout=0.1
)
# Create and start the task
task = asyncio.create_task(
self.crawl_url(url, config, task_id, retry_count)
)
active_tasks.append(task)
# Update waiting time in monitor
if self.monitor:
wait_time = time.time() - enqueue_time
self.monitor.update_task(
task_id,
wait_time=wait_time,
status=CrawlStatus.IN_PROGRESS
)
except asyncio.TimeoutError:
# No tasks in queue, that's fine
pass
# Process completed tasks and yield results
if active_tasks:
done, pending = await asyncio.wait(
active_tasks, timeout=0.1, return_when=asyncio.FIRST_COMPLETED
)
for completed_task in done:
result = await completed_task
# Only count as completed if it wasn't requeued
if "requeued" not in result.error_message:
completed_count += 1
yield result
# Update active tasks list
active_tasks = list(pending)
else:
# If no active tasks but still waiting, sleep briefly
await asyncio.sleep(self.check_interval / 2)
# Update priorities for waiting tasks if needed
await self._update_queue_priorities()
finally:
# Clean up
memory_monitor.cancel()
if self.monitor:
self.monitor.stop()
class SemaphoreDispatcher(BaseDispatcher):
def __init__(
@@ -620,7 +652,7 @@ class SemaphoreDispatcher(BaseDispatcher):
async def run_urls(
self,
crawler: "AsyncWebCrawler", # noqa: F821
crawler: AsyncWebCrawler, # noqa: F821
urls: List[str],
config: CrawlerRunConfig,
) -> List[CrawlerTaskResult]:
@@ -644,4 +676,4 @@ class SemaphoreDispatcher(BaseDispatcher):
return await asyncio.gather(*tasks, return_exceptions=True)
finally:
if self.monitor:
self.monitor.stop()
self.monitor.stop()

View File

@@ -1,18 +1,48 @@
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"
def __str__(self):
"""Automatically convert rich color to string."""
return self.value
class AsyncLoggerBase(ABC):
@@ -37,13 +67,14 @@ 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 +92,21 @@ 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 +115,7 @@ 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 +129,13 @@ 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 +148,23 @@ 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 +172,9 @@ 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,
):
"""
@@ -144,42 +186,44 @@ 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
if colors:
for key, color in colors.items():
# Find the formatted value in the message and wrap it with 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)
@@ -199,6 +243,22 @@ 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."""
@@ -210,7 +270,7 @@ 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.
@@ -222,19 +282,20 @@ 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,
},
)
@@ -250,11 +311,13 @@ 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):
@@ -298,13 +361,13 @@ 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)

1493
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
@@ -10,50 +9,46 @@ import asyncio
# from contextlib import nullcontext, asynccontextmanager
from contextlib import asynccontextmanager
from .models import CrawlResult, MarkdownGenerationResult, DispatchResult, ScrapingResult
from .models import (
CrawlResult,
MarkdownGenerationResult,
DispatchResult,
ScrapingResult,
CrawlResultContainer,
RunManyReturn
)
from .async_database import async_db_manager
from .chunking_strategy import * # noqa: F403
from .chunking_strategy import RegexChunking, ChunkingStrategy, IdentityChunking
from .chunking_strategy import IdentityChunking
from .content_filter_strategy import * # noqa: F403
from .content_filter_strategy import RelevantContentFilter
from .extraction_strategy import * # noqa: F403
from .extraction_strategy import NoExtractionStrategy, ExtractionStrategy
from .extraction_strategy import * # noqa: F403
from .extraction_strategy import NoExtractionStrategy
from .async_crawler_strategy import (
AsyncCrawlerStrategy,
AsyncPlaywrightCrawlerStrategy,
AsyncCrawlResponse,
)
from .cache_context import CacheMode, CacheContext, _legacy_to_cache_mode
from .cache_context import CacheMode, CacheContext
from .markdown_generation_strategy import (
DefaultMarkdownGenerator,
MarkdownGenerationStrategy,
)
from .deep_crawling import DeepCrawlDecorator
from .async_logger import AsyncLogger, AsyncLoggerBase
from .async_configs import BrowserConfig, CrawlerRunConfig
from .async_dispatcher import * # noqa: F403
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 .config import MIN_WORD_THRESHOLD
from .utils import (
sanitize_input_encode,
InvalidCSSSelectorError,
fast_format_html,
create_box_message,
get_error_context,
RobotsParser,
preprocess_html_for_schema,
)
from typing import Union, AsyncGenerator, TypeVar
CrawlResultT = TypeVar('CrawlResultT', bound=CrawlResult)
RunManyReturn = Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
DeepCrawlSingleReturn = Union[List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
DeepCrawlManyReturn = Union[
List[List[CrawlResultT]],
AsyncGenerator[CrawlResultT, None],
]
class AsyncWebCrawler:
"""
@@ -116,7 +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,
@@ -144,7 +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,
@@ -166,23 +163,20 @@ class AsyncWebCrawler:
# Decorate arun method with deep crawling capabilities
self._deep_handler = DeepCrawlDecorator(self)
self.arun = self._deep_handler(self.arun)
self.arun = self._deep_handler(self.arun)
self.url_seeder: Optional[AsyncUrlSeeder] = None
async def start(self):
"""
Start the crawler explicitly without using context manager.
This is equivalent to using 'async with' but gives more control over the lifecycle.
This method will:
1. Initialize the browser and context
2. Perform warmup sequence
3. Return the crawler instance for method chaining
Returns:
AsyncWebCrawler: The initialized crawler instance
"""
await self.crawler_strategy.__aenter__()
await self.awarmup()
self.logger.info(f"Crawl4AI {crawl4ai_version}", tag="INIT")
self.ready = True
return self
async def close(self):
@@ -202,18 +196,6 @@ class AsyncWebCrawler:
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.close()
async def awarmup(self):
"""
Initialize the crawler with warm-up sequence.
This method:
1. Logs initialization info
2. Sets up browser configuration
3. Marks the crawler as ready
"""
self.logger.info(f"Crawl4AI {crawl4ai_version}", tag="INIT")
self.ready = True
@asynccontextmanager
async def nullcontext(self):
"""异步空上下文管理器"""
@@ -223,23 +205,6 @@ class AsyncWebCrawler:
self,
url: str,
config: CrawlerRunConfig = None,
# Legacy parameters maintained for backwards compatibility
# word_count_threshold=MIN_WORD_THRESHOLD,
# extraction_strategy: ExtractionStrategy = None,
# chunking_strategy: ChunkingStrategy = RegexChunking(),
# content_filter: RelevantContentFilter = None,
# cache_mode: Optional[CacheMode] = None,
# Deprecated cache parameters
# bypass_cache: bool = False,
# disable_cache: bool = False,
# no_cache_read: bool = False,
# no_cache_write: bool = False,
# Other legacy parameters
# css_selector: str = None,
# screenshot: bool = False,
# pdf: bool = False,
# user_agent: str = None,
# verbose=True,
**kwargs,
) -> RunManyReturn:
"""
@@ -270,56 +235,25 @@ class AsyncWebCrawler:
Returns:
CrawlResult: The result of crawling and processing
"""
crawler_config = config or CrawlerRunConfig()
# Auto-start if not ready
if not self.ready:
await self.start()
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:
self.logger.verbose = crawler_config.verbose
# Handle configuration
if crawler_config is not None:
config = crawler_config
else:
# Merge all parameters into a single kwargs dict for config creation
# config_kwargs = {
# "word_count_threshold": word_count_threshold,
# "extraction_strategy": extraction_strategy,
# "chunking_strategy": chunking_strategy,
# "content_filter": content_filter,
# "cache_mode": cache_mode,
# "bypass_cache": bypass_cache,
# "disable_cache": disable_cache,
# "no_cache_read": no_cache_read,
# "no_cache_write": no_cache_write,
# "css_selector": css_selector,
# "screenshot": screenshot,
# "pdf": pdf,
# "verbose": verbose,
# **kwargs,
# }
# config = CrawlerRunConfig.from_kwargs(config_kwargs)
pass
# Handle deprecated cache parameters
# if any([bypass_cache, disable_cache, no_cache_read, no_cache_write]):
# # Convert legacy parameters if cache_mode not provided
# if config.cache_mode is None:
# config.cache_mode = _legacy_to_cache_mode(
# disable_cache=disable_cache,
# bypass_cache=bypass_cache,
# no_cache_read=no_cache_read,
# no_cache_write=no_cache_write,
# )
self.logger.verbose = config.verbose
# Default to ENABLED if no cache mode specified
if config.cache_mode is None:
config.cache_mode = CacheMode.ENABLED
# Create cache context
cache_context = CacheContext(
url, config.cache_mode, False
)
cache_context = CacheContext(url, config.cache_mode, False)
# Initialize processing variables
async_response: AsyncCrawlResponse = None
@@ -349,7 +283,7 @@ class AsyncWebCrawler:
# if config.screenshot and not screenshot or config.pdf and not pdf:
if config.screenshot and not screenshot_data:
cached_result = None
if config.pdf and not pdf_data:
cached_result = None
@@ -362,12 +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)
@@ -377,18 +311,23 @@ 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:
if not await self.robots_parser.can_fetch(url, self.browser_config.user_agent):
if not await self.robots_parser.can_fetch(
url, self.browser_config.user_agent
):
return 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"}
response_headers={
"X-Robots-Status": "Blocked by robots.txt"
},
)
##############################
@@ -415,15 +354,16 @@ class AsyncWebCrawler:
###############################################################
# Process the HTML content, Call CrawlerStrategy.process_html #
###############################################################
crawl_result : CrawlResult = await self.aprocess_html(
crawl_result: CrawlResult = await self.aprocess_html(
url=url,
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,
)
@@ -432,49 +372,41 @@ 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
if cache_context.should_write() and not bool(cached_result):
await async_db_manager.acache_url(crawl_result)
return crawl_result
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 cached_result
return CrawlResultContainer(cached_result)
except Exception as e:
error_context = get_error_context(sys.exc_info())
@@ -488,12 +420,14 @@ class AsyncWebCrawler:
self.logger.error_status(
url=url,
error=create_box_message(error_message, type="error"),
error=error_message,
tag="ERROR",
)
return CrawlResult(
url=url, html="", success=False, error_message=error_message
return CrawlResultContainer(
CrawlResult(
url=url, html="", success=False, error_message=error_message
)
)
async def aprocess_html(
@@ -502,7 +436,7 @@ class AsyncWebCrawler:
html: str,
extracted_content: str,
config: CrawlerRunConfig,
screenshot: str,
screenshot_data: str,
pdf_data: str,
verbose: bool,
**kwargs,
@@ -515,7 +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
@@ -534,15 +468,17 @@ class AsyncWebCrawler:
scraping_strategy.logger = self.logger
# Process HTML content
params = {k: v for k, v in config.to_dict().items() if k not in ["url"]}
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(
@@ -558,15 +494,23 @@ 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 #
@@ -575,24 +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 #
@@ -616,6 +601,7 @@ class AsyncWebCrawler:
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)
@@ -623,7 +609,7 @@ 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)
@@ -639,10 +625,6 @@ class AsyncWebCrawler:
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
# Apply HTML formatting if requested
if config.prettiify:
cleaned_html = fast_format_html(cleaned_html)
@@ -651,9 +633,11 @@ 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,
@@ -666,22 +650,22 @@ class AsyncWebCrawler:
async def arun_many(
self,
urls: List[str],
config: Optional[CrawlerRunConfig] = None,
config: Optional[CrawlerRunConfig] = None,
dispatcher: Optional[BaseDispatcher] = None,
# Legacy parameters maintained for backwards compatibility
word_count_threshold=MIN_WORD_THRESHOLD,
extraction_strategy: ExtractionStrategy = None,
chunking_strategy: ChunkingStrategy = RegexChunking(),
content_filter: RelevantContentFilter = None,
cache_mode: Optional[CacheMode] = None,
bypass_cache: bool = False,
css_selector: str = None,
screenshot: bool = False,
pdf: bool = False,
user_agent: str = None,
verbose=True,
**kwargs
) -> RunManyReturn:
# word_count_threshold=MIN_WORD_THRESHOLD,
# extraction_strategy: ExtractionStrategy = None,
# chunking_strategy: ChunkingStrategy = RegexChunking(),
# content_filter: RelevantContentFilter = None,
# cache_mode: Optional[CacheMode] = None,
# bypass_cache: bool = False,
# css_selector: str = None,
# screenshot: bool = False,
# pdf: bool = False,
# user_agent: str = None,
# verbose=True,
**kwargs,
) -> RunManyReturn:
"""
Runs the crawler for multiple URLs concurrently using a configurable dispatcher strategy.
@@ -712,20 +696,21 @@ class AsyncWebCrawler:
):
print(f"Processed {result.url}: {len(result.markdown)} chars")
"""
if config is None:
config = CrawlerRunConfig(
word_count_threshold=word_count_threshold,
extraction_strategy=extraction_strategy,
chunking_strategy=chunking_strategy,
content_filter=content_filter,
cache_mode=cache_mode,
bypass_cache=bypass_cache,
css_selector=css_selector,
screenshot=screenshot,
pdf=pdf,
verbose=verbose,
**kwargs,
)
config = config or CrawlerRunConfig()
# if config is None:
# config = CrawlerRunConfig(
# word_count_threshold=word_count_threshold,
# extraction_strategy=extraction_strategy,
# chunking_strategy=chunking_strategy,
# content_filter=content_filter,
# cache_mode=cache_mode,
# bypass_cache=bypass_cache,
# css_selector=css_selector,
# screenshot=screenshot,
# pdf=pdf,
# verbose=verbose,
# **kwargs,
# )
if dispatcher is None:
dispatcher = MemoryAdaptiveDispatcher(
@@ -736,37 +721,123 @@ class AsyncWebCrawler:
def transform_result(task_result):
return (
setattr(task_result.result, 'dispatch_result',
DispatchResult(
task_id=task_result.task_id,
memory_usage=task_result.memory_usage,
peak_memory=task_result.peak_memory,
start_time=task_result.start_time,
end_time=task_result.end_time,
error_message=task_result.error_message,
)
) or task_result.result
setattr(
task_result.result,
"dispatch_result",
DispatchResult(
task_id=task_result.task_id,
memory_usage=task_result.memory_usage,
peak_memory=task_result.peak_memory,
start_time=task_result.start_time,
end_time=task_result.end_time,
error_message=task_result.error_message,
),
)
or task_result.result
)
stream = config.stream
if stream:
async def result_transformer():
async for task_result in dispatcher.run_urls_stream(crawler=self, urls=urls, config=config):
async for task_result in dispatcher.run_urls_stream(
crawler=self, urls=urls, config=config
):
yield transform_result(task_result)
return result_transformer()
else:
_results = await dispatcher.run_urls(crawler=self, urls=urls, config=config)
return [transform_result(res) for res in _results]
return [transform_result(res) for res in _results]
async def aclear_cache(self):
"""Clear the cache database."""
await async_db_manager.cleanup()
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.
async def aflush_cache(self):
"""Flush the cache database."""
await async_db_manager.aflush_db()
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.
async def aget_cache_size(self):
"""Get the total number of cached items."""
return await async_db_manager.aget_total_count()
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

@@ -5,7 +5,10 @@ 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
@@ -76,6 +79,51 @@ 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 +142,7 @@ 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 +158,19 @@ 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:
"""
@@ -142,20 +193,112 @@ 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:
self.browser_process = subprocess.Popen(
args, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
# Monitor browser process output for errors
asyncio.create_task(self._monitor_browser_process())
# 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 sys.platform == "win32":
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
)
# 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()
await asyncio.sleep(2) # Give browser time to start
return f"http://{self.host}:{self.debugging_port}"
except Exception as e:
await self.cleanup()
raise Exception(f"Failed to start browser: {e}")
async def _initial_startup_check(self):
"""
Perform a quick check to make sure the browser started successfully.
This only runs once at startup rather than continuously monitoring.
"""
if not self.browser_process:
return
# Check that process started without immediate termination
await asyncio.sleep(0.5)
if self.browser_process.poll() is not None:
# Process already terminated
stdout, stderr = b"", b""
try:
stdout, stderr = self.browser_process.communicate(timeout=0.5)
except subprocess.TimeoutExpired:
pass
self.logger.error(
message="Browser process terminated during startup | Code: {code} | STDOUT: {stdout} | STDERR: {stderr}",
tag="ERROR",
params={
"code": self.browser_process.returncode,
"stdout": stdout.decode() if stdout else "",
"stderr": stderr.decode() if stderr else "",
},
)
async def _monitor_browser_process(self):
"""
Monitor the browser process for unexpected termination.
@@ -167,6 +310,7 @@ class ManagedBrowser:
4. If any other error occurs, log the error message.
Note: This method should be called in a separate task to avoid blocking the main event loop.
This is DEPRECATED and should not be used for builtin browsers that need to outlive the Python process.
"""
if self.browser_process:
try:
@@ -230,29 +374,29 @@ 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"""
@@ -261,22 +405,33 @@ class ManagedBrowser:
if self.browser_process:
try:
self.browser_process.terminate()
# Wait for process to end gracefully
for _ in range(10): # 10 attempts, 100ms each
if self.browser_process.poll() is not None:
break
await asyncio.sleep(0.1)
# For builtin browsers that should persist, we should check if it's a detached process
# Only terminate if we have proper control over the process
if not self.browser_process.poll():
# Process is still running
self.browser_process.terminate()
# Wait for process to end gracefully
for _ in range(10): # 10 attempts, 100ms each
if self.browser_process.poll() is not None:
break
await asyncio.sleep(0.1)
# Force kill if still running
if self.browser_process.poll() is None:
self.browser_process.kill()
await asyncio.sleep(0.1) # Brief wait for kill to take effect
# Force kill if still running
if self.browser_process.poll() is None:
if sys.platform == "win32":
# On Windows we might need taskkill for detached processes
try:
subprocess.run(["taskkill", "/F", "/PID", str(self.browser_process.pid)])
except Exception:
self.browser_process.kill()
else:
self.browser_process.kill()
await asyncio.sleep(0.1) # Brief wait for kill to take effect
except Exception as e:
self.logger.error(
message="Error terminating browser: {error}",
tag="ERROR",
tag="ERROR",
params={"error": str(e)},
)
@@ -363,6 +518,56 @@ 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:
@@ -379,7 +584,14 @@ class BrowserManager:
sessions (dict): Dictionary to store session information
session_ttl (int): Session timeout in seconds
"""
_playwright_instance = None
@classmethod
async def get_playwright(cls):
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):
"""
@@ -415,6 +627,7 @@ 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):
@@ -429,32 +642,22 @@ class BrowserManager:
Note: This method should be called in a separate task to avoid blocking the main event loop.
"""
if self.playwright is None:
from playwright.async_api import async_playwright
if self.playwright is not None:
await self.close()
from playwright.async_api import async_playwright
self.playwright = await async_playwright().start()
self.playwright = await async_playwright().start()
if self.config.use_managed_browser:
cdp_url = await self.managed_browser.start()
if self.config.cdp_url or self.config.use_managed_browser:
self.config.use_managed_browser = True
cdp_url = await self.managed_browser.start() if not self.config.cdp_url else self.config.cdp_url
self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
contexts = self.browser.contexts
if contexts:
self.default_context = contexts[0]
else:
self.default_context = await self.create_browser_context()
# self.default_context = await self.browser.new_context(
# viewport={
# "width": self.config.viewport_width,
# "height": self.config.viewport_height,
# },
# storage_state=self.config.storage_state,
# user_agent=self.config.headers.get(
# "User-Agent", self.config.user_agent
# ),
# accept_downloads=self.config.accept_downloads,
# ignore_https_errors=self.config.ignore_https_errors,
# java_script_enabled=self.config.java_script_enabled,
# )
await self.setup_context(self.default_context)
else:
browser_args = self._build_browser_args()
@@ -469,6 +672,7 @@ class BrowserManager:
self.default_context = self.browser
def _build_browser_args(self) -> dict:
"""Build browser launch arguments from config."""
args = [
@@ -512,6 +716,9 @@ 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:
@@ -530,9 +737,9 @@ class BrowserManager:
ProxySettings(server=self.config.proxy)
if self.config.proxy
else ProxySettings(
server=self.config.proxy_config.get("server"),
username=self.config.proxy_config.get("username"),
password=self.config.proxy_config.get("password"),
server=self.config.proxy_config.server,
username=self.config.proxy_config.username,
password=self.config.proxy_config.password,
)
)
browser_args["proxy"] = proxy_settings
@@ -607,7 +814,7 @@ class BrowserManager:
"name": "cookiesEnabled",
"value": "true",
"url": crawlerRunConfig.url
if crawlerRunConfig
if crawlerRunConfig and crawlerRunConfig.url
else "https://crawl4ai.com/",
}
]
@@ -726,6 +933,23 @@ 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)
@@ -758,6 +982,10 @@ 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]
@@ -789,8 +1017,17 @@ class BrowserManager:
# If using a managed browser, just grab the shared default_context
if self.config.use_managed_browser:
context = self.default_context
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)
@@ -840,6 +1077,9 @@ class BrowserManager:
async def close(self):
"""Close all browser resources and clean up."""
if self.config.cdp_url:
return
if self.config.sleep_on_close:
await asyncio.sleep(0.5)

View File

@@ -12,12 +12,15 @@ import sys
import datetime
import uuid
import shutil
import json
import subprocess
import time
from typing import List, Dict, Optional, Any
from colorama import Fore, Style, init
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
@@ -42,8 +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:
@@ -56,6 +59,11 @@ class BrowserProfiler:
# Ensure profiles directory exists
self.profiles_dir = os.path.join(get_home_folder(), "profiles")
os.makedirs(self.profiles_dir, exist_ok=True)
# Builtin browser config file
self.builtin_browser_dir = os.path.join(get_home_folder(), "builtin-browser")
self.builtin_config_file = os.path.join(self.builtin_browser_dir, "browser_config.json")
os.makedirs(self.builtin_browser_dir, exist_ok=True)
async def create_profile(self,
profile_name: Optional[str] = None,
@@ -119,26 +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
@@ -173,7 +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()
@@ -189,7 +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
@@ -206,8 +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
@@ -215,7 +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())
@@ -232,15 +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:
@@ -342,7 +376,11 @@ class BrowserProfiler:
# Check if path exists and is a valid profile
if not os.path.isdir(profile_path):
return None
# Chrck 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 = (
@@ -428,25 +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.print(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":
@@ -457,11 +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
@@ -474,12 +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
@@ -487,17 +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:
@@ -511,12 +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
@@ -524,7 +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)
@@ -541,4 +585,449 @@ class BrowserProfiler:
break
else:
self.logger.error(f"Invalid choice. Please enter a number between 1 and {exit_option}.", tag="MENU")
self.logger.error(f"Invalid choice. Please enter a number between 1 and {exit_option}.", tag="MENU")
async def launch_standalone_browser(self,
browser_type: str = "chromium",
user_data_dir: Optional[str] = None,
debugging_port: int = 9222,
headless: bool = False,
save_as_builtin: bool = False) -> Optional[str]:
"""
Launch a standalone browser with CDP debugging enabled and keep it running
until the user presses 'q'. Returns and displays the CDP URL.
Args:
browser_type (str): Type of browser to launch ('chromium' or 'firefox')
user_data_dir (str, optional): Path to user profile directory
debugging_port (int): Port to use for CDP debugging
headless (bool): Whether to run in headless mode
Returns:
str: CDP URL for the browser, or None if launch failed
Example:
```python
profiler = BrowserProfiler()
cdp_url = await profiler.launch_standalone_browser(
user_data_dir="/path/to/profile",
debugging_port=9222
)
# Use cdp_url to connect to the browser
```
"""
# Use the provided directory if specified, otherwise create a temporary directory
if user_data_dir:
# Directory is provided directly, ensure it exists
profile_path = user_data_dir
os.makedirs(profile_path, exist_ok=True)
else:
# Create a temporary profile directory
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
profile_name = f"temp_{timestamp}_{uuid.uuid4().hex[:6]}"
profile_path = os.path.join(self.profiles_dir, profile_name)
os.makedirs(profile_path, exist_ok=True)
# Print initial information
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 managed browser instance
managed_browser = ManagedBrowser(
browser_type=browser_type,
user_data_dir=profile_path,
headless=headless,
logger=self.logger,
debugging_port=debugging_port
)
# 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="CDP")
await managed_browser.cleanup()
# Restore original signal handlers
signal.signal(signal.SIGINT, original_sigint)
signal.signal(signal.SIGTERM, original_sigterm)
if sig == signal.SIGINT:
self.logger.error("Browser terminated by user.", tag="CDP")
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 wants to exit
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("Press 'q' to stop the browser and exit...", tag="CDP")
# 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("Closing browser...", tag="CDP")
user_done_event.set()
return
# Check if the browser process has already exited
if managed_browser.browser_process and managed_browser.browser_process.poll() is not None:
self.logger.info("Browser already closed. Ending input listener.", tag="CDP")
user_done_event.set()
return
await asyncio.sleep(0.1)
finally:
# Restore terminal settings
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
# Function to retrieve and display CDP JSON config
async def get_cdp_json(port):
import aiohttp
cdp_url = f"http://localhost:{port}"
json_url = f"{cdp_url}/json/version"
try:
async with aiohttp.ClientSession() as session:
# Try multiple times in case the browser is still starting up
for _ in range(10):
try:
async with session.get(json_url) as response:
if response.status == 200:
data = await response.json()
return cdp_url, data
except Exception:
pass
await asyncio.sleep(0.5)
return cdp_url, None
except Exception as e:
self.logger.error(f"Error fetching CDP JSON: {str(e)}", tag="CDP")
return cdp_url, None
cdp_url = None
config_json = None
try:
# Start the browser
await managed_browser.start()
# Check if browser started successfully
browser_process = managed_browser.browser_process
if not browser_process:
self.logger.error("Failed to start browser process.", tag="CDP")
return None
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: {cdp_url}", tag="CDP")
if config_json:
# Display relevant CDP information
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("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:
self.logger.error(f"Failed to get CDP URL on port {debugging_port}", tag="CDP")
await managed_browser.cleanup()
return None
# Start listening for keyboard input
listener_task = asyncio.create_task(listen_for_quit_command())
# Wait for the user to press 'q' or for the browser process to exit naturally
while not user_done_event.is_set() and 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_process.poll() is None and user_done_event.is_set():
self.logger.info("Terminating browser process...", tag="CDP")
await managed_browser.cleanup()
self.logger.success("Browser closed.", tag="CDP")
except Exception as e:
self.logger.error(f"Error launching standalone browser: {str(e)}", tag="CDP")
await managed_browser.cleanup()
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 managed_browser.cleanup()
# Return the CDP URL
return cdp_url
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 builtin browser.
Args:
browser_type (str): Type of browser to launch ('chromium' or 'firefox')
debugging_port (int): Port to use for CDP debugging
headless (bool): 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()
if browser_info and self._is_browser_running(browser_info.get('pid')):
self.logger.info("Builtin browser is already running", tag="BUILTIN")
return browser_info.get('cdp_url')
# Create a user data directory for the builtin browser
user_data_dir = os.path.join(self.builtin_browser_dir, "user_data")
os.makedirs(user_data_dir, exist_ok=True)
# Create managed browser instance
managed_browser = ManagedBrowser(
browser_type=browser_type,
user_data_dir=user_data_dir,
headless=headless,
logger=self.logger,
debugging_port=debugging_port
)
try:
# Start the browser
await managed_browser.start()
# Check if browser started successfully
browser_process = managed_browser.browser_process
if not browser_process:
self.logger.error("Failed to start browser process.", tag="BUILTIN")
return None
# Get CDP URL
cdp_url = f"http://localhost:{debugging_port}"
# Try to verify browser is responsive by fetching version info
import aiohttp
json_url = f"{cdp_url}/json/version"
config_json = None
try:
async with aiohttp.ClientSession() as session:
for _ in range(10): # Try multiple times
try:
async with session.get(json_url) as response:
if response.status == 200:
config_json = await response.json()
break
except Exception:
pass
await asyncio.sleep(0.5)
except Exception as e:
self.logger.warning(f"Could not verify browser: {str(e)}", tag="BUILTIN")
# Save browser info
browser_info = {
'pid': browser_process.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
}
with open(self.builtin_config_file, 'w') as f:
json.dump(browser_info, 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
# We'll just record the PID and other info, and the browser will run independently
managed_browser.browser_process = None
self.logger.success(f"Builtin browser launched at CDP URL: {cdp_url}", tag="BUILTIN")
return cdp_url
except Exception as e:
self.logger.error(f"Error launching builtin browser: {str(e)}", tag="BUILTIN")
if managed_browser:
await managed_browser.cleanup()
return None
def get_builtin_browser_info(self) -> Optional[Dict[str, Any]]:
"""
Get information about the builtin browser.
Returns:
dict: Browser information or None if no builtin browser is configured
"""
if not os.path.exists(self.builtin_config_file):
return None
try:
with open(self.builtin_config_file, 'r') as f:
browser_info = json.load(f)
# Check if the browser is still running
if not self._is_browser_running(browser_info.get('pid')):
self.logger.warning("Builtin browser is not running", tag="BUILTIN")
return None
return browser_info
except Exception as e:
self.logger.error(f"Error reading builtin browser config: {str(e)}", tag="BUILTIN")
return None
def _is_browser_running(self, pid: Optional[int]) -> bool:
"""Check if a process with the given PID is running"""
if not pid:
return False
try:
# Check if the process exists
if sys.platform == "win32":
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
async def kill_builtin_browser(self) -> bool:
"""
Kill the builtin browser if it's running.
Returns:
bool: True if the browser was killed, False otherwise
"""
browser_info = self.get_builtin_browser_info()
if not browser_info:
self.logger.warning("No builtin browser found", tag="BUILTIN")
return False
pid = browser_info.get('pid')
if not pid:
return False
try:
if sys.platform == "win32":
subprocess.run(["taskkill", "/F", "/PID", str(pid)], check=True)
else:
os.kill(pid, signal.SIGTERM)
# Wait for termination
for _ in range(5):
if not self._is_browser_running(pid):
break
await asyncio.sleep(0.5)
else:
# Force kill if still running
os.kill(pid, signal.SIGKILL)
# Remove config file
if os.path.exists(self.builtin_config_file):
os.unlink(self.builtin_config_file)
self.logger.success("Builtin browser terminated", tag="BUILTIN")
return True
except Exception as e:
self.logger.error(f"Error killing builtin browser: {str(e)}", tag="BUILTIN")
return False
async def get_builtin_browser_status(self) -> Dict[str, Any]:
"""
Get status information about the builtin browser.
Returns:
dict: Status information with running, cdp_url, and info fields
"""
browser_info = self.get_builtin_browser_info()
if not browser_info:
return {
'running': False,
'cdp_url': None,
'info': None
}
return {
'running': True,
'cdp_url': browser_info.get('cdp_url'),
'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

@@ -1,9 +1,8 @@
import click
import os
import time
import datetime
import sys
import shutil
import time
import humanize
from typing import Dict, Any, Optional, List
import json
@@ -13,7 +12,6 @@ from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich.prompt import Prompt, Confirm
from rich.style import Style
from crawl4ai import (
CacheMode,
@@ -22,16 +20,22 @@ from crawl4ai import (
BrowserConfig,
CrawlerRunConfig,
LLMExtractionStrategy,
LXMLWebScrapingStrategy,
JsonCssExtractionStrategy,
JsonXPathExtractionStrategy,
BM25ContentFilter,
PruningContentFilter,
BrowserProfiler
BrowserProfiler,
DefaultMarkdownGenerator,
LLMConfig,
BFSDeepCrawlStrategy,
DFSDeepCrawlStrategy,
BestFirstCrawlingStrategy,
)
from crawl4ai.config import USER_SETTINGS
from litellm import completion
from pathlib import Path
from crawl4ai.async_configs import LlmConfig
# Initialize rich console
console = Console()
@@ -177,8 +181,12 @@ def show_examples():
# CSS-based extraction
crwl https://example.com -e extract_css.yml -s css_schema.json -o json
# LLM-based extraction
# LLM-based extraction with config file
crwl https://example.com -e extract_llm.yml -s llm_schema.json -o json
# Quick LLM-based JSON extraction (prompts for LLM provider first time)
crwl https://example.com -j # Auto-extracts structured data
crwl https://example.com -j "Extract product details including name, price, and features" # With specific instructions
3⃣ Direct Parameters:
# Browser settings
@@ -201,7 +209,24 @@ def show_examples():
# 2. Then use that profile to crawl the authenticated site:
crwl https://site-requiring-login.com/dashboard -p my-profile-name
5Sample Config Files:
5CDP Mode for Browser Automation:
# Launch browser with CDP debugging on default port 9222
crwl cdp
# Use a specific profile and custom port
crwl cdp -p my-profile -P 9223
# Launch headless browser with CDP enabled
crwl cdp --headless
# Launch in incognito mode (ignores profile)
crwl cdp --incognito
# Use the CDP URL with other tools (Puppeteer, Playwright, etc.)
# The URL will be displayed in the terminal when the browser starts
6⃣ Sample Config Files:
browser.yml:
headless: true
@@ -259,11 +284,11 @@ llm_schema.json:
}
}
6️⃣ Advanced Usage:
7️⃣ Advanced Usage:
# Combine configs with direct parameters
crwl https://example.com -B browser.yml -b "headless=false,viewport_width=1920"
# Full extraction pipeline
# Full extraction pipeline with config files
crwl https://example.com \\
-B browser.yml \\
-C crawler.yml \\
@@ -271,6 +296,12 @@ llm_schema.json:
-s llm_schema.json \\
-o json \\
-v
# Quick LLM-based extraction with specific instructions
crwl https://amazon.com/dp/B01DFKC2SO \\
-j "Extract product title, current price, original price, rating, and all product specifications" \\
-b "headless=true,viewport_width=1280" \\
-v
# Content filtering with BM25
crwl https://example.com \\
@@ -285,7 +316,7 @@ llm_schema.json:
For more documentation visit: https://github.com/unclecode/crawl4ai
7️⃣ Q&A with LLM:
8️⃣ Q&A with LLM:
# Ask a question about the content
crwl https://example.com -q "What is the main topic discussed?"
@@ -312,8 +343,16 @@ For more documentation visit: https://github.com/unclecode/crawl4ai
- google/gemini-pro
See full list of providers: https://docs.litellm.ai/docs/providers
# Set default LLM provider and token in advance
crwl config set DEFAULT_LLM_PROVIDER "anthropic/claude-3-sonnet"
crwl config set DEFAULT_LLM_PROVIDER_TOKEN "your-api-token-here"
# Set default browser behavior
crwl config set BROWSER_HEADLESS false # Always show browser window
crwl config set USER_AGENT_MODE random # Use random user agent
8️⃣ Profile Management:
9️⃣ Profile Management:
# Launch interactive profile manager
crwl profiles
@@ -326,6 +365,32 @@ For more documentation visit: https://github.com/unclecode/crawl4ai
crwl profiles # Select "Create new profile" option
# 2. Then use that profile to crawl authenticated content:
crwl https://site-requiring-login.com/dashboard -p my-profile-name
🔄 Builtin Browser Management:
# Start a builtin browser (runs in the background)
crwl browser start
# Check builtin browser status
crwl browser status
# Open a visible window to see the browser
crwl browser view --url https://example.com
# Stop the builtin browser
crwl browser stop
# Restart with different options
crwl browser restart --browser-type chromium --port 9223 --no-headless
# Use the builtin browser in your code
# (Just set browser_mode="builtin" in your BrowserConfig)
browser_config = BrowserConfig(
browser_mode="builtin",
headless=True
)
# Usage via CLI:
crwl https://example.com -b "browser_mode=builtin"
"""
click.echo(examples)
@@ -552,28 +617,411 @@ async def manage_profiles():
# Add a separator between operations
console.print("\n")
@click.group(context_settings={"help_option_names": ["-h", "--help"]})
def cli():
"""Crawl4AI CLI - Web content extraction and browser profile management tool"""
pass
@cli.group("browser")
def browser_cmd():
"""Manage browser instances for Crawl4AI
Commands to manage browser instances for Crawl4AI, including:
- status - Check status of the builtin browser
- start - Start a new builtin browser
- stop - Stop the running builtin browser
- restart - Restart the builtin browser
"""
pass
@browser_cmd.command("status")
def browser_status_cmd():
"""Show status of the builtin browser"""
profiler = BrowserProfiler()
try:
status = anyio.run(profiler.get_builtin_browser_status)
if status["running"]:
info = status["info"]
console.print(Panel(
f"[green]Builtin browser is running[/green]\n\n"
f"CDP URL: [cyan]{info['cdp_url']}[/cyan]\n"
f"Process ID: [yellow]{info['pid']}[/yellow]\n"
f"Browser type: [blue]{info['browser_type']}[/blue]\n"
f"User data directory: [magenta]{info['user_data_dir']}[/magenta]\n"
f"Started: [cyan]{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(info['start_time']))}[/cyan]",
title="Builtin Browser Status",
border_style="green"
))
else:
console.print(Panel(
"[yellow]Builtin browser is not running[/yellow]\n\n"
"Use 'crwl browser start' to start a builtin browser",
title="Builtin Browser Status",
border_style="yellow"
))
except Exception as e:
console.print(f"[red]Error checking browser status: {str(e)}[/red]")
sys.exit(1)
@browser_cmd.command("start")
@click.option("--browser-type", "-b", type=click.Choice(["chromium", "firefox"]), default="chromium",
help="Browser type (default: chromium)")
@click.option("--port", "-p", type=int, default=9222, help="Debugging port (default: 9222)")
@click.option("--headless/--no-headless", default=True, help="Run browser in headless mode")
def browser_start_cmd(browser_type: str, port: int, headless: bool):
"""Start a builtin browser instance
This will start a persistent browser instance that can be used by Crawl4AI
by setting browser_mode="builtin" in BrowserConfig.
"""
profiler = BrowserProfiler()
# First check if browser is already running
status = anyio.run(profiler.get_builtin_browser_status)
if status["running"]:
console.print(Panel(
"[yellow]Builtin browser is already running[/yellow]\n\n"
f"CDP URL: [cyan]{status['cdp_url']}[/cyan]\n\n"
"Use 'crwl browser restart' to restart the browser",
title="Builtin Browser Start",
border_style="yellow"
))
return
try:
console.print(Panel(
f"[cyan]Starting builtin browser[/cyan]\n\n"
f"Browser type: [green]{browser_type}[/green]\n"
f"Debugging port: [yellow]{port}[/yellow]\n"
f"Headless: [cyan]{'Yes' if headless else 'No'}[/cyan]",
title="Builtin Browser Start",
border_style="cyan"
))
cdp_url = anyio.run(
profiler.launch_builtin_browser,
browser_type,
port,
headless
)
if cdp_url:
console.print(Panel(
f"[green]Builtin browser started successfully[/green]\n\n"
f"CDP URL: [cyan]{cdp_url}[/cyan]\n\n"
"This browser will be used automatically when setting browser_mode='builtin'",
title="Builtin Browser Start",
border_style="green"
))
else:
console.print(Panel(
"[red]Failed to start builtin browser[/red]",
title="Builtin Browser Start",
border_style="red"
))
sys.exit(1)
except Exception as e:
console.print(f"[red]Error starting builtin browser: {str(e)}[/red]")
sys.exit(1)
@browser_cmd.command("stop")
def browser_stop_cmd():
"""Stop the running builtin browser"""
profiler = BrowserProfiler()
try:
# First check if browser is running
status = anyio.run(profiler.get_builtin_browser_status)
if not status["running"]:
console.print(Panel(
"[yellow]No builtin browser is currently running[/yellow]",
title="Builtin Browser Stop",
border_style="yellow"
))
return
console.print(Panel(
"[cyan]Stopping builtin browser...[/cyan]",
title="Builtin Browser Stop",
border_style="cyan"
))
success = anyio.run(profiler.kill_builtin_browser)
if success:
console.print(Panel(
"[green]Builtin browser stopped successfully[/green]",
title="Builtin Browser Stop",
border_style="green"
))
else:
console.print(Panel(
"[red]Failed to stop builtin browser[/red]",
title="Builtin Browser Stop",
border_style="red"
))
sys.exit(1)
except Exception as e:
console.print(f"[red]Error stopping builtin browser: {str(e)}[/red]")
sys.exit(1)
@browser_cmd.command("view")
@click.option("--url", "-u", help="URL to navigate to (defaults to about:blank)")
def browser_view_cmd(url: Optional[str]):
"""
Open a visible window of the builtin browser
This command connects to the running builtin browser and opens a visible window,
allowing you to see what the browser is currently viewing or navigate to a URL.
"""
profiler = BrowserProfiler()
try:
# First check if browser is running
status = anyio.run(profiler.get_builtin_browser_status)
if not status["running"]:
console.print(Panel(
"[yellow]No builtin browser is currently running[/yellow]\n\n"
"Use 'crwl browser start' to start a builtin browser first",
title="Builtin Browser View",
border_style="yellow"
))
return
info = status["info"]
cdp_url = info["cdp_url"]
console.print(Panel(
f"[cyan]Opening visible window connected to builtin browser[/cyan]\n\n"
f"CDP URL: [green]{cdp_url}[/green]\n"
f"URL to load: [yellow]{url or 'about:blank'}[/yellow]",
title="Builtin Browser View",
border_style="cyan"
))
# Use the CDP URL to launch a new visible window
import subprocess
import os
# Determine the browser command based on platform
if sys.platform == "darwin": # macOS
browser_cmd = ["/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"]
elif sys.platform == "win32": # Windows
browser_cmd = ["C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"]
else: # Linux
browser_cmd = ["google-chrome"]
# Add arguments
browser_args = [
f"--remote-debugging-port={info['debugging_port']}",
"--remote-debugging-address=localhost",
"--no-first-run",
"--no-default-browser-check"
]
# Add URL if provided
if url:
browser_args.append(url)
# Launch browser
try:
subprocess.Popen(browser_cmd + browser_args)
console.print("[green]Browser window opened. Close it when finished viewing.[/green]")
except Exception as e:
console.print(f"[red]Error launching browser: {str(e)}[/red]")
console.print(f"[yellow]Try connecting manually to {cdp_url} in Chrome or using the '--remote-debugging-port' flag.[/yellow]")
except Exception as e:
console.print(f"[red]Error viewing builtin browser: {str(e)}[/red]")
sys.exit(1)
@browser_cmd.command("restart")
@click.option("--browser-type", "-b", type=click.Choice(["chromium", "firefox"]), default=None,
help="Browser type (defaults to same as current)")
@click.option("--port", "-p", type=int, default=None, help="Debugging port (defaults to same as current)")
@click.option("--headless/--no-headless", default=None, help="Run browser in headless mode")
def browser_restart_cmd(browser_type: Optional[str], port: Optional[int], headless: Optional[bool]):
"""Restart the builtin browser
Stops the current builtin browser if running and starts a new one.
By default, uses the same configuration as the current browser.
"""
profiler = BrowserProfiler()
try:
# First check if browser is running and get its config
status = anyio.run(profiler.get_builtin_browser_status)
current_config = {}
if status["running"]:
info = status["info"]
current_config = {
"browser_type": info["browser_type"],
"port": info["debugging_port"],
"headless": True # Default assumption
}
# Stop the browser
console.print(Panel(
"[cyan]Stopping current builtin browser...[/cyan]",
title="Builtin Browser Restart",
border_style="cyan"
))
success = anyio.run(profiler.kill_builtin_browser)
if not success:
console.print(Panel(
"[red]Failed to stop current browser[/red]",
title="Builtin Browser Restart",
border_style="red"
))
sys.exit(1)
# Use provided options or defaults from current config
browser_type = browser_type or current_config.get("browser_type", "chromium")
port = port or current_config.get("port", 9222)
headless = headless if headless is not None else current_config.get("headless", True)
# Start a new browser
console.print(Panel(
f"[cyan]Starting new builtin browser[/cyan]\n\n"
f"Browser type: [green]{browser_type}[/green]\n"
f"Debugging port: [yellow]{port}[/yellow]\n"
f"Headless: [cyan]{'Yes' if headless else 'No'}[/cyan]",
title="Builtin Browser Restart",
border_style="cyan"
))
cdp_url = anyio.run(
profiler.launch_builtin_browser,
browser_type,
port,
headless
)
if cdp_url:
console.print(Panel(
f"[green]Builtin browser restarted successfully[/green]\n\n"
f"CDP URL: [cyan]{cdp_url}[/cyan]",
title="Builtin Browser Restart",
border_style="green"
))
else:
console.print(Panel(
"[red]Failed to restart builtin browser[/red]",
title="Builtin Browser Restart",
border_style="red"
))
sys.exit(1)
except Exception as e:
console.print(f"[red]Error restarting builtin browser: {str(e)}[/red]")
sys.exit(1)
@cli.command("cdp")
@click.option("--user-data-dir", "-d", help="Directory to use for browser data (will be created if it doesn't exist)")
@click.option("--port", "-P", type=int, default=9222, help="Debugging port (default: 9222)")
@click.option("--browser-type", "-b", type=click.Choice(["chromium", "firefox"]), default="chromium",
help="Browser type (default: chromium)")
@click.option("--headless", is_flag=True, help="Run browser in headless mode")
@click.option("--incognito", is_flag=True, help="Run in incognito/private mode (ignores user-data-dir)")
def cdp_cmd(user_data_dir: Optional[str], port: int, browser_type: str, headless: bool, incognito: bool):
"""Launch a standalone browser with CDP debugging enabled
This command launches a browser with Chrome DevTools Protocol (CDP) debugging enabled,
prints the CDP URL, and keeps the browser running until you press 'q'.
The CDP URL can be used for various automation and debugging tasks.
Examples:
# Launch Chromium with CDP on default port 9222
crwl cdp
# Use a specific directory for browser data and custom port
crwl cdp --user-data-dir ~/browser-data --port 9223
# Launch in headless mode
crwl cdp --headless
# Launch in incognito mode (ignores user-data-dir)
crwl cdp --incognito
"""
profiler = BrowserProfiler()
try:
# Handle data directory
data_dir = None
if not incognito and user_data_dir:
# Expand user path (~/something)
expanded_path = os.path.expanduser(user_data_dir)
# Create directory if it doesn't exist
if not os.path.exists(expanded_path):
console.print(f"[yellow]Directory '{expanded_path}' doesn't exist. Creating it.[/yellow]")
os.makedirs(expanded_path, exist_ok=True)
data_dir = expanded_path
# Print launch info
console.print(Panel(
f"[cyan]Launching browser with CDP debugging[/cyan]\n\n"
f"Browser type: [green]{browser_type}[/green]\n"
f"Debugging port: [yellow]{port}[/yellow]\n"
f"User data directory: [cyan]{data_dir or 'Temporary directory'}[/cyan]\n"
f"Headless: [cyan]{'Yes' if headless else 'No'}[/cyan]\n"
f"Incognito: [cyan]{'Yes' if incognito else 'No'}[/cyan]\n\n"
f"[yellow]Press 'q' to quit when done[/yellow]",
title="CDP Browser",
border_style="cyan"
))
# Run the browser
cdp_url = anyio.run(
profiler.launch_standalone_browser,
browser_type,
data_dir,
port,
headless
)
if not cdp_url:
console.print("[red]Failed to launch browser or get CDP URL[/red]")
sys.exit(1)
except Exception as e:
console.print(f"[red]Error launching CDP browser: {str(e)}[/red]")
sys.exit(1)
@cli.command("crawl")
@click.argument("url", required=True)
@click.option("--browser-config", "-B", type=click.Path(exists=True), help="Browser config file (YAML/JSON)")
@click.option("--crawler-config", "-C", type=click.Path(exists=True), help="Crawler config file (YAML/JSON)")
@click.option("--filter-config", "-f", type=click.Path(exists=True), help="Content filter config file")
@click.option("--extraction-config", "-e", type=click.Path(exists=True), help="Extraction strategy config file")
@click.option("--json-extract", "-j", is_flag=False, flag_value="", default=None, help="Extract structured data using LLM with optional description")
@click.option("--schema", "-s", type=click.Path(exists=True), help="JSON schema for extraction")
@click.option("--browser", "-b", type=str, callback=parse_key_values, help="Browser parameters as key1=value1,key2=value2")
@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("--bypass-cache", is_flag=True, default=True, help="Bypass cache when crawling")
@click.option("--output-file", "-O", type=click.Path(), help="Output file path (default: stdout)")
@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, schema: str, browser: Dict, crawler: Dict,
output: str, bypass_cache: bool, question: str, verbose: bool, profile: 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, deep_crawl: str, max_pages: int):
"""Crawl a website and extract content
Simple Usage:
@@ -617,21 +1065,66 @@ def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config:
crawler_cfg = crawler_cfg.clone(**crawler)
# Handle content filter config
if filter_config:
filter_conf = load_config_file(filter_config)
if filter_config or output in ["markdown-fit", "md-fit"]:
if filter_config:
filter_conf = load_config_file(filter_config)
elif not filter_config and output in ["markdown-fit", "md-fit"]:
filter_conf = {
"type": "pruning",
"query": "",
"threshold": 0.48
}
if filter_conf["type"] == "bm25":
crawler_cfg.content_filter = BM25ContentFilter(
user_query=filter_conf.get("query"),
bm25_threshold=filter_conf.get("threshold", 1.0)
crawler_cfg.markdown_generator = DefaultMarkdownGenerator(
content_filter = BM25ContentFilter(
user_query=filter_conf.get("query"),
bm25_threshold=filter_conf.get("threshold", 1.0),
use_stemming=filter_conf.get("use_stemming", True),
)
)
elif filter_conf["type"] == "pruning":
crawler_cfg.content_filter = PruningContentFilter(
user_query=filter_conf.get("query"),
threshold=filter_conf.get("threshold", 0.48)
crawler_cfg.markdown_generator = DefaultMarkdownGenerator(
content_filter = PruningContentFilter(
user_query=filter_conf.get("query"),
threshold=filter_conf.get("threshold", 0.48)
)
)
# Handle json-extract option (takes precedence over extraction-config)
if json_extract is not None:
# Get LLM provider and token
provider, token = setup_llm_config()
# Default sophisticated instruction for structured data extraction
default_instruction = """Analyze the web page content and extract structured data as JSON.
If the page contains a list of items with repeated patterns, extract all items in an array.
If the page is an article or contains unique content, extract a comprehensive JSON object with all relevant information.
Look at the content, intention of content, what it offers and find the data item(s) in the page.
Always return valid, properly formatted JSON."""
default_instruction_with_user_query = """Analyze the web page content and extract structured data as JSON, following the below instruction and explanation of schema and always return valid, properly formatted JSON. \n\nInstruction:\n\n""" + json_extract
# Determine instruction based on whether json_extract is empty or has content
instruction = default_instruction_with_user_query if json_extract else default_instruction
# Create LLM extraction strategy
crawler_cfg.extraction_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider=provider, api_token=token),
instruction=instruction,
schema=load_schema_file(schema), # Will be None if no schema is provided
extraction_type="schema", #if schema else "block",
apply_chunking=False,
force_json_response=True,
verbose=verbose,
)
# Set output to JSON if not explicitly specified
if output == "all":
output = "json"
# Handle extraction strategy
if extraction_config:
# Handle extraction strategy from config file (only if json-extract wasn't used)
elif extraction_config:
extract_conf = load_config_file(extraction_config)
schema_data = load_schema_file(schema)
@@ -647,7 +1140,7 @@ def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config:
raise click.ClickException("LLM provider and API token are required for LLM extraction")
crawler_cfg.extraction_strategy = LLMExtractionStrategy(
llmConfig=LlmConfig(provider=extract_conf["provider"], api_token=extract_conf["api_token"]),
llm_config=LLMConfig(provider=extract_conf["provider"], api_token=extract_conf["api_token"]),
instruction=extract_conf["instruction"],
schema=schema_data,
**extract_conf.get("params", {})
@@ -665,6 +1158,34 @@ def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config:
# No cache
if bypass_cache:
crawler_cfg.cache_mode = CacheMode.BYPASS
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)
crawler_cfg.verbose = config.get("VERBOSE", False)
# Run crawler
result : CrawlResult = anyio.run(
@@ -675,22 +1196,60 @@ def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config:
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 output == "all":
click.echo(json.dumps(result.model_dump(), indent=2))
elif output == "json":
click.echo(json.dumps(json.loads(result.extracted_content), indent=2))
elif output in ["markdown", "md"]:
click.echo(result.markdown.raw_markdown)
elif output in ["markdown-fit", "md-fit"]:
click.echo(result.markdown.fit_markdown)
if not output_file:
if output == "all":
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(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(main_result.markdown.raw_markdown)
elif output in ["markdown-fit", "md-fit"]:
click.echo(main_result.markdown.fit_markdown)
else:
if output == "all":
with open(output_file, "w") as f:
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(main_result.extracted_content)
elif output in ["markdown", "md"]:
with open(output_file, "w") as f:
f.write(main_result.markdown.raw_markdown)
elif output in ["markdown-fit", "md-fit"]:
with open(output_file, "w") as f:
f.write(main_result.markdown.fit_markdown)
except Exception as e:
raise click.ClickException(str(e))
@@ -700,6 +1259,120 @@ def examples_cmd():
"""Show usage examples"""
show_examples()
@cli.group("config")
def config_cmd():
"""Manage global configuration settings
Commands to view and update global configuration settings:
- list: Display all current configuration settings
- get: Get the value of a specific setting
- set: Set the value of a specific setting
"""
pass
@config_cmd.command("list")
def config_list_cmd():
"""List all configuration settings"""
config = get_global_config()
table = Table(title="Crawl4AI Configuration", show_header=True, header_style="bold cyan", border_style="blue")
table.add_column("Setting", style="cyan")
table.add_column("Value", style="green")
table.add_column("Default", style="yellow")
table.add_column("Description", style="white")
for key, setting in USER_SETTINGS.items():
value = config.get(key, setting["default"])
# Handle secret values
display_value = value
if setting.get("secret", False) and value:
display_value = "********"
# Handle boolean values
if setting["type"] == "boolean":
display_value = str(value).lower()
default_value = str(setting["default"]).lower()
else:
default_value = str(setting["default"])
table.add_row(
key,
str(display_value),
default_value,
setting["description"]
)
console.print(table)
@config_cmd.command("get")
@click.argument("key", required=True)
def config_get_cmd(key: str):
"""Get a specific configuration setting"""
config = get_global_config()
# Normalize key to uppercase
key = key.upper()
if key not in USER_SETTINGS:
console.print(f"[red]Error: Unknown setting '{key}'[/red]")
return
value = config.get(key, USER_SETTINGS[key]["default"])
# Handle secret values
display_value = value
if USER_SETTINGS[key].get("secret", False) and value:
display_value = "********"
console.print(f"[cyan]{key}[/cyan] = [green]{display_value}[/green]")
console.print(f"[dim]Description: {USER_SETTINGS[key]['description']}[/dim]")
@config_cmd.command("set")
@click.argument("key", required=True)
@click.argument("value", required=True)
def config_set_cmd(key: str, value: str):
"""Set a configuration setting"""
config = get_global_config()
# Normalize key to uppercase
key = key.upper()
if key not in USER_SETTINGS:
console.print(f"[red]Error: Unknown setting '{key}'[/red]")
console.print(f"[yellow]Available settings: {', '.join(USER_SETTINGS.keys())}[/yellow]")
return
setting = USER_SETTINGS[key]
# Type conversion and validation
if setting["type"] == "boolean":
if value.lower() in ["true", "yes", "1", "y"]:
typed_value = True
elif value.lower() in ["false", "no", "0", "n"]:
typed_value = False
else:
console.print(f"[red]Error: Invalid boolean value. Use 'true' or 'false'.[/red]")
return
elif setting["type"] == "string":
typed_value = value
# Check if the value should be one of the allowed options
if "options" in setting and value not in setting["options"]:
console.print(f"[red]Error: Value must be one of: {', '.join(setting['options'])}[/red]")
return
# Update config
config[key] = typed_value
save_global_config(config)
# Handle secret values for display
display_value = typed_value
if setting.get("secret", False) and typed_value:
display_value = "********"
console.print(f"[green]Successfully set[/green] [cyan]{key}[/cyan] = [green]{display_value}[/green]")
@cli.command("profiles")
def profiles_cmd():
"""Manage browser profiles interactively
@@ -719,6 +1392,7 @@ def profiles_cmd():
@click.option("--crawler-config", "-C", type=click.Path(exists=True), help="Crawler config file (YAML/JSON)")
@click.option("--filter-config", "-f", type=click.Path(exists=True), help="Content filter config file")
@click.option("--extraction-config", "-e", type=click.Path(exists=True), help="Extraction strategy config file")
@click.option("--json-extract", "-j", is_flag=False, flag_value="", default=None, help="Extract structured data using LLM with optional description")
@click.option("--schema", "-s", type=click.Path(exists=True), help="JSON schema for extraction")
@click.option("--browser", "-b", type=str, callback=parse_key_values, help="Browser parameters as key1=value1,key2=value2")
@click.option("--crawler", "-c", type=str, callback=parse_key_values, help="Crawler parameters as key1=value1,key2=value2")
@@ -727,9 +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, schema: str, browser: Dict, crawler: Dict,
output: str, bypass_cache: bool, question: str, verbose: bool, profile: str):
extraction_config: str, json_extract: str, schema: str, browser: Dict, crawler: Dict,
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:
@@ -740,7 +1416,16 @@ def default(url: str, example: bool, browser_config: str, crawler_config: str, f
Other commands:
crwl profiles - Manage browser profiles for identity-based crawling
crwl crawl - Crawl a website with advanced options
crwl cdp - Launch browser with CDP debugging enabled
crwl browser - Manage builtin browser (start, stop, status, restart)
crwl config - Manage global configuration settings
crwl examples - Show more usage examples
Configuration Examples:
crwl config list - List all configuration settings
crwl config get DEFAULT_LLM_PROVIDER - Show current LLM provider
crwl config set VERBOSE true - Enable verbose mode globally
crwl config set BROWSER_HEADLESS false - Default to visible browser
"""
if example:
@@ -761,7 +1446,8 @@ def default(url: str, example: bool, browser_config: str, crawler_config: str, f
browser_config=browser_config,
crawler_config=crawler_config,
filter_config=filter_config,
extraction_config=extraction_config,
extraction_config=extraction_config,
json_extract=json_extract,
schema=schema,
browser=browser,
crawler=crawler,
@@ -769,7 +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

@@ -0,0 +1,837 @@
import time
import uuid
import threading
import psutil
from datetime import datetime, timedelta
from typing import Dict, Optional, List
import threading
from rich.console import Console
from rich.layout import Layout
from rich.panel import Panel
from rich.table import Table
from rich.text import Text
from rich.live import Live
from rich import box
from ..models import CrawlStatus
class TerminalUI:
"""Terminal user interface for CrawlerMonitor using rich library."""
def __init__(self, refresh_rate: float = 1.0, max_width: int = 120):
"""
Initialize the terminal UI.
Args:
refresh_rate: How often to refresh the UI (in seconds)
max_width: Maximum width of the UI in characters
"""
self.console = Console(width=max_width)
self.layout = Layout()
self.refresh_rate = refresh_rate
self.stop_event = threading.Event()
self.ui_thread = None
self.monitor = None # Will be set by CrawlerMonitor
self.max_width = max_width
# Setup layout - vertical layout (top to bottom)
self.layout.split(
Layout(name="header", size=3),
Layout(name="pipeline_status", size=10),
Layout(name="task_details", ratio=1),
Layout(name="footer", size=3) # Increased footer size to fit all content
)
def start(self, monitor):
"""Start the UI thread."""
self.monitor = monitor
self.stop_event.clear()
self.ui_thread = threading.Thread(target=self._ui_loop)
self.ui_thread.daemon = True
self.ui_thread.start()
def stop(self):
"""Stop the UI thread."""
if self.ui_thread and self.ui_thread.is_alive():
self.stop_event.set()
# Only try to join if we're not in the UI thread
# This prevents "cannot join current thread" errors
if threading.current_thread() != self.ui_thread:
self.ui_thread.join(timeout=5.0)
def _ui_loop(self):
"""Main UI rendering loop."""
import sys
import select
import termios
import tty
# Setup terminal for non-blocking input
old_settings = termios.tcgetattr(sys.stdin)
try:
tty.setcbreak(sys.stdin.fileno())
# Use Live display to render the UI
with Live(self.layout, refresh_per_second=1/self.refresh_rate, screen=True) as live:
self.live = live # Store the live display for updates
# Main UI loop
while not self.stop_event.is_set():
self._update_display()
# Check for key press (non-blocking)
if select.select([sys.stdin], [], [], 0)[0]:
key = sys.stdin.read(1)
# Check for 'q' to quit
if key == 'q':
# Signal stop but don't call monitor.stop() from UI thread
# as it would cause the thread to try to join itself
self.stop_event.set()
self.monitor.is_running = False
break
time.sleep(self.refresh_rate)
# Just check if the monitor was stopped
if not self.monitor.is_running:
break
finally:
# Restore terminal settings
termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)
def _update_display(self):
"""Update the terminal display with current statistics."""
if not self.monitor:
return
# Update crawler status panel
self.layout["header"].update(self._create_status_panel())
# Update pipeline status panel and task details panel
self.layout["pipeline_status"].update(self._create_pipeline_panel())
self.layout["task_details"].update(self._create_task_details_panel())
# Update footer
self.layout["footer"].update(self._create_footer())
def _create_status_panel(self) -> Panel:
"""Create the crawler status panel."""
summary = self.monitor.get_summary()
# Format memory status with icon
memory_status = self.monitor.get_memory_status()
memory_icon = "🟢" # Default NORMAL
if memory_status == "PRESSURE":
memory_icon = "🟠"
elif memory_status == "CRITICAL":
memory_icon = "🔴"
# Get current memory usage
current_memory = psutil.Process().memory_info().rss / (1024 * 1024) # MB
memory_percent = (current_memory / psutil.virtual_memory().total) * 100
# Format runtime
runtime = self.monitor._format_time(time.time() - self.monitor.start_time if self.monitor.start_time else 0)
# Create the status text
status_text = Text()
status_text.append(f"Web Crawler Dashboard | Runtime: {runtime} | Memory: {memory_percent:.1f}% {memory_icon}\n")
status_text.append(f"Status: {memory_status} | URLs: {summary['urls_completed']}/{summary['urls_total']} | ")
status_text.append(f"Peak Mem: {summary['peak_memory_percent']:.1f}% at {self.monitor._format_time(summary['peak_memory_time'])}")
return Panel(status_text, title="Crawler Status", border_style="blue")
def _create_pipeline_panel(self) -> Panel:
"""Create the pipeline status panel."""
summary = self.monitor.get_summary()
queue_stats = self.monitor.get_queue_stats()
# Create a table for status counts
table = Table(show_header=True, box=None)
table.add_column("Status", style="cyan")
table.add_column("Count", justify="right")
table.add_column("Percentage", justify="right")
table.add_column("Stat", style="cyan")
table.add_column("Value", justify="right")
# Calculate overall progress
progress = f"{summary['urls_completed']}/{summary['urls_total']}"
progress_percent = f"{summary['completion_percentage']:.1f}%"
# Add rows for each status
table.add_row(
"Overall Progress",
progress,
progress_percent,
"Est. Completion",
summary.get('estimated_completion_time', "N/A")
)
# Add rows for each status
status_counts = summary['status_counts']
total = summary['urls_total'] or 1 # Avoid division by zero
# Status rows
table.add_row(
"Completed",
str(status_counts.get(CrawlStatus.COMPLETED.name, 0)),
f"{status_counts.get(CrawlStatus.COMPLETED.name, 0) / total * 100:.1f}%",
"Avg. Time/URL",
f"{summary.get('avg_task_duration', 0):.2f}s"
)
table.add_row(
"Failed",
str(status_counts.get(CrawlStatus.FAILED.name, 0)),
f"{status_counts.get(CrawlStatus.FAILED.name, 0) / total * 100:.1f}%",
"Concurrent Tasks",
str(status_counts.get(CrawlStatus.IN_PROGRESS.name, 0))
)
table.add_row(
"In Progress",
str(status_counts.get(CrawlStatus.IN_PROGRESS.name, 0)),
f"{status_counts.get(CrawlStatus.IN_PROGRESS.name, 0) / total * 100:.1f}%",
"Queue Size",
str(queue_stats['total_queued'])
)
table.add_row(
"Queued",
str(status_counts.get(CrawlStatus.QUEUED.name, 0)),
f"{status_counts.get(CrawlStatus.QUEUED.name, 0) / total * 100:.1f}%",
"Max Wait Time",
f"{queue_stats['highest_wait_time']:.1f}s"
)
# Requeued is a special case as it's not a status
requeued_count = summary.get('requeued_count', 0)
table.add_row(
"Requeued",
str(requeued_count),
f"{summary.get('requeue_rate', 0):.1f}%",
"Avg Wait Time",
f"{queue_stats['avg_wait_time']:.1f}s"
)
# Add empty row for spacing
table.add_row(
"",
"",
"",
"Requeue Rate",
f"{summary.get('requeue_rate', 0):.1f}%"
)
return Panel(table, title="Pipeline Status", border_style="green")
def _create_task_details_panel(self) -> Panel:
"""Create the task details panel."""
# Create a table for task details
table = Table(show_header=True, expand=True)
table.add_column("Task ID", style="cyan", no_wrap=True, width=10)
table.add_column("URL", style="blue", ratio=3)
table.add_column("Status", style="green", width=15)
table.add_column("Memory", justify="right", width=8)
table.add_column("Peak", justify="right", width=8)
table.add_column("Duration", justify="right", width=10)
# Get all task stats
task_stats = self.monitor.get_all_task_stats()
# Add summary row
active_tasks = sum(1 for stats in task_stats.values()
if stats['status'] == CrawlStatus.IN_PROGRESS.name)
total_memory = sum(stats['memory_usage'] for stats in task_stats.values())
total_peak = sum(stats['peak_memory'] for stats in task_stats.values())
# Summary row with separators
table.add_row(
"SUMMARY",
f"Total: {len(task_stats)}",
f"Active: {active_tasks}",
f"{total_memory:.1f}",
f"{total_peak:.1f}",
"N/A"
)
# Add a separator
table.add_row("" * 10, "" * 20, "" * 10, "" * 8, "" * 8, "" * 10)
# Status icons
status_icons = {
CrawlStatus.QUEUED.name: "",
CrawlStatus.IN_PROGRESS.name: "🔄",
CrawlStatus.COMPLETED.name: "",
CrawlStatus.FAILED.name: ""
}
# Calculate how many rows we can display based on available space
# We can display more rows now that we have a dedicated panel
display_count = min(len(task_stats), 20) # Display up to 20 tasks
# Add rows for each task
for task_id, stats in sorted(
list(task_stats.items())[:display_count],
# Sort: 1. IN_PROGRESS first, 2. QUEUED, 3. COMPLETED/FAILED by recency
key=lambda x: (
0 if x[1]['status'] == CrawlStatus.IN_PROGRESS.name else
1 if x[1]['status'] == CrawlStatus.QUEUED.name else
2,
-1 * (x[1].get('end_time', 0) or 0) # Most recent first
)
):
# Truncate task_id and URL for display
short_id = task_id[:8]
url = stats['url']
if len(url) > 50: # Allow longer URLs in the dedicated panel
url = url[:47] + "..."
# Format status with icon
status = f"{status_icons.get(stats['status'], '?')} {stats['status']}"
# Add row
table.add_row(
short_id,
url,
status,
f"{stats['memory_usage']:.1f}",
f"{stats['peak_memory']:.1f}",
stats['duration'] if 'duration' in stats else "0:00"
)
return Panel(table, title="Task Details", border_style="yellow")
def _create_footer(self) -> Panel:
"""Create the footer panel."""
from rich.columns import Columns
from rich.align import Align
memory_status = self.monitor.get_memory_status()
memory_icon = "🟢" # Default NORMAL
if memory_status == "PRESSURE":
memory_icon = "🟠"
elif memory_status == "CRITICAL":
memory_icon = "🔴"
# Left section - memory status
left_text = Text()
left_text.append("Memory Status: ", style="bold")
status_style = "green" if memory_status == "NORMAL" else "yellow" if memory_status == "PRESSURE" else "red bold"
left_text.append(f"{memory_icon} {memory_status}", style=status_style)
# Center section - copyright
center_text = Text("© Crawl4AI 2025 | Made by UnclecCode", style="cyan italic")
# Right section - quit instruction
right_text = Text()
right_text.append("Press ", style="bold")
right_text.append("q", style="white on blue")
right_text.append(" to quit", style="bold")
# Create columns with the three sections
footer_content = Columns(
[
Align.left(left_text),
Align.center(center_text),
Align.right(right_text)
],
expand=True
)
# Create a more visible footer panel
return Panel(
footer_content,
border_style="white",
padding=(0, 1) # Add padding for better visibility
)
class CrawlerMonitor:
"""
Comprehensive monitoring and visualization system for tracking web crawler operations in real-time.
Provides a terminal-based dashboard that displays task statuses, memory usage, queue statistics,
and performance metrics.
"""
def __init__(
self,
urls_total: int = 0,
refresh_rate: float = 1.0,
enable_ui: bool = True,
max_width: int = 120
):
"""
Initialize the CrawlerMonitor.
Args:
urls_total: Total number of URLs to be crawled
refresh_rate: How often to refresh the UI (in seconds)
enable_ui: Whether to display the terminal UI
max_width: Maximum width of the UI in characters
"""
# Core monitoring attributes
self.stats = {} # Task ID -> stats dict
self.memory_status = "NORMAL"
self.start_time = None
self.end_time = None
self.is_running = False
self.queue_stats = {
"total_queued": 0,
"highest_wait_time": 0.0,
"avg_wait_time": 0.0
}
self.urls_total = urls_total
self.urls_completed = 0
self.peak_memory_percent = 0.0
self.peak_memory_time = 0.0
# Status counts
self.status_counts = {
CrawlStatus.QUEUED.name: 0,
CrawlStatus.IN_PROGRESS.name: 0,
CrawlStatus.COMPLETED.name: 0,
CrawlStatus.FAILED.name: 0
}
# Requeue tracking
self.requeued_count = 0
# Thread-safety
self._lock = threading.RLock()
# Terminal UI
self.enable_ui = enable_ui
self.terminal_ui = TerminalUI(
refresh_rate=refresh_rate,
max_width=max_width
) if enable_ui else None
def start(self):
"""
Start the monitoring session.
- Initializes the start_time
- Sets is_running to True
- Starts the terminal UI if enabled
"""
with self._lock:
self.start_time = time.time()
self.is_running = True
# Start the terminal UI
if self.enable_ui and self.terminal_ui:
self.terminal_ui.start(self)
def stop(self):
"""
Stop the monitoring session.
- Records end_time
- Sets is_running to False
- Stops the terminal UI
- Generates final summary statistics
"""
with self._lock:
self.end_time = time.time()
self.is_running = False
# Stop the terminal UI
if self.enable_ui and self.terminal_ui:
self.terminal_ui.stop()
def add_task(self, task_id: str, url: str):
"""
Register a new task with the monitor.
Args:
task_id: Unique identifier for the task
url: URL being crawled
The task is initialized with:
- status: QUEUED
- url: The URL to crawl
- enqueue_time: Current time
- memory_usage: 0
- peak_memory: 0
- wait_time: 0
- retry_count: 0
"""
with self._lock:
self.stats[task_id] = {
"task_id": task_id,
"url": url,
"status": CrawlStatus.QUEUED.name,
"enqueue_time": time.time(),
"start_time": None,
"end_time": None,
"memory_usage": 0.0,
"peak_memory": 0.0,
"error_message": "",
"wait_time": 0.0,
"retry_count": 0,
"duration": "0:00",
"counted_requeue": False
}
# Update status counts
self.status_counts[CrawlStatus.QUEUED.name] += 1
def update_task(
self,
task_id: str,
status: Optional[CrawlStatus] = None,
start_time: Optional[float] = None,
end_time: Optional[float] = None,
memory_usage: Optional[float] = None,
peak_memory: Optional[float] = None,
error_message: Optional[str] = None,
retry_count: Optional[int] = None,
wait_time: Optional[float] = None
):
"""
Update statistics for a specific task.
Args:
task_id: Unique identifier for the task
status: New status (QUEUED, IN_PROGRESS, COMPLETED, FAILED)
start_time: When task execution started
end_time: When task execution ended
memory_usage: Current memory usage in MB
peak_memory: Maximum memory usage in MB
error_message: Error description if failed
retry_count: Number of retry attempts
wait_time: Time spent in queue
Updates task statistics and updates status counts.
If status changes, decrements old status count and
increments new status count.
"""
with self._lock:
# Check if task exists
if task_id not in self.stats:
return
task_stats = self.stats[task_id]
# Update status counts if status is changing
old_status = task_stats["status"]
if status and status.name != old_status:
self.status_counts[old_status] -= 1
self.status_counts[status.name] += 1
# Track completion
if status == CrawlStatus.COMPLETED:
self.urls_completed += 1
# Track requeues
if old_status in [CrawlStatus.COMPLETED.name, CrawlStatus.FAILED.name] and not task_stats.get("counted_requeue", False):
self.requeued_count += 1
task_stats["counted_requeue"] = True
# Update task statistics
if status:
task_stats["status"] = status.name
if start_time is not None:
task_stats["start_time"] = start_time
if end_time is not None:
task_stats["end_time"] = end_time
if memory_usage is not None:
task_stats["memory_usage"] = memory_usage
# Update peak memory if necessary
current_percent = (memory_usage / psutil.virtual_memory().total) * 100
if current_percent > self.peak_memory_percent:
self.peak_memory_percent = current_percent
self.peak_memory_time = time.time()
if peak_memory is not None:
task_stats["peak_memory"] = peak_memory
if error_message is not None:
task_stats["error_message"] = error_message
if retry_count is not None:
task_stats["retry_count"] = retry_count
if wait_time is not None:
task_stats["wait_time"] = wait_time
# Calculate duration
if task_stats["start_time"]:
end = task_stats["end_time"] or time.time()
duration = end - task_stats["start_time"]
task_stats["duration"] = self._format_time(duration)
def update_memory_status(self, status: str):
"""
Update the current memory status.
Args:
status: Memory status (NORMAL, PRESSURE, CRITICAL, or custom)
Also updates the UI to reflect the new status.
"""
with self._lock:
self.memory_status = status
def update_queue_statistics(
self,
total_queued: int,
highest_wait_time: float,
avg_wait_time: float
):
"""
Update statistics related to the task queue.
Args:
total_queued: Number of tasks currently in queue
highest_wait_time: Longest wait time of any queued task
avg_wait_time: Average wait time across all queued tasks
"""
with self._lock:
self.queue_stats = {
"total_queued": total_queued,
"highest_wait_time": highest_wait_time,
"avg_wait_time": avg_wait_time
}
def get_task_stats(self, task_id: str) -> Dict:
"""
Get statistics for a specific task.
Args:
task_id: Unique identifier for the task
Returns:
Dictionary containing all task statistics
"""
with self._lock:
return self.stats.get(task_id, {}).copy()
def get_all_task_stats(self) -> Dict[str, Dict]:
"""
Get statistics for all tasks.
Returns:
Dictionary mapping task_ids to their statistics
"""
with self._lock:
return self.stats.copy()
def get_memory_status(self) -> str:
"""
Get the current memory status.
Returns:
Current memory status string
"""
with self._lock:
return self.memory_status
def get_queue_stats(self) -> Dict:
"""
Get current queue statistics.
Returns:
Dictionary with queue statistics including:
- total_queued: Number of tasks in queue
- highest_wait_time: Longest wait time
- avg_wait_time: Average wait time
"""
with self._lock:
return self.queue_stats.copy()
def get_summary(self) -> Dict:
"""
Get a summary of all crawler statistics.
Returns:
Dictionary containing:
- runtime: Total runtime in seconds
- urls_total: Total URLs to process
- urls_completed: Number of completed URLs
- completion_percentage: Percentage complete
- status_counts: Count of tasks in each status
- memory_status: Current memory status
- peak_memory_percent: Highest memory usage
- peak_memory_time: When peak memory occurred
- avg_task_duration: Average task processing time
- estimated_completion_time: Projected finish time
- requeue_rate: Percentage of tasks requeued
"""
with self._lock:
# Calculate runtime
current_time = time.time()
runtime = current_time - (self.start_time or current_time)
# Calculate completion percentage
completion_percentage = 0
if self.urls_total > 0:
completion_percentage = (self.urls_completed / self.urls_total) * 100
# Calculate average task duration for completed tasks
completed_tasks = [
task for task in self.stats.values()
if task["status"] == CrawlStatus.COMPLETED.name and task.get("start_time") and task.get("end_time")
]
avg_task_duration = 0
if completed_tasks:
total_duration = sum(task["end_time"] - task["start_time"] for task in completed_tasks)
avg_task_duration = total_duration / len(completed_tasks)
# Calculate requeue rate
requeue_rate = 0
if len(self.stats) > 0:
requeue_rate = (self.requeued_count / len(self.stats)) * 100
# Calculate estimated completion time
estimated_completion_time = "N/A"
if avg_task_duration > 0 and self.urls_total > 0 and self.urls_completed > 0:
remaining_tasks = self.urls_total - self.urls_completed
estimated_seconds = remaining_tasks * avg_task_duration
estimated_completion_time = self._format_time(estimated_seconds)
return {
"runtime": runtime,
"urls_total": self.urls_total,
"urls_completed": self.urls_completed,
"completion_percentage": completion_percentage,
"status_counts": self.status_counts.copy(),
"memory_status": self.memory_status,
"peak_memory_percent": self.peak_memory_percent,
"peak_memory_time": self.peak_memory_time,
"avg_task_duration": avg_task_duration,
"estimated_completion_time": estimated_completion_time,
"requeue_rate": requeue_rate,
"requeued_count": self.requeued_count
}
def render(self):
"""
Render the terminal UI.
This is the main UI rendering loop that:
1. Updates all statistics
2. Formats the display
3. Renders the ASCII interface
4. Handles keyboard input
Note: The actual rendering is handled by the TerminalUI class
which uses the rich library's Live display.
"""
if self.enable_ui and self.terminal_ui:
# Force an update of the UI
if hasattr(self.terminal_ui, '_update_display'):
self.terminal_ui._update_display()
def _format_time(self, seconds: float) -> str:
"""
Format time in hours:minutes:seconds.
Args:
seconds: Time in seconds
Returns:
Formatted time string (e.g., "1:23:45")
"""
delta = timedelta(seconds=int(seconds))
hours, remainder = divmod(delta.seconds, 3600)
minutes, seconds = divmod(remainder, 60)
if hours > 0:
return f"{hours}:{minutes:02}:{seconds:02}"
else:
return f"{minutes}:{seconds:02}"
def _calculate_estimated_completion(self) -> str:
"""
Calculate estimated completion time based on current progress.
Returns:
Formatted time string
"""
summary = self.get_summary()
return summary.get("estimated_completion_time", "N/A")
# Example code for testing
if __name__ == "__main__":
# Initialize the monitor
monitor = CrawlerMonitor(urls_total=100)
# Start monitoring
monitor.start()
try:
# Simulate some tasks
for i in range(20):
task_id = str(uuid.uuid4())
url = f"https://example.com/page{i}"
monitor.add_task(task_id, url)
# Simulate 20% of tasks are already running
if i < 4:
monitor.update_task(
task_id=task_id,
status=CrawlStatus.IN_PROGRESS,
start_time=time.time() - 30, # Started 30 seconds ago
memory_usage=10.5
)
# Simulate 10% of tasks are completed
if i >= 4 and i < 6:
start_time = time.time() - 60
end_time = time.time() - 15
monitor.update_task(
task_id=task_id,
status=CrawlStatus.IN_PROGRESS,
start_time=start_time,
memory_usage=8.2
)
monitor.update_task(
task_id=task_id,
status=CrawlStatus.COMPLETED,
end_time=end_time,
memory_usage=0,
peak_memory=15.7
)
# Simulate 5% of tasks fail
if i >= 6 and i < 7:
start_time = time.time() - 45
end_time = time.time() - 20
monitor.update_task(
task_id=task_id,
status=CrawlStatus.IN_PROGRESS,
start_time=start_time,
memory_usage=12.3
)
monitor.update_task(
task_id=task_id,
status=CrawlStatus.FAILED,
end_time=end_time,
memory_usage=0,
peak_memory=18.2,
error_message="Connection timeout"
)
# Simulate memory pressure
monitor.update_memory_status("PRESSURE")
# Simulate queue statistics
monitor.update_queue_statistics(
total_queued=16, # 20 - 4 (in progress)
highest_wait_time=120.5,
avg_wait_time=60.2
)
# Keep the monitor running for a demonstration
print("Crawler Monitor is running. Press 'q' to exit.")
while monitor.is_running:
time.sleep(0.1)
except KeyboardInterrupt:
print("\nExiting crawler monitor...")
finally:
# Stop the monitor
monitor.stop()
print("Crawler monitor exited successfully.")

View File

@@ -4,7 +4,8 @@ from dotenv import load_dotenv
load_dotenv() # Load environment variables from .env file
# Default provider, ONLY used when the extraction strategy is LLMExtractionStrategy
DEFAULT_PROVIDER = "openai/gpt-4o-mini"
DEFAULT_PROVIDER = "openai/gpt-4o"
DEFAULT_PROVIDER_API_KEY = "OPENAI_API_KEY"
MODEL_REPO_BRANCH = "new-release-0.0.2"
# Provider-model dictionary, ONLY used when the extraction strategy is LLMExtractionStrategy
PROVIDER_MODELS = {
@@ -28,6 +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
@@ -92,3 +101,46 @@ SHOW_DEPRECATION_WARNINGS = True
SCREENSHOT_HEIGHT_TRESHOLD = 10000
PAGE_TIMEOUT = 60000
DOWNLOAD_PAGE_TIMEOUT = 60000
# Global user settings with descriptions and default values
USER_SETTINGS = {
"DEFAULT_LLM_PROVIDER": {
"default": "openai/gpt-4o",
"description": "Default LLM provider in 'company/model' format (e.g., 'openai/gpt-4o', 'anthropic/claude-3-sonnet')",
"type": "string"
},
"DEFAULT_LLM_PROVIDER_TOKEN": {
"default": "",
"description": "API token for the default LLM provider",
"type": "string",
"secret": True
},
"VERBOSE": {
"default": False,
"description": "Enable verbose output for all commands",
"type": "boolean"
},
"BROWSER_HEADLESS": {
"default": True,
"description": "Run browser in headless mode by default",
"type": "boolean"
},
"BROWSER_TYPE": {
"default": "chromium",
"description": "Default browser type (chromium or firefox)",
"type": "string",
"options": ["chromium", "firefox"]
},
"CACHE_MODE": {
"default": "bypass",
"description": "Default cache mode (bypass, use, or refresh)",
"type": "string",
"options": ["bypass", "use", "refresh"]
},
"USER_AGENT_MODE": {
"default": "default",
"description": "Default user agent mode (default, random, or mobile)",
"type": "string",
"options": ["default", "random", "mobile"]
}
}

View File

@@ -1,2 +0,0 @@
from .proxy_config import ProxyConfig
__all__ = ["ProxyConfig"]

View File

@@ -1,113 +0,0 @@
import os
from typing import Dict, List, Optional
class ProxyConfig:
def __init__(
self,
server: str,
username: Optional[str] = None,
password: Optional[str] = None,
ip: Optional[str] = None,
):
"""Configuration class for a single proxy.
Args:
server: Proxy server URL (e.g., "http://127.0.0.1:8080")
username: Optional username for proxy authentication
password: Optional password for proxy authentication
ip: Optional IP address for verification purposes
"""
self.server = server
self.username = username
self.password = password
# Extract IP from server if not explicitly provided
self.ip = ip or self._extract_ip_from_server()
def _extract_ip_from_server(self) -> Optional[str]:
"""Extract IP address from server URL."""
try:
# Simple extraction assuming http://ip:port format
if "://" in self.server:
parts = self.server.split("://")[1].split(":")
return parts[0]
else:
parts = self.server.split(":")
return parts[0]
except Exception:
return None
@staticmethod
def from_string(proxy_str: str) -> "ProxyConfig":
"""Create a ProxyConfig from a string in the format 'ip:port:username:password'."""
parts = proxy_str.split(":")
if len(parts) == 4: # ip:port:username:password
ip, port, username, password = parts
return ProxyConfig(
server=f"http://{ip}:{port}",
username=username,
password=password,
ip=ip
)
elif len(parts) == 2: # ip:port only
ip, port = parts
return ProxyConfig(
server=f"http://{ip}:{port}",
ip=ip
)
else:
raise ValueError(f"Invalid proxy string format: {proxy_str}")
@staticmethod
def from_dict(proxy_dict: Dict) -> "ProxyConfig":
"""Create a ProxyConfig from a dictionary."""
return ProxyConfig(
server=proxy_dict.get("server"),
username=proxy_dict.get("username"),
password=proxy_dict.get("password"),
ip=proxy_dict.get("ip")
)
@staticmethod
def from_env(env_var: str = "PROXIES") -> List["ProxyConfig"]:
"""Load proxies from environment variable.
Args:
env_var: Name of environment variable containing comma-separated proxy strings
Returns:
List of ProxyConfig objects
"""
proxies = []
try:
proxy_list = os.getenv(env_var, "").split(",")
for proxy in proxy_list:
if not proxy:
continue
proxies.append(ProxyConfig.from_string(proxy))
except Exception as e:
print(f"Error loading proxies from environment: {e}")
return proxies
def to_dict(self) -> Dict:
"""Convert to dictionary representation."""
return {
"server": self.server,
"username": self.username,
"password": self.password,
"ip": self.ip
}
def clone(self, **kwargs) -> "ProxyConfig":
"""Create a copy of this configuration with updated values.
Args:
**kwargs: Key-value pairs of configuration options to update
Returns:
ProxyConfig: A new instance with the specified updates
"""
config_dict = self.to_dict()
config_dict.update(kwargs)
return ProxyConfig.from_dict(config_dict)

View File

@@ -16,19 +16,18 @@ from .utils import (
extract_xml_data,
merge_chunks,
)
from .types import LLMConfig
from .config import DEFAULT_PROVIDER, OVERLAP_RATE, WORD_TOKEN_RATE
from abc import ABC, abstractmethod
import math
from snowballstemmer import stemmer
from .config import DEFAULT_PROVIDER, OVERLAP_RATE, WORD_TOKEN_RATE, PROVIDER_MODELS
from .models import TokenUsage
from .prompts import PROMPT_FILTER_CONTENT
import os
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]
@@ -770,37 +778,56 @@ class PruningContentFilter(RelevantContentFilter):
class LLMContentFilter(RelevantContentFilter):
"""Content filtering using LLMs to generate relevant markdown."""
"""Content filtering using LLMs to generate relevant markdown.
How it works:
1. Extracts page metadata with fallbacks.
2. Extracts text chunks from the body element.
3. Applies LLMs to generate markdown for each chunk.
4. Filters out chunks below the threshold.
5. Sorts chunks by score in descending order.
6. Returns the top N chunks.
Attributes:
llm_config (LLMConfig): LLM configuration object.
instruction (str): Instruction for LLM markdown generation
chunk_token_threshold (int): Chunk token threshold for splitting (default: 1e9).
overlap_rate (float): Overlap rate for chunking (default: 0.5).
word_token_rate (float): Word token rate for chunking (default: 0.2).
verbose (bool): Enable verbose logging (default: False).
logger (AsyncLogger): Custom logger for LLM operations (optional).
"""
_UNWANTED_PROPS = {
'provider' : 'Instead, use llmConfig=LlmConfig(provider="...")',
'api_token' : 'Instead, use llmConfig=LlMConfig(api_token="...")',
'base_url' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
'api_base' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
'provider' : 'Instead, use llm_config=LLMConfig(provider="...")',
'api_token' : 'Instead, use llm_config=LlMConfig(api_token="...")',
'base_url' : 'Instead, use llm_config=LLMConfig(base_url="...")',
'api_base' : 'Instead, use llm_config=LLMConfig(base_url="...")',
}
def __init__(
self,
provider: str = DEFAULT_PROVIDER,
api_token: Optional[str] = None,
llmConfig: "LlmConfig" = None,
llm_config: "LLMConfig" = None,
instruction: str = None,
chunk_token_threshold: int = int(1e9),
overlap_rate: float = OVERLAP_RATE,
word_token_rate: float = WORD_TOKEN_RATE,
base_url: Optional[str] = None,
api_base: Optional[str] = None,
extra_args: Dict = None,
# char_token_rate: float = WORD_TOKEN_RATE * 5,
# chunk_mode: str = "char",
verbose: bool = False,
logger: Optional[AsyncLogger] = None,
ignore_cache: bool = True,
# Deprecated properties
provider: str = DEFAULT_PROVIDER,
api_token: Optional[str] = None,
base_url: Optional[str] = None,
api_base: Optional[str] = None,
extra_args: Dict = None,
):
super().__init__(None)
self.provider = provider
self.api_token = api_token
self.base_url = base_url or api_base
self.llmConfig = llmConfig
self.llm_config = llm_config
self.instruction = instruction
self.chunk_token_threshold = chunk_token_threshold
self.overlap_rate = overlap_rate
@@ -827,8 +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:
@@ -872,8 +898,8 @@ class LLMContentFilter(RelevantContentFilter):
self.logger.info(
"Starting LLM markdown content filtering process",
tag="LLM",
params={"provider": self.llmConfig.provider},
colors={"provider": Fore.CYAN},
params={"provider": self.llm_config.provider},
colors={"provider": LogColor.CYAN},
)
# Cache handling
@@ -910,7 +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()
@@ -959,10 +985,10 @@ class LLMContentFilter(RelevantContentFilter):
future = executor.submit(
_proceed_with_chunk,
self.llmConfig.provider,
self.llm_config.provider,
prompt,
self.llmConfig.api_token,
self.llmConfig.base_url,
self.llm_config.api_token,
self.llm_config.base_url,
self.extra_args,
)
futures.append((i, future))
@@ -1019,7 +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 []

View File

@@ -23,11 +23,14 @@ from .utils import (
is_external_url,
get_base_domain,
extract_metadata_using_lxml,
extract_page_context,
calculate_link_intrinsic_score,
)
from lxml import etree
from lxml import html as lhtml
from typing import List
from .models import ScrapingResult, MediaItem, Link, Media, Links
import copy
# Pre-compile regular expressions for Open Graph and Twitter metadata
OG_REGEX = re.compile(r"^og:")
@@ -48,7 +51,7 @@ def parse_srcset(s: str) -> List[Dict]:
if len(parts) >= 1:
url = parts[0]
width = (
parts[1].rstrip("w")
parts[1].rstrip("w").split('.')[0]
if len(parts) > 1 and parts[1].endswith("w")
else None
)
@@ -128,7 +131,8 @@ class WebScrapingStrategy(ContentScrapingStrategy):
Returns:
ScrapingResult: A structured result containing the scraped content.
"""
raw_result = self._scrap(url, html, is_async=False, **kwargs)
actual_url = kwargs.get("redirected_url", url)
raw_result = self._scrap(actual_url, html, is_async=False, **kwargs)
if raw_result is None:
return ScrapingResult(
cleaned_html="",
@@ -155,6 +159,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
for aud in raw_result.get("media", {}).get("audios", [])
if aud
],
tables=raw_result.get("media", {}).get("tables", [])
)
# Convert links
@@ -193,6 +198,153 @@ class WebScrapingStrategy(ContentScrapingStrategy):
"""
return await asyncio.to_thread(self._scrap, url, html, **kwargs)
def is_data_table(self, table: Tag, **kwargs) -> bool:
"""
Determine if a table element is a data table (not a layout table).
Args:
table (Tag): BeautifulSoup Tag representing a table element
**kwargs: Additional keyword arguments including table_score_threshold
Returns:
bool: True if the table is a data table, False otherwise
"""
score = 0
# Check for thead and tbody
has_thead = len(table.select('thead')) > 0
has_tbody = len(table.select('tbody')) > 0
if has_thead:
score += 2
if has_tbody:
score += 1
# Check for th elements
th_count = len(table.select('th'))
if th_count > 0:
score += 2
if has_thead or len(table.select('tr:first-child th')) > 0:
score += 1
# Check for nested tables
if len(table.select('table')) > 0:
score -= 3
# Role attribute check
role = table.get('role', '').lower()
if role in {'presentation', 'none'}:
score -= 3
# Column consistency
rows = table.select('tr')
if not rows:
return False
col_counts = [len(row.select('td, th')) for row in rows]
avg_cols = sum(col_counts) / len(col_counts)
variance = sum((c - avg_cols)**2 for c in col_counts) / len(col_counts)
if variance < 1:
score += 2
# Caption and summary
if table.select('caption'):
score += 2
if table.has_attr('summary') and table['summary']:
score += 1
# Text density
total_text = sum(len(cell.get_text().strip()) for row in rows for cell in row.select('td, th'))
total_tags = sum(1 for _ in table.descendants if isinstance(_, Tag))
text_ratio = total_text / (total_tags + 1e-5)
if text_ratio > 20:
score += 3
elif text_ratio > 10:
score += 2
# Data attributes
data_attrs = sum(1 for attr in table.attrs if attr.startswith('data-'))
score += data_attrs * 0.5
# Size check
if avg_cols >= 2 and len(rows) >= 2:
score += 2
threshold = kwargs.get('table_score_threshold', 7)
return score >= threshold
def extract_table_data(self, table: Tag) -> dict:
"""
Extract structured data from a table element.
Args:
table (Tag): BeautifulSoup Tag representing a table element
Returns:
dict: Dictionary containing table data (headers, rows, caption, summary)
"""
caption_elem = table.select_one('caption')
caption = caption_elem.get_text().strip() if caption_elem else ""
summary = table.get('summary', '').strip()
# Extract headers with colspan handling
headers = []
thead_rows = table.select('thead tr')
if thead_rows:
header_cells = thead_rows[0].select('th')
for cell in header_cells:
text = cell.get_text().strip()
colspan = int(cell.get('colspan', 1))
headers.extend([text] * colspan)
else:
first_row = table.select('tr:first-child')
if first_row:
for cell in first_row[0].select('th, td'):
text = cell.get_text().strip()
colspan = int(cell.get('colspan', 1))
headers.extend([text] * colspan)
# Extract rows with colspan handling
rows = []
all_rows = table.select('tr')
thead = table.select_one('thead')
tbody_rows = []
if thead:
thead_rows = thead.select('tr')
tbody_rows = [row for row in all_rows if row not in thead_rows]
else:
if all_rows and all_rows[0].select('th'):
tbody_rows = all_rows[1:]
else:
tbody_rows = all_rows
for row in tbody_rows:
# for row in table.select('tr:not(:has(ancestor::thead))'):
row_data = []
for cell in row.select('td'):
text = cell.get_text().strip()
colspan = int(cell.get('colspan', 1))
row_data.extend([text] * colspan)
if row_data:
rows.append(row_data)
# Align rows with headers
max_columns = len(headers) if headers else (max(len(row) for row in rows) if rows else 0)
aligned_rows = []
for row in rows:
aligned = row[:max_columns] + [''] * (max_columns - len(row))
aligned_rows.append(aligned)
if not headers:
headers = [f"Column {i+1}" for i in range(max_columns)]
return {
"headers": headers,
"rows": aligned_rows,
"caption": caption,
"summary": summary,
}
def flatten_nested_elements(self, node):
"""
Flatten nested elements in a HTML tree.
@@ -431,7 +583,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
Returns:
dict: A dictionary containing the processed element information.
"""
media = {"images": [], "videos": [], "audios": []}
media = {"images": [], "videos": [], "audios": [], "tables": []}
internal_links_dict = {}
external_links_dict = {}
self._process_element(
@@ -471,6 +623,9 @@ class WebScrapingStrategy(ContentScrapingStrategy):
return False
keep_element = False
# Special case for table elements - always preserve structure
if element.name in ["tr", "td", "th"]:
keep_element = True
exclude_domains = kwargs.get("exclude_domains", [])
# exclude_social_media_domains = kwargs.get('exclude_social_media_domains', set(SOCIAL_MEDIA_DOMAINS))
@@ -688,6 +843,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
html: str,
word_count_threshold: int = MIN_WORD_THRESHOLD,
css_selector: str = None,
target_elements: List[str] = None,
**kwargs,
) -> Dict[str, Any]:
"""
@@ -710,7 +866,15 @@ class WebScrapingStrategy(ContentScrapingStrategy):
parser_type = kwargs.get("parser", "lxml")
soup = BeautifulSoup(html, parser_type)
body = soup.body
if body is None:
raise Exception("'<body>' tag is not found in fetched html. Consider adding wait_for=\"css:body\" to wait for body tag to be loaded into DOM.")
base_domain = get_base_domain(url)
# Early removal of all images if exclude_all_images is set
# This happens before any processing to minimize memory usage
if kwargs.get("exclude_all_images", False):
for img in body.find_all('img'):
img.decompose()
try:
meta = extract_metadata("", soup)
@@ -742,22 +906,20 @@ class WebScrapingStrategy(ContentScrapingStrategy):
for element in body.select(excluded_selector):
element.extract()
if css_selector:
selected_elements = body.select(css_selector)
if not selected_elements:
return {
"markdown": "",
"cleaned_html": "",
"success": True,
"media": {"images": [], "videos": [], "audios": []},
"links": {"internal": [], "external": []},
"metadata": {},
"message": f"No elements found for CSS selector: {css_selector}",
}
# raise InvalidCSSSelectorError(f"Invalid CSS selector, No elements found for CSS selector: {css_selector}")
body = soup.new_tag("div")
for el in selected_elements:
body.append(el)
content_element = None
if target_elements:
try:
for_content_targeted_element = []
for target_element in target_elements:
for_content_targeted_element.extend(body.select(target_element))
content_element = soup.new_tag("div")
for el in for_content_targeted_element:
content_element.append(copy.deepcopy(el))
except Exception as e:
self._log("error", f"Error with target element detection: {str(e)}", "SCRAPE")
return None
else:
content_element = body
kwargs["exclude_social_media_domains"] = set(
kwargs.get("exclude_social_media_domains", []) + SOCIAL_MEDIA_DOMAINS
@@ -784,6 +946,72 @@ class WebScrapingStrategy(ContentScrapingStrategy):
# Update the links dictionary with unique links
links["internal"] = list(internal_links_dict.values())
links["external"] = list(external_links_dict.values())
# Extract head content for links if configured
link_preview_config = kwargs.get("link_preview_config")
if link_preview_config is not None:
try:
import asyncio
from .link_preview import LinkPreview
from .models import Links, Link
verbose = link_preview_config.verbose
if verbose:
self._log("info", "Starting link head extraction for {internal} internal and {external} external links",
params={"internal": len(links["internal"]), "external": len(links["external"])}, tag="LINK_EXTRACT")
# Convert dict links to Link objects
internal_links = [Link(**link_data) for link_data in links["internal"]]
external_links = [Link(**link_data) for link_data in links["external"]]
links_obj = Links(internal=internal_links, external=external_links)
# Create a config object for LinkPreview
class TempCrawlerRunConfig:
def __init__(self, link_config, score_links):
self.link_preview_config = link_config
self.score_links = score_links
config = TempCrawlerRunConfig(link_preview_config, kwargs.get("score_links", False))
# Extract head content (run async operation in sync context)
async def extract_links():
async with LinkPreview(self.logger) as extractor:
return await extractor.extract_link_heads(links_obj, config)
# Run the async operation
try:
# Check if we're already in an async context
loop = asyncio.get_running_loop()
# If we're in an async context, we need to run in a thread
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(asyncio.run, extract_links())
updated_links = future.result()
except RuntimeError:
# No running loop, we can use asyncio.run directly
updated_links = asyncio.run(extract_links())
# Convert back to dict format
links["internal"] = [link.dict() for link in updated_links.internal]
links["external"] = [link.dict() for link in updated_links.external]
if verbose:
successful_internal = len([l for l in updated_links.internal if l.head_extraction_status == "valid"])
successful_external = len([l for l in updated_links.external if l.head_extraction_status == "valid"])
self._log("info", "Link head extraction completed: {internal_success}/{internal_total} internal, {external_success}/{external_total} external",
params={
"internal_success": successful_internal,
"internal_total": len(updated_links.internal),
"external_success": successful_external,
"external_total": len(updated_links.external)
}, tag="LINK_EXTRACT")
else:
self._log("info", "Link head extraction completed successfully", tag="LINK_EXTRACT")
except Exception as e:
self._log("error", f"Link head extraction failed: {str(e)}", tag="LINK_EXTRACT")
# Continue with original links if extraction fails
# # Process images using ThreadPoolExecutor
imgs = body.find_all("img")
@@ -797,6 +1025,15 @@ class WebScrapingStrategy(ContentScrapingStrategy):
if result is not None
for img in result
]
# Process tables if not excluded
excluded_tags = set(kwargs.get("excluded_tags", []) or [])
if 'table' not in excluded_tags:
tables = body.find_all('table')
for table in tables:
if self.is_data_table(table, **kwargs):
table_data = self.extract_table_data(table)
media["tables"].append(table_data)
body = self.flatten_nested_elements(body)
base64_pattern = re.compile(r'data:image/[^;]+;base64,([^"]+)')
@@ -808,7 +1045,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
str_body = ""
try:
str_body = body.encode_contents().decode("utf-8")
str_body = content_element.encode_contents().decode("utf-8")
except Exception:
# Reset body to the original HTML
success = False
@@ -847,7 +1084,6 @@ class WebScrapingStrategy(ContentScrapingStrategy):
cleaned_html = str_body.replace("\n\n", "\n").replace(" ", " ")
return {
# **markdown_content,
"cleaned_html": cleaned_html,
"success": success,
"media": media,
@@ -869,6 +1105,7 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
media: Dict[str, List],
internal_links_dict: Dict[str, Any],
external_links_dict: Dict[str, Any],
page_context: dict = None,
**kwargs,
) -> bool:
base_domain = kwargs.get("base_domain", get_base_domain(url))
@@ -888,6 +1125,25 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
"title": link.get("title", "").strip(),
"base_domain": base_domain,
}
# Add intrinsic scoring if enabled
if kwargs.get("score_links", False) and page_context is not None:
try:
intrinsic_score = calculate_link_intrinsic_score(
link_text=link_data["text"],
url=normalized_href,
title_attr=link_data["title"],
class_attr=link.get("class", ""),
rel_attr=link.get("rel", ""),
page_context=page_context
)
link_data["intrinsic_score"] = intrinsic_score
except Exception:
# Fail gracefully - assign default score
link_data["intrinsic_score"] = float('inf')
else:
# No scoring enabled - assign infinity (all links equal priority)
link_data["intrinsic_score"] = float('inf')
is_external = is_external_url(normalized_href, base_domain)
if is_external:
@@ -1130,6 +1386,9 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
"source",
"track",
"wbr",
"tr",
"td",
"th",
}
for el in reversed(list(root.iterdescendants())):
@@ -1187,12 +1446,125 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
return root
def is_data_table(self, table: etree.Element, **kwargs) -> bool:
score = 0
# Check for thead and tbody
has_thead = len(table.xpath(".//thead")) > 0
has_tbody = len(table.xpath(".//tbody")) > 0
if has_thead:
score += 2
if has_tbody:
score += 1
# Check for th elements
th_count = len(table.xpath(".//th"))
if th_count > 0:
score += 2
if has_thead or table.xpath(".//tr[1]/th"):
score += 1
# Check for nested tables
if len(table.xpath(".//table")) > 0:
score -= 3
# Role attribute check
role = table.get("role", "").lower()
if role in {"presentation", "none"}:
score -= 3
# Column consistency
rows = table.xpath(".//tr")
if not rows:
return False
col_counts = [len(row.xpath(".//td|.//th")) for row in rows]
avg_cols = sum(col_counts) / len(col_counts)
variance = sum((c - avg_cols)**2 for c in col_counts) / len(col_counts)
if variance < 1:
score += 2
# Caption and summary
if table.xpath(".//caption"):
score += 2
if table.get("summary"):
score += 1
# Text density
total_text = sum(len(''.join(cell.itertext()).strip()) for row in rows for cell in row.xpath(".//td|.//th"))
total_tags = sum(1 for _ in table.iterdescendants())
text_ratio = total_text / (total_tags + 1e-5)
if text_ratio > 20:
score += 3
elif text_ratio > 10:
score += 2
# Data attributes
data_attrs = sum(1 for attr in table.attrib if attr.startswith('data-'))
score += data_attrs * 0.5
# Size check
if avg_cols >= 2 and len(rows) >= 2:
score += 2
threshold = kwargs.get("table_score_threshold", 7)
return score >= threshold
def extract_table_data(self, table: etree.Element) -> dict:
caption = table.xpath(".//caption/text()")
caption = caption[0].strip() if caption else ""
summary = table.get("summary", "").strip()
# Extract headers with colspan handling
headers = []
thead_rows = table.xpath(".//thead/tr")
if thead_rows:
header_cells = thead_rows[0].xpath(".//th")
for cell in header_cells:
text = cell.text_content().strip()
colspan = int(cell.get("colspan", 1))
headers.extend([text] * colspan)
else:
first_row = table.xpath(".//tr[1]")
if first_row:
for cell in first_row[0].xpath(".//th|.//td"):
text = cell.text_content().strip()
colspan = int(cell.get("colspan", 1))
headers.extend([text] * colspan)
# Extract rows with colspan handling
rows = []
for row in table.xpath(".//tr[not(ancestor::thead)]"):
row_data = []
for cell in row.xpath(".//td"):
text = cell.text_content().strip()
colspan = int(cell.get("colspan", 1))
row_data.extend([text] * colspan)
if row_data:
rows.append(row_data)
# Align rows with headers
max_columns = len(headers) if headers else (max(len(row) for row in rows) if rows else 0)
aligned_rows = []
for row in rows:
aligned = row[:max_columns] + [''] * (max_columns - len(row))
aligned_rows.append(aligned)
if not headers:
headers = [f"Column {i+1}" for i in range(max_columns)]
return {
"headers": headers,
"rows": aligned_rows,
"caption": caption,
"summary": summary,
}
def _scrap(
self,
url: str,
html: str,
word_count_threshold: int = MIN_WORD_THRESHOLD,
css_selector: str = None,
target_elements: List[str] = None,
**kwargs,
) -> Dict[str, Any]:
if not html:
@@ -1206,6 +1578,40 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
body = doc
base_domain = get_base_domain(url)
# Extract page context for link scoring (if enabled) - do this BEFORE any removals
page_context = None
if kwargs.get("score_links", False):
try:
# Extract title
title_elements = doc.xpath('//title')
page_title = title_elements[0].text_content() if title_elements else ""
# Extract headlines
headlines = []
for tag in ['h1', 'h2', 'h3']:
elements = doc.xpath(f'//{tag}')
for el in elements:
text = el.text_content().strip()
if text:
headlines.append(text)
headlines_text = ' '.join(headlines)
# Extract meta description
meta_desc_elements = doc.xpath('//meta[@name="description"]/@content')
meta_description = meta_desc_elements[0] if meta_desc_elements else ""
# Create page context
page_context = extract_page_context(page_title, headlines_text, meta_description, url)
except Exception:
page_context = {} # Fail gracefully
# Early removal of all images if exclude_all_images is set
# This is more efficient in lxml as we remove elements before any processing
if kwargs.get("exclude_all_images", False):
for img in body.xpath('//img'):
if img.getparent() is not None:
img.getparent().remove(img)
# Add comment removal
if kwargs.get("remove_comments", False):
@@ -1242,25 +1648,19 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
self._log("error", f"Error extracting metadata: {str(e)}", "SCRAPE")
meta = {}
# Handle CSS selector targeting
if css_selector:
content_element = None
if target_elements:
try:
selected_elements = body.cssselect(css_selector)
if not selected_elements:
return {
"markdown": "",
"cleaned_html": "",
"success": True,
"media": {"images": [], "videos": [], "audios": []},
"links": {"internal": [], "external": []},
"metadata": meta,
"message": f"No elements found for CSS selector: {css_selector}",
}
body = lhtml.Element("div")
body.extend(selected_elements)
for_content_targeted_element = []
for target_element in target_elements:
for_content_targeted_element.extend(body.cssselect(target_element))
content_element = lhtml.Element("div")
content_element.extend(copy.deepcopy(for_content_targeted_element))
except Exception as e:
self._log("error", f"Error with CSS selector: {str(e)}", "SCRAPE")
self._log("error", f"Error with target element detection: {str(e)}", "SCRAPE")
return None
else:
content_element = body
# Remove script and style tags
for tag in ["script", "style", "link", "meta", "noscript"]:
@@ -1284,7 +1684,7 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
form.getparent().remove(form)
# Process content
media = {"images": [], "videos": [], "audios": []}
media = {"images": [], "videos": [], "audios": [], "tables": []}
internal_links_dict = {}
external_links_dict = {}
@@ -1294,10 +1694,18 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
media,
internal_links_dict,
external_links_dict,
page_context=page_context,
base_domain=base_domain,
**kwargs,
)
if 'table' not in excluded_tags:
tables = body.xpath(".//table")
for table in tables:
if self.is_data_table(table, **kwargs):
table_data = self.extract_table_data(table)
media["tables"].append(table_data)
# Handle only_text option
if kwargs.get("only_text", False):
for tag in ONLY_TEXT_ELIGIBLE_TAGS:
@@ -1317,27 +1725,98 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
# Remove empty elements
self.remove_empty_elements_fast(body, 1)
# Remvoe unneeded attributes
# Remove unneeded attributes
self.remove_unwanted_attributes_fast(
body, keep_data_attributes=kwargs.get("keep_data_attributes", False)
)
# Generate output HTML
cleaned_html = lhtml.tostring(
body,
# body,
content_element,
encoding="unicode",
pretty_print=True,
method="html",
with_tail=False,
).strip()
# Create links dictionary in the format expected by LinkPreview
links = {
"internal": list(internal_links_dict.values()),
"external": list(external_links_dict.values()),
}
# Extract head content for links if configured
link_preview_config = kwargs.get("link_preview_config")
if link_preview_config is not None:
try:
import asyncio
from .link_preview import LinkPreview
from .models import Links, Link
verbose = link_preview_config.verbose
if verbose:
self._log("info", "Starting link head extraction for {internal} internal and {external} external links",
params={"internal": len(links["internal"]), "external": len(links["external"])}, tag="LINK_EXTRACT")
# Convert dict links to Link objects
internal_links = [Link(**link_data) for link_data in links["internal"]]
external_links = [Link(**link_data) for link_data in links["external"]]
links_obj = Links(internal=internal_links, external=external_links)
# Create a config object for LinkPreview
class TempCrawlerRunConfig:
def __init__(self, link_config, score_links):
self.link_preview_config = link_config
self.score_links = score_links
config = TempCrawlerRunConfig(link_preview_config, kwargs.get("score_links", False))
# Extract head content (run async operation in sync context)
async def extract_links():
async with LinkPreview(self.logger) as extractor:
return await extractor.extract_link_heads(links_obj, config)
# Run the async operation
try:
# Check if we're already in an async context
loop = asyncio.get_running_loop()
# If we're in an async context, we need to run in a thread
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(asyncio.run, extract_links())
updated_links = future.result()
except RuntimeError:
# No running loop, we can use asyncio.run directly
updated_links = asyncio.run(extract_links())
# Convert back to dict format
links["internal"] = [link.dict() for link in updated_links.internal]
links["external"] = [link.dict() for link in updated_links.external]
if verbose:
successful_internal = len([l for l in updated_links.internal if l.head_extraction_status == "valid"])
successful_external = len([l for l in updated_links.external if l.head_extraction_status == "valid"])
self._log("info", "Link head extraction completed: {internal_success}/{internal_total} internal, {external_success}/{external_total} external",
params={
"internal_success": successful_internal,
"internal_total": len(updated_links.internal),
"external_success": successful_external,
"external_total": len(updated_links.external)
}, tag="LINK_EXTRACT")
else:
self._log("info", "Link head extraction completed successfully", tag="LINK_EXTRACT")
except Exception as e:
self._log("error", f"Error during link head extraction: {str(e)}", tag="LINK_EXTRACT")
# Continue with original links if head extraction fails
return {
"cleaned_html": cleaned_html,
"success": success,
"media": media,
"links": {
"internal": list(internal_links_dict.values()),
"external": list(external_links_dict.values()),
},
"links": links,
"metadata": meta,
}
@@ -1369,7 +1848,12 @@ class LXMLWebScrapingStrategy(WebScrapingStrategy):
return {
"cleaned_html": cleaned_html,
"success": False,
"media": {"images": [], "videos": [], "audios": []},
"media": {
"images": [],
"videos": [],
"audios": [],
"tables": []
},
"links": {"internal": [], "external": []},
"metadata": {},
}

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
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai.utils import optimize_html, get_home_folder, preprocess_html_for_schema
from crawl4ai import JsonCssExtractionStrategy
from pathlib import Path
import json
import os
@@ -68,7 +68,8 @@ class GoogleSearchCrawler(BaseCrawler):
home_dir = get_home_folder() if not schema_cache_path else schema_cache_path
os.makedirs(f"{home_dir}/schema", exist_ok=True)
cleaned_html = optimize_html(html, threshold=100)
# cleaned_html = optimize_html(html, threshold=100)
cleaned_html = preprocess_html_for_schema(html)
organic_schema = None
if os.path.exists(f"{home_dir}/schema/organic_schema.json"):

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:

View File

@@ -10,6 +10,7 @@ from .filters import FilterChain
from .scorers import URLScorer
from . import DeepCrawlStrategy
from ..types import AsyncWebCrawler, CrawlerRunConfig, CrawlResult
from ..utils import normalize_url_for_deep_crawl, efficient_normalize_url_for_deep_crawl
from math import inf as infinity
class BFSDeepCrawlStrategy(DeepCrawlStrategy):
@@ -99,22 +100,26 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
# First collect all valid links
for link in links:
url = link.get("href")
if url in visited:
# Strip URL fragments to avoid duplicate crawling
# base_url = url.split('#')[0] if url else url
base_url = normalize_url_for_deep_crawl(url, source_url)
if base_url in visited:
continue
if not await self.can_process_url(url, next_depth):
self.stats.urls_skipped += 1
continue
# Score the URL if a scorer is provided
score = self.url_scorer.score(url) if self.url_scorer else 0
score = self.url_scorer.score(base_url) if self.url_scorer else 0
# Skip URLs with scores below the threshold
if score < self.score_threshold:
self.logger.debug(f"URL {url} skipped: score {score} below threshold {self.score_threshold}")
self.stats.urls_skipped += 1
continue
valid_links.append((url, score))
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
if len(valid_links) > remaining_capacity:
@@ -154,7 +159,6 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
while current_level and not self._cancel_event.is_set():
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)

View File

@@ -124,6 +124,7 @@ class URLPatternFilter(URLFilter):
"_simple_prefixes",
"_domain_patterns",
"_path_patterns",
"_reverse",
)
PATTERN_TYPES = {
@@ -138,8 +139,10 @@ class URLPatternFilter(URLFilter):
self,
patterns: Union[str, Pattern, List[Union[str, Pattern]]],
use_glob: bool = True,
reverse: bool = False,
):
super().__init__()
self._reverse = reverse
patterns = [patterns] if isinstance(patterns, (str, Pattern)) else patterns
self._simple_suffixes = set()
@@ -205,36 +208,40 @@ class URLPatternFilter(URLFilter):
@lru_cache(maxsize=10000)
def apply(self, url: str) -> bool:
"""Hierarchical pattern matching"""
# Quick suffix check (*.html)
if self._simple_suffixes:
path = url.split("?")[0]
if path.split("/")[-1].split(".")[-1] in self._simple_suffixes:
self._update_stats(True)
return True
result = True
self._update_stats(result)
return not result if self._reverse else result
# Domain check
if self._domain_patterns:
for pattern in self._domain_patterns:
if pattern.match(url):
self._update_stats(True)
return True
result = True
self._update_stats(result)
return not result if self._reverse else result
# Prefix check (/foo/*)
if self._simple_prefixes:
path = url.split("?")[0]
if any(path.startswith(p) for p in self._simple_prefixes):
self._update_stats(True)
return True
result = True
self._update_stats(result)
return not result if self._reverse else result
# Complex patterns
if self._path_patterns:
if any(p.search(url) for p in self._path_patterns):
self._update_stats(True)
return True
result = True
self._update_stats(result)
return not result if self._reverse else result
self._update_stats(False)
return False
result = False
self._update_stats(result)
return not result if self._reverse else result
class ContentTypeFilter(URLFilter):
@@ -427,6 +434,11 @@ class DomainFilter(URLFilter):
if isinstance(domains, str):
return {domains.lower()}
return {d.lower() for d in domains}
@staticmethod
def _is_subdomain(domain: str, parent_domain: str) -> bool:
"""Check if domain is a subdomain of parent_domain"""
return domain == parent_domain or domain.endswith(f".{parent_domain}")
@staticmethod
@lru_cache(maxsize=10000)
@@ -444,20 +456,26 @@ class DomainFilter(URLFilter):
domain = self._extract_domain(url)
# Early return for blocked domains
if domain in self._blocked_domains:
self._update_stats(False)
return False
# Check for blocked domains, including subdomains
for blocked in self._blocked_domains:
if self._is_subdomain(domain, blocked):
self._update_stats(False)
return False
# If no allowed domains specified, accept all non-blocked
if self._allowed_domains is None:
self._update_stats(True)
return True
# Final allowed domains check
result = domain in self._allowed_domains
self._update_stats(result)
return result
# Check if domain matches any allowed domain (including subdomains)
for allowed in self._allowed_domains:
if self._is_subdomain(domain, allowed):
self._update_stats(True)
return True
# No matches found
self._update_stats(False)
return False
class ContentRelevanceFilter(URLFilter):

File diff suppressed because it is too large Load Diff

View File

@@ -40,12 +40,55 @@ 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()
logger.success("Post-installation setup completed!", tag="COMPLETE")
def setup_builtin_browser():
"""Set up a builtin browser for use with Crawl4AI"""
try:
logger.info("Setting up builtin browser...", tag="INIT")
asyncio.run(_setup_builtin_browser())
logger.success("Builtin browser setup completed!", tag="COMPLETE")
except Exception as e:
logger.warning(f"Failed to set up builtin browser: {e}")
logger.warning("You can manually set up a builtin browser using 'crawl4ai-doctor builtin-browser-start'")
async def _setup_builtin_browser():
try:
# Import BrowserProfiler here to avoid circular imports
from .browser_profiler import BrowserProfiler
profiler = BrowserProfiler(logger=logger)
# Launch the builtin browser
cdp_url = await profiler.launch_builtin_browser(headless=True)
if cdp_url:
logger.success(f"Builtin browser launched at {cdp_url}", tag="BROWSER")
else:
logger.warning("Failed to launch builtin browser", tag="BROWSER")
except Exception as e:
logger.warning(f"Error setting up builtin browser: {e}", tag="BROWSER")
raise
def install_playwright():

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));
};

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

@@ -1,8 +1,8 @@
from abc import ABC, abstractmethod
from tabnanny import verbose
from typing import Optional, Dict, Any, Tuple
from .models import MarkdownGenerationResult
from .html2text import CustomHTML2Text
# from .types import RelevantContentFilter
from .content_filter_strategy import RelevantContentFilter
import re
from urllib.parse import urljoin
@@ -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
)

View File

@@ -1,6 +1,7 @@
from re import U
from pydantic import BaseModel, HttpUrl, PrivateAttr
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
from enum import Enum
from dataclasses import dataclass
from .ssl_certificate import SSLCertificate
@@ -28,7 +29,12 @@ class CrawlerTaskResult:
start_time: Union[datetime, float]
end_time: Union[datetime, float]
error_message: str = ""
retry_count: int = 0
wait_time: float = 0.0
@property
def success(self) -> bool:
return self.result.success
class CrawlStatus(Enum):
QUEUED = "QUEUED"
@@ -36,27 +42,39 @@ class CrawlStatus(Enum):
COMPLETED = "COMPLETED"
FAILED = "FAILED"
@dataclass
class CrawlStats:
task_id: str
url: str
status: CrawlStatus
start_time: Optional[datetime] = None
end_time: Optional[datetime] = None
start_time: Optional[Union[datetime, float]] = None
end_time: Optional[Union[datetime, float]] = None
memory_usage: float = 0.0
peak_memory: float = 0.0
error_message: str = ""
wait_time: float = 0.0
retry_count: int = 0
counted_requeue: bool = False
@property
def duration(self) -> str:
if not self.start_time:
return "0:00"
# Convert start_time to datetime if it's a float
start = self.start_time
if isinstance(start, float):
start = datetime.fromtimestamp(start)
# Get end time or use current time
end = self.end_time or datetime.now()
duration = end - self.start_time
# Convert end_time to datetime if it's a float
if isinstance(end, float):
end = datetime.fromtimestamp(end)
duration = end - start
return str(timedelta(seconds=int(duration.total_seconds())))
class DisplayMode(Enum):
DETAILED = "DETAILED"
AGGREGATED = "AGGREGATED"
@@ -73,21 +91,11 @@ class TokenUsage:
completion_tokens_details: Optional[dict] = None
prompt_tokens_details: Optional[dict] = None
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:
@@ -108,9 +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]] = {}
@@ -119,6 +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
@@ -129,6 +149,9 @@ 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}]
class Config:
arbitrary_types_allowed = True
@@ -149,7 +172,11 @@ class CrawlResult(BaseModel):
markdown_result = data.pop('markdown', None)
super().__init__(**data)
if markdown_result is not None:
self._markdown = markdown_result
self._markdown = (
MarkdownGenerationResult(**markdown_result)
if isinstance(markdown_result, dict)
else markdown_result
)
@property
def markdown(self):
@@ -241,6 +268,40 @@ class StringCompatibleMarkdown(str):
def __getattr__(self, name):
return getattr(self._markdown_result, name)
CrawlResultT = TypeVar('CrawlResultT', bound=CrawlResult)
class CrawlResultContainer(Generic[CrawlResultT]):
def __init__(self, results: Union[CrawlResultT, List[CrawlResultT]]):
# Normalize to a list
if isinstance(results, list):
self._results = results
else:
self._results = [results]
def __iter__(self):
return iter(self._results)
def __getitem__(self, index):
return self._results[index]
def __len__(self):
return len(self._results)
def __getattr__(self, attr):
# Delegate attribute access to the first element.
if self._results:
return getattr(self._results[0], attr)
raise AttributeError(f"{self.__class__.__name__} object has no attribute '{attr}'")
def __repr__(self):
return f"{self.__class__.__name__}({self._results!r})"
RunManyReturn = Union[
CrawlResultContainer[CrawlResultT],
AsyncGenerator[CrawlResultT, None]
]
# END of backward compatibility code for markdown/markdown_v2.
# When removing this code in the future, make sure to:
# 1. Replace the private attribute and property with a standard field
@@ -253,15 +314,17 @@ 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
class Config:
arbitrary_types_allowed = True
###############################
# Scraping Models
###############################
@@ -282,6 +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):
@@ -292,6 +361,7 @@ class Media(BaseModel):
audios: List[
MediaItem
] = [] # Using MediaItem model for now, can be extended with Audio model if needed
tables: List[Dict] = [] # Table data extracted from HTML tables
class Links(BaseModel):

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

@@ -203,6 +203,62 @@ Avoid Common Mistakes:
Result
Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""
PROMPT_EXTRACT_INFERRED_SCHEMA = """Here is the content from the URL:
<url>{URL}</url>
<url_content>
{HTML}
</url_content>
Please carefully read the URL content and the user's request. Analyze the page structure and infer the most appropriate JSON schema based on the content and request.
Extraction Strategy:
1. First, determine if the page contains repetitive items (like multiple products, articles, etc.) or a single content item (like a single article or page).
2. For repetitive items: Identify the common pattern and extract each instance as a separate JSON object in an array.
3. For single content: Extract the key information into a comprehensive JSON object that captures the essential details.
Extraction instructions:
Return the extracted information as a list of JSON objects. For repetitive content, each object in the list should correspond to a distinct item. For single content, you may return just one detailed JSON object. Wrap the entire JSON list in <blocks>...</blocks> XML tags.
Schema Design Guidelines:
- Create meaningful property names that clearly describe the data they contain
- Use nested objects for hierarchical information
- Use arrays for lists of related items
- Include all information requested by the user
- Maintain consistency in property names and data structures
- Only include properties that are actually present in the content
- For dates, prefer ISO format (YYYY-MM-DD)
- For prices or numeric values, extract them without currency symbols when possible
Quality Reflection:
Before outputting your final answer, double check that:
1. The inferred schema makes logical sense for the type of content
2. All requested information is included
3. The JSON is valid and could be parsed without errors
4. Property names are consistent and descriptive
5. The structure is optimal for the type of data being represented
Avoid Common Mistakes:
- Do NOT add any comments using "//" or "#" in the JSON output. It causes parsing errors.
- Make sure the JSON is properly formatted with curly braces, square brackets, and commas in the right places.
- Do not miss closing </blocks> tag at the end of the JSON output.
- Do not generate Python code showing how to do the task; this is your task to extract the information and return it in JSON format.
- Ensure consistency in property names across all objects
- Don't include empty properties or null values unless they're meaningful
- For repetitive content, ensure all objects follow the same schema
Important: If user specific instruction is provided, then stress significantly on what user is requesting and describing about the schema of end result (if any). If user is requesting to extract specific information, then focus on that and ignore the rest of the content.
<user_request>
{REQUEST}
</user_request>
Result:
Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly.
DO NOT ADD ANY PRE OR POST COMMENTS. JUST RETURN THE JSON OBJECTS INSIDE <blocks>...</blocks> TAGS.
CRITICAL: The content inside the <blocks> tags MUST be a direct array of JSON objects (starting with '[' and ending with ']'), not a dictionary/object containing an array. For example, use <blocks>[{...}, {...}]</blocks> instead of <blocks>{"items": [{...}, {...}]}</blocks>. This is essential for proper parsing.
"""
PROMPT_FILTER_CONTENT = """Your task is to filter and convert HTML content into clean, focused markdown that's optimized for use with LLMs and information retrieval systems.
@@ -998,4 +1054,525 @@ Your output must:
5. Include all required fields
6. Use valid XPath selectors
</output_requirements>
"""
"""
GENERATE_SCRIPT_PROMPT = """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

@@ -1,19 +1,133 @@
from typing import List, Dict, Optional
from abc import ABC, abstractmethod
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,
server: str,
username: Optional[str] = None,
password: Optional[str] = None,
ip: Optional[str] = None,
):
"""Configuration class for a single proxy.
Args:
server: Proxy server URL (e.g., "http://127.0.0.1:8080")
username: Optional username for proxy authentication
password: Optional password for proxy authentication
ip: Optional IP address for verification purposes
"""
self.server = server
self.username = username
self.password = password
# Extract IP from server if not explicitly provided
self.ip = ip or self._extract_ip_from_server()
def _extract_ip_from_server(self) -> Optional[str]:
"""Extract IP address from server URL."""
try:
# Simple extraction assuming http://ip:port format
if "://" in self.server:
parts = self.server.split("://")[1].split(":")
return parts[0]
else:
parts = self.server.split(":")
return parts[0]
except Exception:
return None
@staticmethod
def from_string(proxy_str: str) -> "ProxyConfig":
"""Create a ProxyConfig from a string in the format 'ip:port:username:password'."""
parts = proxy_str.split(":")
if len(parts) == 4: # ip:port:username:password
ip, port, username, password = parts
return ProxyConfig(
server=f"http://{ip}:{port}",
username=username,
password=password,
ip=ip
)
elif len(parts) == 2: # ip:port only
ip, port = parts
return ProxyConfig(
server=f"http://{ip}:{port}",
ip=ip
)
else:
raise ValueError(f"Invalid proxy string format: {proxy_str}")
@staticmethod
def from_dict(proxy_dict: Dict) -> "ProxyConfig":
"""Create a ProxyConfig from a dictionary."""
return ProxyConfig(
server=proxy_dict.get("server"),
username=proxy_dict.get("username"),
password=proxy_dict.get("password"),
ip=proxy_dict.get("ip")
)
@staticmethod
def from_env(env_var: str = "PROXIES") -> List["ProxyConfig"]:
"""Load proxies from environment variable.
Args:
env_var: Name of environment variable containing comma-separated proxy strings
Returns:
List of ProxyConfig objects
"""
proxies = []
try:
proxy_list = os.getenv(env_var, "").split(",")
for proxy in proxy_list:
if not proxy:
continue
proxies.append(ProxyConfig.from_string(proxy))
except Exception as e:
print(f"Error loading proxies from environment: {e}")
return proxies
def to_dict(self) -> Dict:
"""Convert to dictionary representation."""
return {
"server": self.server,
"username": self.username,
"password": self.password,
"ip": self.ip
}
def clone(self, **kwargs) -> "ProxyConfig":
"""Create a copy of this configuration with updated values.
Args:
**kwargs: Key-value pairs of configuration options to update
Returns:
ProxyConfig: A new instance with the specified updates
"""
config_dict = self.to_dict()
config_dict.update(kwargs)
return ProxyConfig.from_dict(config_dict)
from crawl4ai.configs import ProxyConfig
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

@@ -1,14 +1,195 @@
from typing import TYPE_CHECKING, Union
AsyncWebCrawler = Union['AsyncWebCrawlerType'] # Note the string literal
CrawlerRunConfig = Union['CrawlerRunConfigType']
# Logger types
AsyncLoggerBase = Union['AsyncLoggerBaseType']
AsyncLogger = Union['AsyncLoggerType']
# Crawler core types
AsyncWebCrawler = Union['AsyncWebCrawlerType']
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']
# Proxy types
ProxyRotationStrategy = Union['ProxyRotationStrategyType']
RoundRobinProxyStrategy = Union['RoundRobinProxyStrategyType']
# Extraction types
ExtractionStrategy = Union['ExtractionStrategyType']
LLMExtractionStrategy = Union['LLMExtractionStrategyType']
CosineStrategy = Union['CosineStrategyType']
JsonCssExtractionStrategy = Union['JsonCssExtractionStrategyType']
JsonXPathExtractionStrategy = Union['JsonXPathExtractionStrategyType']
# Chunking types
ChunkingStrategy = Union['ChunkingStrategyType']
RegexChunking = Union['RegexChunkingType']
# Markdown generation types
DefaultMarkdownGenerator = Union['DefaultMarkdownGeneratorType']
MarkdownGenerationResult = Union['MarkdownGenerationResultType']
# Content filter types
RelevantContentFilter = Union['RelevantContentFilterType']
PruningContentFilter = Union['PruningContentFilterType']
BM25ContentFilter = Union['BM25ContentFilterType']
LLMContentFilter = Union['LLMContentFilterType']
# Dispatcher types
BaseDispatcher = Union['BaseDispatcherType']
MemoryAdaptiveDispatcher = Union['MemoryAdaptiveDispatcherType']
SemaphoreDispatcher = Union['SemaphoreDispatcherType']
RateLimiter = Union['RateLimiterType']
CrawlerMonitor = Union['CrawlerMonitorType']
DisplayMode = Union['DisplayModeType']
RunManyReturn = Union['RunManyReturnType']
# Docker client
Crawl4aiDockerClient = Union['Crawl4aiDockerClientType']
# Deep crawling types
DeepCrawlStrategy = Union['DeepCrawlStrategyType']
BFSDeepCrawlStrategy = Union['BFSDeepCrawlStrategyType']
FilterChain = Union['FilterChainType']
ContentTypeFilter = Union['ContentTypeFilterType']
DomainFilter = Union['DomainFilterType']
URLFilter = Union['URLFilterType']
FilterStats = Union['FilterStatsType']
SEOFilter = Union['SEOFilterType']
KeywordRelevanceScorer = Union['KeywordRelevanceScorerType']
URLScorer = Union['URLScorerType']
CompositeScorer = Union['CompositeScorerType']
DomainAuthorityScorer = Union['DomainAuthorityScorerType']
FreshnessScorer = Union['FreshnessScorerType']
PathDepthScorer = Union['PathDepthScorerType']
BestFirstCrawlingStrategy = Union['BestFirstCrawlingStrategyType']
DFSDeepCrawlStrategy = Union['DFSDeepCrawlStrategyType']
DeepCrawlDecorator = Union['DeepCrawlDecoratorType']
# Only import types during type checking to avoid circular imports
if TYPE_CHECKING:
from . import (
# Logger imports
from .async_logger import (
AsyncLoggerBase as AsyncLoggerBaseType,
AsyncLogger as AsyncLoggerType,
)
# Crawler core imports
from .async_webcrawler import (
AsyncWebCrawler as AsyncWebCrawlerType,
CacheMode as CacheModeType,
)
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 (
BrowserConfig as BrowserConfigType,
CrawlerRunConfig as CrawlerRunConfigType,
CrawlResult as CrawlResultType,
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,
)
# Proxy imports
from .proxy_strategy import (
ProxyRotationStrategy as ProxyRotationStrategyType,
RoundRobinProxyStrategy as RoundRobinProxyStrategyType,
)
# Extraction imports
from .extraction_strategy import (
ExtractionStrategy as ExtractionStrategyType,
LLMExtractionStrategy as LLMExtractionStrategyType,
CosineStrategy as CosineStrategyType,
JsonCssExtractionStrategy as JsonCssExtractionStrategyType,
JsonXPathExtractionStrategy as JsonXPathExtractionStrategyType,
)
# Chunking imports
from .chunking_strategy import (
ChunkingStrategy as ChunkingStrategyType,
RegexChunking as RegexChunkingType,
)
# Markdown generation imports
from .markdown_generation_strategy import (
DefaultMarkdownGenerator as DefaultMarkdownGeneratorType,
)
from .models import MarkdownGenerationResult as MarkdownGenerationResultType
# Content filter imports
from .content_filter_strategy import (
RelevantContentFilter as RelevantContentFilterType,
PruningContentFilter as PruningContentFilterType,
BM25ContentFilter as BM25ContentFilterType,
LLMContentFilter as LLMContentFilterType,
)
# Dispatcher imports
from .async_dispatcher import (
BaseDispatcher as BaseDispatcherType,
MemoryAdaptiveDispatcher as MemoryAdaptiveDispatcherType,
SemaphoreDispatcher as SemaphoreDispatcherType,
RateLimiter as RateLimiterType,
CrawlerMonitor as CrawlerMonitorType,
DisplayMode as DisplayModeType,
RunManyReturn as RunManyReturnType,
)
)
# Docker client
from .docker_client import Crawl4aiDockerClient as Crawl4aiDockerClientType
# Deep crawling imports
from .deep_crawling import (
DeepCrawlStrategy as DeepCrawlStrategyType,
BFSDeepCrawlStrategy as BFSDeepCrawlStrategyType,
FilterChain as FilterChainType,
ContentTypeFilter as ContentTypeFilterType,
DomainFilter as DomainFilterType,
URLFilter as URLFilterType,
FilterStats as FilterStatsType,
SEOFilter as SEOFilterType,
KeywordRelevanceScorer as KeywordRelevanceScorerType,
URLScorer as URLScorerType,
CompositeScorer as CompositeScorerType,
DomainAuthorityScorer as DomainAuthorityScorerType,
FreshnessScorer as FreshnessScorerType,
PathDepthScorer as PathDepthScorerType,
BestFirstCrawlingStrategy as BestFirstCrawlingStrategyType,
DFSDeepCrawlStrategy as DFSDeepCrawlStrategyType,
DeepCrawlDecorator as DeepCrawlDecoratorType,
)
def create_llm_config(*args, **kwargs) -> 'LLMConfigType':
from .async_configs import LLMConfig
return LLMConfig(*args, **kwargs)

View File

@@ -1,5 +1,4 @@
import time
from urllib.parse import urlparse
from concurrent.futures import ThreadPoolExecutor, as_completed
from bs4 import BeautifulSoup, Comment, element, Tag, NavigableString
import json
@@ -7,6 +6,7 @@ import html
import lxml
import re
import os
import subprocess
import platform
from .prompts import PROMPT_EXTRACT_BLOCKS
from array import array
@@ -21,18 +21,19 @@ 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
from functools import wraps
import asyncio
from lxml import etree, html as lhtml
import sqlite3
import hashlib
from urllib.robotparser import RobotFileParser
import aiohttp
from urllib.parse import urlparse, urlunparse
from functools import lru_cache
from packaging import version
from . import __version__
@@ -135,13 +136,20 @@ 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
@@ -150,7 +158,7 @@ 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)
@@ -440,14 +448,13 @@ 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"])
@@ -479,12 +486,12 @@ 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)
@@ -1110,6 +1117,23 @@ 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
@@ -1152,6 +1176,20 @@ 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", "")
@@ -1173,6 +1211,20 @@ 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)
@@ -1187,6 +1239,16 @@ 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)
@@ -1197,8 +1259,6 @@ 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)
@@ -1550,7 +1610,7 @@ def extract_xml_tags(string):
return list(set(tags))
def extract_xml_data(tags, string):
def extract_xml_data_legacy(tags, string):
"""
Extract data for specified XML tags from a string.
@@ -1579,6 +1639,38 @@ def extract_xml_data(tags, string):
return data
def extract_xml_data(tags, string):
"""
Extract data for specified XML tags from a string, returning the longest content for each tag.
How it works:
1. Finds all occurrences of each tag in the string using regex.
2. For each tag, selects the occurrence with the longest content.
3. Returns a dictionary of tag-content pairs.
Args:
tags (List[str]): The list of XML tags to extract.
string (str): The input string containing XML data.
Returns:
Dict[str, str]: A dictionary with tag names as keys and longest extracted content as values.
"""
data = {}
for tag in tags:
pattern = f"<{tag}>(.*?)</{tag}>"
matches = re.findall(pattern, string, re.DOTALL)
if matches:
# Find the longest content for this tag
longest_content = max(matches, key=len).strip()
data[tag] = longest_content
else:
data[tag] = ""
return data
def perform_completion_with_backoff(
provider,
@@ -1647,6 +1739,19 @@ def perform_completion_with_backoff(
"content": ["Rate limit error. Please try again later."],
}
]
except Exception as e:
raise e # Raise any other exceptions immediately
# print("Error during completion request:", str(e))
# error_message = e.message
# return [
# {
# "index": 0,
# "tags": ["error"],
# "content": [
# f"Error during LLM completion request. {error_message}"
# ],
# }
# ]
def extract_blocks(url, html, provider=DEFAULT_PROVIDER, api_token=None, base_url=None):
@@ -1957,11 +2062,91 @@ def normalize_url(href, base_url):
if not parsed_base.scheme or not parsed_base.netloc:
raise ValueError(f"Invalid base URL format: {base_url}")
# Ensure base_url ends with a trailing slash if it's a directory path
if not base_url.endswith('/'):
base_url = base_url + '/'
# Use urljoin to handle all cases
normalized = urljoin(base_url, href.strip())
return normalized
def normalize_url_for_deep_crawl(href, base_url):
"""Normalize URLs to ensure consistent format"""
from urllib.parse import urljoin, urlparse, urlunparse, parse_qs, urlencode
# Handle None or empty values
if not href:
return None
# Use urljoin to handle relative URLs
full_url = urljoin(base_url, href.strip())
# Parse the URL for normalization
parsed = urlparse(full_url)
# Convert hostname to lowercase
netloc = parsed.netloc.lower()
# Remove fragment entirely
fragment = ''
# Normalize query parameters if needed
query = parsed.query
if query:
# Parse query parameters
params = parse_qs(query)
# Remove tracking parameters (example - customize as needed)
tracking_params = ['utm_source', 'utm_medium', 'utm_campaign', 'ref', 'fbclid']
for param in tracking_params:
if param in params:
del params[param]
# Rebuild query string, sorted for consistency
query = urlencode(params, doseq=True) if params else ''
# Build normalized URL
normalized = urlunparse((
parsed.scheme,
netloc,
parsed.path.rstrip('/'), # Normalize trailing slash
parsed.params,
query,
fragment
))
return normalized
@lru_cache(maxsize=10000)
def efficient_normalize_url_for_deep_crawl(href, base_url):
"""Efficient URL normalization with proper parsing"""
from urllib.parse import urljoin
if not href:
return None
# Resolve relative URLs
full_url = urljoin(base_url, href.strip())
# Use proper URL parsing
parsed = urlparse(full_url)
# Only perform the most critical normalizations
# 1. Lowercase hostname
# 2. Remove fragment
normalized = urlunparse((
parsed.scheme,
parsed.netloc.lower(),
parsed.path.rstrip('/'),
parsed.params,
parsed.query,
'' # Remove fragment
))
return normalized
def normalize_url_tmp(href, base_url):
"""Normalize URLs to ensure consistent format"""
# Extract protocol and domain from base URL
@@ -2540,3 +2725,426 @@ class HeadPeekr:
def get_title(head_content: str):
title_match = re.search(r'<title>(.*?)</title>', head_content, re.IGNORECASE | re.DOTALL)
return title_match.group(1) if title_match else None
def preprocess_html_for_schema(html_content, text_threshold=100, attr_value_threshold=200, max_size=100000):
"""
Preprocess HTML to reduce size while preserving structure for schema generation.
Args:
html_content (str): Raw HTML content
text_threshold (int): Maximum length for text nodes before truncation
attr_value_threshold (int): Maximum length for attribute values before truncation
max_size (int): Target maximum size for output HTML
Returns:
str: Preprocessed HTML content
"""
try:
# Parse HTML with error recovery
parser = etree.HTMLParser(remove_comments=True, remove_blank_text=True)
tree = lhtml.fromstring(html_content, parser=parser)
# 1. Remove HEAD section (keep only BODY)
head_elements = tree.xpath('//head')
for head in head_elements:
if head.getparent() is not None:
head.getparent().remove(head)
# 2. Define tags to remove completely
tags_to_remove = [
'script', 'style', 'noscript', 'iframe', 'canvas', 'svg',
'video', 'audio', 'source', 'track', 'map', 'area'
]
# Remove unwanted elements
for tag in tags_to_remove:
elements = tree.xpath(f'//{tag}')
for element in elements:
if element.getparent() is not None:
element.getparent().remove(element)
# 3. Process remaining elements to clean attributes and truncate text
for element in tree.iter():
# Skip if we're at the root level
if element.getparent() is None:
continue
# Clean non-essential attributes but preserve structural ones
# attribs_to_keep = {'id', 'class', 'name', 'href', 'src', 'type', 'value', 'data-'}
# This is more aggressive than the previous version
attribs_to_keep = {'id', 'class', 'name', 'type', 'value'}
# attributes_hates_truncate = ['id', 'class', "data-"]
# This means, I don't care, if an attribute is too long, truncate it, go and find a better css selector to build a schema
attributes_hates_truncate = []
# Process each attribute
for attrib in list(element.attrib.keys()):
# Keep if it's essential or starts with data-
if not (attrib in attribs_to_keep or attrib.startswith('data-')):
element.attrib.pop(attrib)
# Truncate long attribute values except for selectors
elif attrib not in attributes_hates_truncate and len(element.attrib[attrib]) > attr_value_threshold:
element.attrib[attrib] = element.attrib[attrib][:attr_value_threshold] + '...'
# Truncate text content if it's too long
if element.text and len(element.text.strip()) > text_threshold:
element.text = element.text.strip()[:text_threshold] + '...'
# Also truncate tail text if present
if element.tail and len(element.tail.strip()) > text_threshold:
element.tail = element.tail.strip()[:text_threshold] + '...'
# 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
cls = el.get('class')
if not cls:
continue
# ── 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:
seen[sig] = None
# # 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')
# If still over the size limit, apply more aggressive truncation
if len(result) > max_size:
return result[:max_size] + "..."
return result
except Exception as e:
# Fallback for parsing errors
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))

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View File

@@ -1,7 +1,10 @@
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
import logging
from typing import Optional, AsyncGenerator
@@ -18,7 +21,8 @@ from crawl4ai import (
CacheMode,
BrowserConfig,
MemoryAdaptiveDispatcher,
RateLimiter
RateLimiter,
LLMConfig
)
from crawl4ai.utils import perform_completion_with_backoff
from crawl4ai.content_filter_strategy import (
@@ -38,8 +42,19 @@ from utils import (
decode_redis_hash
)
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,
@@ -47,6 +62,8 @@ 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:
@@ -60,7 +77,7 @@ 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.
@@ -103,8 +120,10 @@ async def process_llm_extraction(
else:
api_key = os.environ.get(config["llm"].get("api_key_env", None), "")
llm_strategy = LLMExtractionStrategy(
provider=config["llm"]["provider"],
api_token=api_key,
llm_config=LLMConfig(
provider=config["llm"]["provider"],
api_token=api_key
),
instruction=instruction,
schema=json.loads(schema) if schema else None,
)
@@ -164,8 +183,10 @@ async def handle_markdown_request(
FilterType.FIT: PruningContentFilter(),
FilterType.BM25: BM25ContentFilter(user_query=query or ""),
FilterType.LLM: LLMContentFilter(
provider=config["llm"]["provider"],
api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
llm_config=LLMConfig(
provider=config["llm"]["provider"],
api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
),
instruction=query or "Extract main content"
)
}[filter_type]
@@ -253,7 +274,9 @@ 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}")
@@ -267,7 +290,7 @@ 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)
@@ -345,7 +368,9 @@ 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
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')
@@ -359,10 +384,11 @@ 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],
@@ -371,7 +397,13 @@ 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)
@@ -379,26 +411,68 @@ 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 = await crawler.arun_many(
urls=urls,
config=crawler_config,
dispatcher=dispatcher
)
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")
return {
"success": True,
"results": [result.model_dump() for result in 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: {close_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(
@@ -410,9 +484,11 @@ 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"],
@@ -421,8 +497,11 @@ 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,
@@ -433,10 +512,60 @@ 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: {close_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}

View File

@@ -10,7 +10,7 @@ from pydantic.main import BaseModel
import base64
instance = JWT()
security = HTTPBearer()
security = HTTPBearer(auto_error=False)
SECRET_KEY = os.environ.get("SECRET_KEY", "mysecret")
ACCESS_TOKEN_EXPIRE_MINUTES = 60
@@ -30,6 +30,9 @@ def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -
def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)) -> Dict:
"""Verify the JWT token from the Authorization header."""
if credentials is None:
return None
token = credentials.credentials
verifying_key = get_jwk_from_secret(SECRET_KEY)
try:
@@ -38,9 +41,15 @@ def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security))
except Exception:
raise HTTPException(status_code=401, detail="Invalid or expired token")
def get_token_dependency(config: Dict):
"""Return the token dependency if JWT is enabled, else None."""
return verify_token if config.get("security", {}).get("jwt_enabled", False) else None
"""Return the token dependency if JWT is enabled, else a function that returns None."""
if config.get("security", {}).get("jwt_enabled", False):
return verify_token
else:
return lambda: None
class TokenRequest(BaseModel):
email: EmailStr

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@@ -3,8 +3,9 @@ app:
title: "Crawl4AI API"
version: "1.0.0"
host: "0.0.0.0"
port: 8000
reload: True
port: 11234
reload: False
workers: 1
timeout_keep_alive: 300
# Default LLM Configuration
@@ -38,8 +39,8 @@ rate_limiting:
# Security Configuration
security:
enabled: true
jwt_enabled: true
enabled: false
jwt_enabled: false
https_redirect: false
trusted_hosts: ["*"]
headers:
@@ -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)

99
deploy/docker/job.py Normal file
View File

@@ -0,0 +1,99 @@
"""
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
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,
)
@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")
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
)
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 +1,955 @@
<!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>
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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;
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.CodeMirror-gutters {
background-color: #1E1E1E !important;
border-right: 1px solid #3F3F44 !important;
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.cm-s-darcula .cm-string {
color: #F380F5 !important;
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.cm-s-darcula .cm-number {
color: #D5CEBF !important;
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/* Add to your <style> section or Tailwind config */
.hljs {
background: #1E1E1E !important;
border-radius: 4px;
padding: 1rem !important;
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pre code.hljs {
display: block;
overflow-x: auto;
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/* Language-specific colors */
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color: #4EFFFF;
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pre code {
white-space: pre-wrap;
word-break: break-word;
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transition: all 0.2s ease;
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.copy-btn:hover {
opacity: 1;
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.tab-content:hover .copy-btn {
opacity: 0.7;
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.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>
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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'));
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// Initialize clipboard and highlight.js
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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,
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CrawlerRunConfig: `CrawlerRunConfig(
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extra_args=[
"--no-sandbox",
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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' }
});
} 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

@@ -45,10 +45,10 @@ 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."""

View File

@@ -1,16 +1,21 @@
# 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:-}
volumes:
- /dev/shm:/dev/shm
- /dev/shm:/dev/shm # Chromium performance
deploy:
resources:
limits:
@@ -24,42 +29,21 @@ 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`.

View File

@@ -0,0 +1,446 @@
#!/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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@@ -0,0 +1,381 @@
#!/usr/bin/env python3
"""
Stage-2 Insights builder
------------------------
Reads companies.jsonl & people.jsonl (Stage-1 output) and produces:
• company_graph.json
• org_chart_<handle>.json (one per company)
• decision_makers.csv
• graph_view.html (interactive visualisation)
Run:
python c4ai_insights.py --in ./stage1_out --out ./stage2_out
Author : Tom @ Kidocode, 2025-04-28
"""
from __future__ import annotations
# ───────────────────────────────────────────────────────────────────────────────
# Imports & Third-party
# ───────────────────────────────────────────────────────────────────────────────
import argparse, asyncio, json, pathlib, random
from datetime import datetime, UTC
from types import SimpleNamespace
from pathlib import Path
from typing import List, Dict, Any
# Pretty CLI UX
from rich.console import Console
from rich.logging import RichHandler
from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
# ───────────────────────────────────────────────────────────────────────────────
BASE_DIR = pathlib.Path(__file__).resolve().parent
# ───────────────────────────────────────────────────────────────────────────────
# 3rd-party deps
# ───────────────────────────────────────────────────────────────────────────────
import numpy as np
# from sentence_transformers import SentenceTransformer
# from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import hashlib
from litellm import completion #Support any LLM Provider
# ───────────────────────────────────────────────────────────────────────────────
# Utils
# ───────────────────────────────────────────────────────────────────────────────
def load_jsonl(path: Path) -> List[Dict[str, Any]]:
with open(path, "r", encoding="utf-8") as f:
return [json.loads(l) for l in f]
def dump_json(obj, path: Path):
with open(path, "w", encoding="utf-8") as f:
json.dump(obj, f, ensure_ascii=False, indent=2)
# ───────────────────────────────────────────────────────────────────────────────
# Constants
# ───────────────────────────────────────────────────────────────────────────────
BASE_DIR = pathlib.Path(__file__).resolve().parent
# ───────────────────────────────────────────────────────────────────────────────
# Debug defaults (mirrors Stage-1 trick)
# ───────────────────────────────────────────────────────────────────────────────
def dev_defaults() -> SimpleNamespace:
return SimpleNamespace(
in_dir="./samples",
out_dir="./samples/insights",
embed_model="all-MiniLM-L6-v2",
top_k=10,
llm_provider="openai/gpt-4.1",
llm_api_key=None,
max_llm_tokens=8000,
llm_temperature=1.0,
stub=False, # Set to True to use a stub for org-chart inference
llm_base_url=None, # e.g., "https://api.openai.com/v1" for OpenAI
workers=4
)
# ───────────────────────────────────────────────────────────────────────────────
# Graph builders
# ───────────────────────────────────────────────────────────────────────────────
def embed_descriptions(companies, model_name:str, opts) -> np.ndarray:
from sentence_transformers import SentenceTransformer
console = Console()
console.print(f"Using embedding model: [bold cyan]{model_name}[/]")
cache_path = BASE_DIR / Path(opts.out_dir) / "embeds_cache.json"
cache = {}
if cache_path.exists():
with open(cache_path) as f:
cache = json.load(f)
# flush cache if model differs
if cache.get("_model") != model_name:
cache = {}
model = SentenceTransformer(model_name)
new_texts, new_indices = [], []
vectors = np.zeros((len(companies), 384), dtype=np.float32)
for idx, comp in enumerate(companies):
text = comp.get("about") or comp.get("descriptor","")
h = hashlib.sha1(text.encode("utf-8")).hexdigest()
cached = cache.get(comp["handle"])
if cached and cached["hash"] == h:
vectors[idx] = np.array(cached["vector"], dtype=np.float32)
else:
new_texts.append(text)
new_indices.append((idx, comp["handle"], h))
if new_texts:
embeds = model.encode(new_texts, show_progress_bar=False, convert_to_numpy=True)
for vec, (idx, handle, h) in zip(embeds, new_indices):
vectors[idx] = vec
cache[handle] = {"hash": h, "vector": vec.tolist()}
cache["_model"] = model_name
with open(cache_path, "w") as f:
json.dump(cache, f)
return vectors
def build_company_graph(companies, embeds:np.ndarray, top_k:int) -> Dict[str,Any]:
from sklearn.metrics.pairwise import cosine_similarity
sims = cosine_similarity(embeds)
nodes, edges = [], []
for i,c in enumerate(companies):
node = dict(
id=c["handle"].strip("/"),
name=c["name"],
handle=c["handle"],
about=c.get("about",""),
people_url=c.get("people_url",""),
industry=c.get("descriptor","").split("")[0].strip(),
geoUrn=c.get("geoUrn"),
followers=c.get("followers",0),
# desc_embed=embeds[i].tolist(),
desc_embed=[],
)
nodes.append(node)
# pick top-k most similar except itself
top_idx = np.argsort(sims[i])[::-1][1:top_k+1]
for j in top_idx:
tgt = companies[j]
weight = float(sims[i,j])
if node["industry"] == tgt.get("descriptor","").split("")[0].strip():
weight += 0.10
if node["geoUrn"] == tgt.get("geoUrn"):
weight += 0.05
tgt['followers'] = tgt.get("followers", None) or 1
node["followers"] = node.get("followers", None) or 1
follower_ratio = min(node["followers"], tgt.get("followers",1)) / max(node["followers"] or 1, tgt.get("followers",1))
weight += 0.05 * follower_ratio
edges.append(dict(
source=node["id"],
target=tgt["handle"].strip("/"),
weight=round(weight,4),
drivers=dict(
embed_sim=round(float(sims[i,j]),4),
industry_match=0.10 if node["industry"] == tgt.get("descriptor","").split("")[0].strip() else 0,
geo_overlap=0.05 if node["geoUrn"] == tgt.get("geoUrn") else 0,
)
))
# return {"nodes":nodes,"edges":edges,"meta":{"generated_at":datetime.now(UTC).isoformat()}}
return {"nodes":nodes,"edges":edges,"meta":{"generated_at":datetime.now(UTC).isoformat()}}
# ───────────────────────────────────────────────────────────────────────────────
# Org-chart via LLM
# ───────────────────────────────────────────────────────────────────────────────
async def infer_org_chart_llm(company, people, llm_provider:str, api_key:str, max_tokens:int, temperature:float, stub:bool=False, base_url:str=None):
if stub:
# Tiny fake org-chart when debugging offline
chief = random.choice(people)
nodes = [{
"id": chief["profile_url"],
"name": chief["name"],
"title": chief["headline"],
"dept": chief["headline"].split()[:1][0],
"yoe_total": 8,
"yoe_current": 2,
"seniority_score": 0.8,
"decision_score": 0.9,
"avatar_url": chief.get("avatar_url")
}]
return {"nodes":nodes,"edges":[],"meta":{"debug_stub":True,"generated_at":datetime.now(UTC).isoformat()}}
prompt = [
{"role":"system","content":"You are an expert B2B org-chart reasoner."},
{"role":"user","content":f"""Here is the company description:
<company>
{json.dumps(company, ensure_ascii=False)}
</company>
Here is a JSON list of employees:
<employees>
{json.dumps(people, ensure_ascii=False)}
</employees>
1) Build a reporting tree (manager -> direct reports)
2) For each person output a decision_score 0-1 for buying new software
Return JSON: {{ "nodes":[{{id,name,title,dept,yoe_total,yoe_current,seniority_score,decision_score,avatar_url,profile_url}}], "edges":[{{source,target,type,confidence}}] }}
"""}
]
resp = completion(
model=llm_provider,
messages=prompt,
max_tokens=max_tokens,
temperature=temperature,
response_format={"type":"json_object"},
api_key=api_key,
base_url=base_url
)
chart = json.loads(resp.choices[0].message.content)
chart["meta"] = dict(
model=llm_provider,
generated_at=datetime.now(UTC).isoformat()
)
return chart
# ───────────────────────────────────────────────────────────────────────────────
# CSV flatten
# ───────────────────────────────────────────────────────────────────────────────
def export_decision_makers(charts_dir:Path, csv_path:Path, threshold:float=0.5):
rows=[]
for p in charts_dir.glob("org_chart_*.json"):
data=json.loads(p.read_text())
comp = p.stem.split("org_chart_")[1]
for n in data.get("nodes",[]):
if n.get("decision_score",0)>=threshold:
rows.append(dict(
company=comp,
person=n["name"],
title=n["title"],
decision_score=n["decision_score"],
profile_url=n["id"]
))
pd.DataFrame(rows).to_csv(csv_path,index=False)
# ───────────────────────────────────────────────────────────────────────────────
# HTML rendering
# ───────────────────────────────────────────────────────────────────────────────
def render_html(out:Path, template_dir:Path):
# From template folder cp graph_view.html and ai.js in out folder
import shutil
shutil.copy(template_dir/"graph_view_template.html", out / "graph_view.html")
shutil.copy(template_dir/"ai.js", out)
# ───────────────────────────────────────────────────────────────────────────────
# Main async pipeline
# ───────────────────────────────────────────────────────────────────────────────
async def run(opts):
# ── silence SDK noise ──────────────────────────────────────────────────────
# for noisy in ("openai", "httpx", "httpcore"):
# lg = logging.getLogger(noisy)
# lg.setLevel(logging.WARNING) # or ERROR if you want total silence
# lg.propagate = False # optional: stop them reaching root
# ────────────── logging bootstrap ──────────────
console = Console()
# logging.basicConfig(
# level="INFO",
# format="%(message)s",
# handlers=[RichHandler(console=console, markup=True, rich_tracebacks=True)],
# )
in_dir = BASE_DIR / Path(opts.in_dir)
out_dir = BASE_DIR / Path(opts.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
companies = load_jsonl(in_dir/"companies.jsonl")
people = load_jsonl(in_dir/"people.jsonl")
console.print(f"[bold cyan]Loaded[/] {len(companies)} companies, {len(people)} people")
console.print("[bold]⇢[/] Embedding company descriptions…")
embeds = embed_descriptions(companies, opts.embed_model, opts)
console.print("[bold]⇢[/] Building similarity graph")
company_graph = build_company_graph(companies, embeds, opts.top_k)
dump_json(company_graph, out_dir/"company_graph.json")
# Filter companies that need processing
to_process = []
for comp in companies:
handle = comp["handle"].strip("/").replace("/","_")
out_file = out_dir/f"org_chart_{handle}.json"
if out_file.exists():
console.print(f"[green]✓[/] Skipping existing {comp['name']}")
continue
to_process.append(comp)
if not to_process:
console.print("[yellow]All companies already processed[/]")
else:
workers = getattr(opts, 'workers', 1)
parallel = workers > 1
console.print(f"[bold]⇢[/] Inferring org-charts via LLM {f'(parallel={workers} workers)' if parallel else ''}")
with Progress(
SpinnerColumn(),
BarColumn(),
TextColumn("[progress.description]{task.description}"),
TimeElapsedColumn(),
console=console,
) as progress:
task = progress.add_task("Org charts", total=len(to_process))
async def process_one(comp):
handle = comp["handle"].strip("/").replace("/","_")
persons = [p for p in people if p["company_handle"].strip("/") == comp["handle"].strip("/")]
chart = await infer_org_chart_llm(
comp, persons,
llm_provider=opts.llm_provider,
api_key=opts.llm_api_key or None,
max_tokens=opts.max_llm_tokens,
temperature=opts.llm_temperature,
stub=opts.stub or False,
base_url=opts.llm_base_url or None
)
chart["meta"]["company"] = comp["name"]
# Save the result immediately
dump_json(chart, out_dir/f"org_chart_{handle}.json")
progress.update(task, advance=1, description=f"{comp['name']} ({len(persons)} ppl)")
# Create tasks for all companies
tasks = [process_one(comp) for comp in to_process]
# Process in batches based on worker count
semaphore = asyncio.Semaphore(workers)
async def bounded_process(coro):
async with semaphore:
return await coro
# Run with concurrency control
await asyncio.gather(*(bounded_process(task) for task in tasks))
console.print("[bold]⇢[/] Flattening decision-makers CSV")
export_decision_makers(out_dir, out_dir/"decision_makers.csv")
render_html(out_dir, template_dir=BASE_DIR/"templates")
console.print(f"[bold green]✓[/] Stage-2 artefacts written to {out_dir}")
# ───────────────────────────────────────────────────────────────────────────────
# CLI
# ───────────────────────────────────────────────────────────────────────────────
def build_arg_parser():
p = argparse.ArgumentParser(description="Build graphs & visualisation from Stage-1 output")
p.add_argument("--in", dest="in_dir", required=False, help="Stage-1 output dir", default=".")
p.add_argument("--out", dest="out_dir", required=False, help="Destination dir", default=".")
p.add_argument("--embed-model", default="all-MiniLM-L6-v2")
p.add_argument("--top-k", type=int, default=10, help="Top-k neighbours per company")
p.add_argument("--llm-provider", default="openai/gpt-4.1",
help="LLM model to use in format 'provider/model_name' (e.g., 'anthropic/claude-3')")
p.add_argument("--llm-api-key", help="API key for LLM provider (defaults to env vars)")
p.add_argument("--llm-base-url", help="Base URL for LLM API endpoint")
p.add_argument("--max-llm-tokens", type=int, default=8024)
p.add_argument("--llm-temperature", type=float, default=1.0)
p.add_argument("--stub", action="store_true", help="Skip OpenAI call and generate tiny fake org charts")
p.add_argument("--workers", type=int, default=4, help="Number of parallel workers for LLM inference")
return p
def main():
dbg = dev_defaults()
opts = dbg if True else build_arg_parser().parse_args()
# opts = build_arg_parser().parse_args()
asyncio.run(run(opts))
if __name__ == "__main__":
main()

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{"handle": "https://www.linkedin.com/company/healthpartnersng/", "name": "Health Partners HMO", "descriptor": "Hospitals and Health Care • Ikoyi, LAGOS", "about": "Healthpartners Ltd is a leading HMO in Nigeria providing affordablehealthinsuranceandhealthmanagementservices for companies and individuals in Nigeria. We have several individual and group plans that meets yourhealthmanagementneeds. Call us now at 0807-460-9165, 0807-714-0759 or email...", "followers": null, "people_url": "https://www.linkedin.com/company/healthpartnersng/people/", "captured_at": "2025-04-29T10:46:08Z"}
{"handle": "https://www.linkedin.com/company/health-insurance-management-services-organization/", "name": "Health & Insurance Management Services Organization", "descriptor": "Non-profit Organizations • Mbeya", "about": "Health&InsuranceManagementServices Organization (HIMSO) was established and registered in 2012 as a Non- Government Organization (NGO) with the aim...", "followers": 35, "people_url": "https://www.linkedin.com/company/health-insurance-management-services-organization/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/national-health-insurance-management-authority/", "name": "National Health Insurance Management Authority", "descriptor": "Insurance • Lusaka, Lusaka", "about": "The NationalHealthInsuranceManagementAuthority (NHIMA) is established pursuant to section 4 of the NationalHealthInsurance(NHI) Act No. 2 of 2018. The compulsory NationalHealthInsurancescheme seeks to provide for a sound and reliable healthcare financing for Zambian households and the entirehealthsector...", "followers": null, "people_url": "https://www.linkedin.com/company/national-health-insurance-management-authority/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/health-alliance-plan/", "name": "Health Alliance Plan", "descriptor": "Hospitals and Health Care • Detroit, MI", "about": "...organizations to enhance the lives of those we touch. We offer six distincthealthinsurancelines: • Group Insured Commercial • Individual • Medicare • Medicaid • Self-Funded • Network Leasing HAP also provides: • Award-winning wellness programs • Community outreach • Digitalhealthtools • Diseasemanagement...", "followers": null, "people_url": "https://www.linkedin.com/company/health-alliance-plan/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/insurance-recruiting-solutions/", "name": "Insurance Recruiting Solutions", "descriptor": "Insurance • Waukee, Iowa", "about": "InsuranceRecruiting Solutions provides staffing and recruiting services exclusively to theinsuranceindustry. We are committed to providing highly personalized recruiting services, tailored to each candidate and employer. With years ofinsuranceindustry experience, we speak your language. As a leading national...", "followers": null, "people_url": "https://www.linkedin.com/company/insurance-recruiting-solutions/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/healthplanofsanmateo/", "name": "Health Plan of San Mateo (HPSM)", "descriptor": "Hospitals and Health Care • South San Francisco, California", "about": "TheHealthPlan of San Mateo (HPSM) is a local non-profithealthcare plan that offershealthcoverage and a provider network to San Mateo County's under-insured population. We currently serve more than 145,000 County residents.", "followers": null, "people_url": "https://www.linkedin.com/company/healthplanofsanmateo/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/insurance-management-group_2/", "name": "Insurance Management Group", "descriptor": "Insurance • Marion, Indiana", "about": "InsuranceManagementGroup is an all-riskinsuranceagency with over 140 years of experience, specializing in Home, Auto, BusinessInsurance, Individual Life &Health, and Employee Benefits. We represent highly rated and financially soundinsurancecarriers, to ensure that our clients are getting the best coverage...", "followers": null, "people_url": "https://www.linkedin.com/company/insurance-management-group_2/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/carecard-health-insurance-management-co/", "name": "CareCard Health Insurance Management Co", "descriptor": "Insurance • Damascus", "about": "CareCard offers Business Process Outsourcing (BPO) services toInsurance, Self Funded and Retireehealthplan market. CareCard provides operational outsourcing...", "followers": 187, "people_url": "https://www.linkedin.com/company/carecard-health-insurance-management-co/people/", "captured_at": "2025-04-29T13:15:04Z"}
{"handle": "https://www.linkedin.com/company/healthcluster/", "name": "Health Cluster", "descriptor": "Technology, Information and Internet • Dubai", "about": "..., knowledge and interaction. The company has solutions and products inHealthTech, eHealth, DigitalHealth, Revenue CycleManagement RCM Solutions, AI & ML, Internet...", "followers": null, "people_url": "https://www.linkedin.com/company/healthcluster/people/", "captured_at": "2025-04-29T13:15:04Z"}

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@@ -0,0 +1,108 @@
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{"profile_url": null, "name": "Michael Akpoarebe-Isaac", "headline": "Chief Operating officer, Health Partners HMO", "followers": null, "connection_degree": "· 3rd", "avatar_url": "https://media.licdn.com/dms/image/v2/C4E03AQE7KNFaLMyqYg/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1602714385413?e=1751500800&v=beta&t=In5GaREqoXtO3sPCx9ZJJBwIPY4008ii13RPRl0w0Fw", "company_handle": "https://www.linkedin.com/company/healthpartnersng/", "captured_at": "2025-04-30T07:45:19+00:00Z"}
{"profile_url": null, "name": "LinkedIn Member", "headline": "REGISTERED NURSE/CLAIMS SUPERVISOR/HEALTH EDUCATOR/ CASE MANAGER/ Lekki.", "followers": null, "connection_degree": null, "avatar_url": "https://media.licdn.com/dms/image/v2/D4E35AQEEqf5i5pD76g/profile-framedphoto-shrink_100_100/profile-framedphoto-shrink_100_100/0/1724219552412?e=1746604800&v=beta&t=h0kqmp2KnpqQxsCCwyy7NpA8CAkSQ6qgbsZ0p0H7mXM", "company_handle": "https://www.linkedin.com/company/healthpartnersng/", "captured_at": "2025-04-30T07:45:19+00:00Z"}
{"profile_url": null, "name": "LinkedIn Member", "headline": "Data Analyst|Dedicated Retention Officer Boosting Customer Loyalty| Business Developer/ Event planner", "followers": null, "connection_degree": null, "avatar_url": "https://media.licdn.com/dms/image/v2/D4D35AQHIgeS1H7w65w/profile-framedphoto-shrink_100_100/B4DZV1QfcCGcAk-/0/1741429012517?e=1746604800&v=beta&t=zZi8WjnLpDrQD271jAId2mnfld_hO538QrN1-q2G4Zw", "company_handle": "https://www.linkedin.com/company/healthpartnersng/", "captured_at": "2025-04-30T07:45:19+00:00Z"}
{"profile_url": null, "name": "LinkedIn Member", "headline": "Former Group managing director at Health Partners Ltd", "followers": null, "connection_degree": null, "avatar_url": "https://media.licdn.com/dms/image/v2/C4E03AQHPQPvIQbPQPg/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1583328612508?e=1751500800&v=beta&t=LpynArccJCWrdWMSBvYLH4SI5G-xae7ECoWUUAl_CeU", "company_handle": "https://www.linkedin.com/company/healthpartnersng/", "captured_at": "2025-04-30T07:45:19+00:00Z"}
{"profile_url": null, "name": "LinkedIn Member", "headline": "HEAD, FINCON, @ HEALTH PARTNERS (HMO) LTD", "followers": null, "connection_degree": null, "avatar_url": "https://media.licdn.com/dms/image/v2/C4D03AQG8XOvnazEibQ/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1518882054975?e=1751500800&v=beta&t=5gT6GAWGTqYfpvkjOk0ArvV73I_KspkWXgoG-VhoStg", "company_handle": "https://www.linkedin.com/company/healthpartnersng/", "captured_at": "2025-04-30T07:45:19+00:00Z"}

View File

@@ -0,0 +1,51 @@
{
"name": "LinkedIn Company Search Result Card",
"baseSelector": "div[data-chameleon-result-urn][data-view-name=\"search-entity-result-universal-template\"]",
"baseFields": [
{
"name": "chameleon_result_urn",
"type": "attribute",
"attribute": "data-chameleon-result-urn"
},
{
"name": "view_name",
"type": "attribute",
"attribute": "data-view-name"
}
],
"fields": [
{
"name": "handle",
"selector": "div.mb1 div.display-flex span a[data-test-app-aware-link]",
"type": "attribute",
"attribute": "href"
},
{
"name": "profile_image",
"selector": "div.ivm-image-view-model img",
"type": "attribute",
"attribute": "src"
},
{
"name": "name",
"selector": "div.mb1 div.display-flex span a[data-test-app-aware-link]",
"type": "text"
},
{
"name": "descriptor",
"selector": "div.mb1 > div[class*=\"t-14 t-black\"]",
"type": "text"
},
{
"name": "about",
"selector": "p.entity-result__summary--2-lines",
"type": "text"
},
{
"name": "followers",
"selector": "div.mb1 > div:nth-of-type(3)",
"type": "regex",
"pattern": "(\\d+[KM]?) followers"
}
]
}

View File

@@ -0,0 +1,41 @@
{
"name": "LinkedIn People Profile Card",
"baseSelector": "li.org-people-profile-card__profile-card-spacing",
"baseFields": [],
"fields": [
{
"name": "profile_url",
"selector": "div.artdeco-entity-lockup__title a[data-test-app-aware-link]",
"type": "attribute",
"attribute": "href"
},
{
"name": "avatar_url",
"selector": "div.artdeco-entity-lockup__image img",
"type": "attribute",
"attribute": "src"
},
{
"name": "name",
"selector": "div.artdeco-entity-lockup__title a div.lt-line-clamp--single-line",
"type": "text"
},
{
"name": "headline",
"selector": "div.artdeco-entity-lockup__subtitle div.lt-line-clamp--multi-line",
"type": "text"
},
{
"name": "followers",
"selector": "span.text-align-center span.lt-line-clamp--multi-line",
"type": "regex",
"pattern": "(\\d+)"
},
{
"name": "connection_degree",
"selector": "span.artdeco-entity-lockup__degree",
"type": "regex",
"pattern": "(\\d+\\w+)"
}
]
}

View File

@@ -0,0 +1,138 @@
<li class="kZRArQqqhjjrHYceWaFbyEGWHRZbtqjTMawKA">
<!----><!---->
<div class="xAuWirHJDUTuhkfOpmJApZWziplUyPIc" data-chameleon-result-urn="urn:li:company:2095237"
data-view-name="search-entity-result-universal-template">
<div class="linked-area flex-1
cursor-pointer">
<div class="qMGLeKnJyQnibGOueKodvnfLgWpsuA">
<div class="cBPGFfFovHsbNhBFmECDIsPgMWmtMozOUfIAbs">
<div class="display-flex align-items-center">
<!---->
<a class="sDWEFrcVubKuUVGggeBOYqLlgYgPbojOc scale-down " aria-hidden="true" tabindex="-1"
href="https://www.linkedin.com/company/health-insurance/" data-test-app-aware-link="">
<div class="ivm-image-view-model ">
<div class="ivm-view-attr__img-wrapper
">
<!---->
<!----> <img width="48"
src="https://media.licdn.com/dms/image/v2/C560BAQEXIoLSJbShlw/company-logo_100_100/company-logo_100_100/0/1662748332921/health_insurance_logo?e=1753920000&amp;v=beta&amp;t=p2ZNMYNsC9KSlp-sIqMYuc88avBTjKF4CqDobq1Xr2M"
loading="lazy" height="48" alt="Health Insurance" id="ember28"
class="ivm-view-attr__img--centered EntityPhoto-square-3 evi-image lazy-image ember-view">
</div>
</div>
</a>
</div>
</div>
<div
class="BNxZPngZfeRnDrIUbICgBZvQjRvMAUnwCHuDrmRg yNRlrJOHDflDBnYPLbVmiAkUsCUZKUznmAc pt3 pb3 t-12 t-black--light">
<div class="mb1">
<div class="t-roman t-sans">
<div class="display-flex">
<span
class="kmApjJVnFerynwITxTBSCqzqgoHwVfkiA HHGiVqODTCkszDUDWwPGPJGUPfAeRpygAKwwLePrQ ">
<span class="OjTMoZLoiuspGuWWptwqxZRcMcHZBoSDxfig
t-16">
<a class="sDWEFrcVubKuUVGggeBOYqLlgYgPbojOc "
href="https://www.linkedin.com/company/health-insurance/"
data-test-app-aware-link="">
<!---->Health Insurance<!---->
<!----> </a>
<!----> </span>
</span>
<!---->
</div>
</div>
<div class="kFTZPhxHBbvnnRxiRPmTxafKGLUNSiaeInag
t-14 t-black t-normal">
<!---->Insurance • Cardiff, CA<!---->
</div>
<div class="FlWUwyrEUZpkVCgzGTDwUHTLntfZNseavlY
t-14 t-normal">
<!---->3K followers<!---->
</div>
</div>
<!---->
<p class="JBUEKeXhPyClEtYwdsASPYsZsCkTvUBqsDUs
entity-result__summary--2-lines
t-12 t-black--light
">
<!---->Your<span class="white-space-pre"> </span><strong><!---->health<!----></strong><span
class="white-space-pre"> </span><!----><!----><strong><!---->insurance<!----></strong><span
class="white-space-pre"> </span>expert for all stages of your life; Medicare, Individuals,
Families, Small Groups, CoveredCA.<!---->
</p>
<!---->
</div>
<div class="JZcKRppsWfaxfMaqtvfVwEeAtzNwryBOMdo yNRlrJOHDflDBnYPLbVmiAkUsCUZKUznmAc">
<!---->
<div>
<button aria-label="Follow Health Insurance" id="ember49"
class="artdeco-button artdeco-button--2 artdeco-button--secondary ember-view"
type="button"><!---->
<span class="artdeco-button__text">
Follow
</span></button>
<!---->
<!---->
</div>
</div>
</div>
</div>
</div>
</li>

View File

@@ -0,0 +1,93 @@
<li class="grid grid__col--lg-8 block org-people-profile-card__profile-card-spacing">
<div>
<section class="artdeco-card full-width IxXiAcHfbZpayHVZUYdQwfYOkMbOirmr">
<!---->
<img width="210" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
ariarole="presentation" loading="lazy" height="210" alt="" id="ember102"
class="evi-image lazy-image ghost-default ember-view org-people-profile-card__cover-photo org-people-profile-card__cover-photo--people">
<div class="org-people-profile-card__profile-info">
<div id="ember103"
class="artdeco-entity-lockup artdeco-entity-lockup--stacked-center artdeco-entity-lockup--size-7 ember-view">
<div id="ember104"
class="artdeco-entity-lockup__image artdeco-entity-lockup__image--type-circle ember-view"
type="circle">
<a class="sDWEFrcVubKuUVGggeBOYqLlgYgPbojOc " id="org-people-profile-card__profile-image-0"
href="https://www.linkedin.com/in/ericweberhcbd?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAABVh2MBFoyTaAxDqYQQcW8oGxVsqsKioHw"
data-test-app-aware-link="">
<img width="104"
src="https://media.licdn.com/dms/image/v2/C4D03AQHNP9KoXtSrkg/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1573501774845?e=1753920000&amp;v=beta&amp;t=JYsY56biGUmDzbYj2ORZMcd1dSm2IRWCA-IM3KNFLw8"
loading="lazy" height="104" alt="Eric Weber" id="ember105"
class="evi-image lazy-image ember-view">
</a>
</div>
<div id="ember106" class="artdeco-entity-lockup__content ember-view">
<div id="ember107" class="artdeco-entity-lockup__title ember-view">
<a class="sDWEFrcVubKuUVGggeBOYqLlgYgPbojOc link-without-visited-state"
aria-label="View Eric Weber’s profile"
href="https://www.linkedin.com/in/ericweberhcbd?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAABVh2MBFoyTaAxDqYQQcW8oGxVsqsKioHw"
data-test-app-aware-link="">
<div id="ember109" class="ember-view lt-line-clamp lt-line-clamp--single-line rMKrzkehlCEvJWoQjDQJFaHmBFAYQLMGrNY
t-black" style="">
Eric Weber
<!---->
</div>
</a>
</div>
<div id="ember110" class="artdeco-entity-lockup__badge ember-view"> <span class="a11y-text">3rd+
degree connection</span>
<span class="artdeco-entity-lockup__degree" aria-hidden="true">
·&nbsp;3rd
</span>
<!----><!---->
</div>
<div id="ember111" class="artdeco-entity-lockup__subtitle ember-view">
<div class="t-14 t-black--light t-normal">
<div id="ember113" class="ember-view lt-line-clamp lt-line-clamp--multi-line"
style="-webkit-line-clamp: 2">
HIPN Executive Editor | Healthcare BizDev CEO — Health Insurance Plan News.
<!---->
</div>
</div>
</div>
<div id="ember114" class="artdeco-entity-lockup__caption ember-view"></div>
</div>
</div>
<span class="text-align-center">
<span id="ember116"
class="ember-view lt-line-clamp lt-line-clamp--multi-line t-12 t-black--light mt2"
style="-webkit-line-clamp: 3">
10K followers
<!----> </span>
</span>
</div>
<footer class="ph3 pb3">
<button aria-label="Follow Eric Weber" id="ember117"
class="artdeco-button artdeco-button--2 artdeco-button--secondary ember-view full-width"
type="button"><!---->
<span class="artdeco-button__text">
Follow
</span></button>
</footer>
</section>
</div>
</li>

View File

@@ -0,0 +1,50 @@
// ==== File: ai.js ====
class ApiHandler {
constructor(apiKey = null) {
this.apiKey = apiKey || localStorage.getItem("openai_api_key") || "";
console.log("ApiHandler ready");
}
setApiKey(k) {
this.apiKey = k.trim();
if (this.apiKey) localStorage.setItem("openai_api_key", this.apiKey);
}
async *chatStream(messages, {model = "gpt-4o", temperature = 0.7} = {}) {
if (!this.apiKey) throw new Error("OpenAI API key missing");
const payload = {model, messages, stream: true, max_tokens: 1024};
const controller = new AbortController();
const res = await fetch("https://api.openai.com/v1/chat/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
},
body: JSON.stringify(payload),
signal: controller.signal,
});
if (!res.ok) throw new Error(`OpenAI: ${res.statusText}`);
const reader = res.body.getReader();
const dec = new TextDecoder();
let buf = "";
while (true) {
const {done, value} = await reader.read();
if (done) break;
buf += dec.decode(value, {stream: true});
for (const line of buf.split("\n")) {
if (!line.startsWith("data: ")) continue;
if (line.includes("[DONE]")) return;
const json = JSON.parse(line.slice(6));
const delta = json.choices?.[0]?.delta?.content;
if (delta) yield delta;
}
buf = buf.endsWith("\n") ? "" : buf; // keep partial line
}
}
}
window.API = new ApiHandler();

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51
docs/codebase/browser.md Normal file
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@@ -0,0 +1,51 @@
### browser_manager.py
| Function | What it does |
|---|---|
| `ManagedBrowser.build_browser_flags` | Returns baseline Chromium CLI flags, disables GPU and sandbox, plugs locale, timezone, stealth tweaks, and any extras from `BrowserConfig`. |
| `ManagedBrowser.__init__` | Stores config and logger, creates temp dir, preps internal state. |
| `ManagedBrowser.start` | Spawns or connects to the Chromium process, returns its CDP endpoint plus the `subprocess.Popen` handle. |
| `ManagedBrowser._initial_startup_check` | Pings the CDP endpoint once to be sure the browser is alive, raises if not. |
| `ManagedBrowser._monitor_browser_process` | Async-loops on the subprocess, logs exits or crashes, restarts if policy allows. |
| `ManagedBrowser._get_browser_path_WIP` | Old helper that maps OS + browser type to an executable path. |
| `ManagedBrowser._get_browser_path` | Current helper, checks env vars, Playwright cache, and OS defaults for the real executable. |
| `ManagedBrowser._get_browser_args` | Builds the final CLI arg list by merging user flags, stealth flags, and defaults. |
| `ManagedBrowser.cleanup` | Terminates the browser, stops monitors, deletes the temp dir. |
| `ManagedBrowser.create_profile` | Opens a visible browser so a human can log in, then zips the resulting user-data-dir to `~/.crawl4ai/profiles/<name>`. |
| `ManagedBrowser.list_profiles` | Thin wrapper, now forwarded to `BrowserProfiler.list_profiles()`. |
| `ManagedBrowser.delete_profile` | Thin wrapper, now forwarded to `BrowserProfiler.delete_profile()`. |
| `BrowserManager.__init__` | Holds the global Playwright instance, browser handle, config signature cache, session map, and logger. |
| `BrowserManager.start` | Boots the underlying `ManagedBrowser`, then spins up the default Playwright browser context with stealth patches. |
| `BrowserManager._build_browser_args` | Translates `CrawlerRunConfig` (proxy, UA, timezone, headless flag, etc.) into Playwright `launch_args`. |
| `BrowserManager.setup_context` | Applies locale, geolocation, permissions, cookies, and UA overrides on a fresh context. |
| `BrowserManager.create_browser_context` | Internal helper that actually calls `browser.new_context(**options)` after running `setup_context`. |
| `BrowserManager._make_config_signature` | Hashes the non-ephemeral parts of `CrawlerRunConfig` so contexts can be reused safely. |
| `BrowserManager.get_page` | Returns a ready `Page` for a given session id, reusing an existing one or creating a new context/page, injects helper scripts, updates `last_used`. |
| `BrowserManager.kill_session` | Force-closes a context/page for a session and removes it from the session map. |
| `BrowserManager._cleanup_expired_sessions` | Periodic sweep that drops sessions idle longer than `ttl_seconds`. |
| `BrowserManager.close` | Gracefully shuts down all contexts, the browser, Playwright, and background tasks. |
---
### browser_profiler.py
| Function | What it does |
|---|---|
| `BrowserProfiler.__init__` | Sets up profile folder paths, async logger, and signal handlers. |
| `BrowserProfiler.create_profile` | Launches a visible browser with a new user-data-dir for manual login, on exit compresses and stores it as a named profile. |
| `BrowserProfiler.cleanup_handler` | General SIGTERM/SIGINT cleanup wrapper that kills child processes. |
| `BrowserProfiler.sigint_handler` | Handles Ctrl-C during an interactive session, makes sure the browser shuts down cleanly. |
| `BrowserProfiler.listen_for_quit_command` | Async REPL that exits when the user types `q`. |
| `BrowserProfiler.list_profiles` | Enumerates `~/.crawl4ai/profiles`, prints profile name, browser type, size, and last modified. |
| `BrowserProfiler.get_profile_path` | Returns the absolute path of a profile given its name, or `None` if missing. |
| `BrowserProfiler.delete_profile` | Removes a profile folder or a direct path from disk, with optional confirmation prompt. |
| `BrowserProfiler.interactive_manager` | Text UI loop for listing, creating, deleting, or launching profiles. |
| `BrowserProfiler.launch_standalone_browser` | Starts a non-headless Chromium with remote debugging enabled and keeps it alive for manual tests. |
| `BrowserProfiler.get_cdp_json` | Pulls `/json/version` from a CDP endpoint and returns the parsed JSON. |
| `BrowserProfiler.launch_builtin_browser` | Spawns a headless Chromium in the background, saves `{wsEndpoint, pid, started_at}` to `~/.crawl4ai/builtin_browser.json`. |
| `BrowserProfiler.get_builtin_browser_info` | Reads that JSON file, verifies the PID, and returns browser status info. |
| `BrowserProfiler._is_browser_running` | Cross-platform helper that checks if a PID is still alive. |
| `BrowserProfiler.kill_builtin_browser` | Terminates the background builtin browser and removes its status file. |
| `BrowserProfiler.get_builtin_browser_status` | Returns `{running: bool, wsEndpoint, pid, started_at}` for quick health checks. |
Let me know what you want to tweak or dive into next.

40
docs/codebase/cli.md Normal file
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@@ -0,0 +1,40 @@
### `cli.py` command surface
| Command | Inputs / flags | What it does |
|---|---|---|
| **profiles** | *(none)* | Opens the interactive profile manager, lets you list, create, delete saved browser profiles that live in `~/.crawl4ai/profiles`. |
| **browser status** | | Prints whether the always-on *builtin* browser is running, shows its CDP URL, PID, start time. |
| **browser stop** | | Kills the builtin browser and deletes its status file. |
| **browser view** | `--url, -u` URL *(optional)* | Pops a visible window of the builtin browser, navigates to `URL` or `about:blank`. |
| **config list** | | Dumps every global setting, showing current value, default, and description. |
| **config get** | `key` | Prints the value of a single setting, falls back to default if unset. |
| **config set** | `key value` | Persists a new value in the global config (stored under `~/.crawl4ai/config.yml`). |
| **examples** | | Just spits out real-world CLI usage samples. |
| **crawl** | `url` *(positional)*<br>`--browser-config,-B` path<br>`--crawler-config,-C` path<br>`--filter-config,-f` path<br>`--extraction-config,-e` path<br>`--json-extract,-j` [desc]\*<br>`--schema,-s` path<br>`--browser,-b` k=v list<br>`--crawler,-c` k=v list<br>`--output,-o` all,json,markdown,md,markdown-fit,md-fit *(default all)*<br>`--output-file,-O` path<br>`--bypass-cache,-b` *(flag, default true — note flag reuse)*<br>`--question,-q` str<br>`--verbose,-v` *(flag)*<br>`--profile,-p` profile-name | One-shot crawl + extraction. Builds `BrowserConfig` and `CrawlerRunConfig` from inline flags or separate YAML/JSON files, runs `AsyncWebCrawler.run()`, can route through a named saved profile and pipe the result to stdout or a file. |
| **(default)** | Same flags as **crawl**, plus `--example` | Shortcut so you can type just `crwl https://site.com`. When first arg is not a known sub-command, it falls through to *crawl*. |
\* `--json-extract/-j` with no value turns on LLM-based JSON extraction using an auto schema, supplying a string lets you prompt-engineer the field descriptions.
> Quick mental model
> `profiles` = manage identities,
> `browser ...` = control long-running headless Chrome that all crawls can piggy-back on,
> `crawl` = do the actual work,
> `config` = tweak global defaults,
> everything else is sugar.
### Quick-fire “profile” usage cheatsheet
| Scenario | Command (copy-paste ready) | Notes |
|---|---|---|
| **Launch interactive Profile Manager UI** | `crwl profiles` | Opens TUI with options: 1 List, 2 Create, 3 Delete, 4 Use-to-crawl, 5 Exit. |
| **Create a fresh profile** | `crwl profiles` → choose **2** → name it → browser opens → log in → press **q** in terminal | Saves to `~/.crawl4ai/profiles/<name>`. |
| **List saved profiles** | `crwl profiles` → choose **1** | Shows name, browser type, size, last-modified. |
| **Delete a profile** | `crwl profiles` → choose **3** → pick the profile index → confirm | Removes the folder. |
| **Crawl with a profile (default alias)** | `crwl https://site.com/dashboard -p my-profile` | Keeps login cookies, sets `use_managed_browser=true` under the hood. |
| **Crawl + verbose JSON output** | `crwl https://site.com -p my-profile -o json -v` | Any other `crawl` flags work the same. |
| **Crawl with extra browser tweaks** | `crwl https://site.com -p my-profile -b "headless=true,viewport_width=1680"` | CLI overrides go on top of the profile. |
| **Same but via explicit sub-command** | `crwl crawl https://site.com -p my-profile` | Identical to default alias. |
| **Use profile from inside Profile Manager** | `crwl profiles` → choose **4** → pick profile → enter URL → follow prompts | Handy when demo-ing to non-CLI folks. |
| **One-off crawl with a profile folder path (no name lookup)** | `crwl https://site.com -b "user_data_dir=$HOME/.crawl4ai/profiles/my-profile,use_managed_browser=true"` | Bypasses registry, useful for CI scripts. |
| **Launch a dev browser on CDP port with the same identity** | `crwl cdp -d $HOME/.crawl4ai/profiles/my-profile -P 9223` | Lets Puppeteer/Playwright attach for debugging. |

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# Builtin Browser in Crawl4AI
This document explains the builtin browser feature in Crawl4AI and how to use it effectively.
## What is the Builtin Browser?
The builtin browser is a persistent Chrome instance that Crawl4AI manages for you. It runs in the background and can be used by multiple crawling operations, eliminating the need to start and stop browsers for each crawl.
Benefits include:
- **Faster startup times** - The browser is already running, so your scripts start faster
- **Shared resources** - All your crawling scripts can use the same browser instance
- **Simplified management** - No need to worry about CDP URLs or browser processes
- **Persistent cookies and sessions** - Browser state persists between script runs
- **Less resource usage** - Only one browser instance for multiple scripts
## Using the Builtin Browser
### In Python Code
Using the builtin browser in your code is simple:
```python
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
# Create browser config with builtin mode
browser_config = BrowserConfig(
browser_mode="builtin", # This is the key setting!
headless=True # Can be headless or not
)
# Create the crawler
crawler = AsyncWebCrawler(config=browser_config)
# Use it - no need to explicitly start()
result = await crawler.arun("https://example.com")
```
Key points:
1. Set `browser_mode="builtin"` in your BrowserConfig
2. No need for explicit `start()` call - the crawler will automatically connect to the builtin browser
3. No need to use a context manager or call `close()` - the browser stays running
### Via CLI
The CLI provides commands to manage the builtin browser:
```bash
# Start the builtin browser
crwl browser start
# Check its status
crwl browser status
# Open a visible window to see what the browser is doing
crwl browser view --url https://example.com
# Stop it when no longer needed
crwl browser stop
# Restart with different settings
crwl browser restart --no-headless
```
When crawling via CLI, simply add the builtin browser mode:
```bash
crwl https://example.com -b "browser_mode=builtin"
```
## How It Works
1. When a crawler with `browser_mode="builtin"` is created:
- It checks if a builtin browser is already running
- If not, it automatically launches one
- It connects to the browser via CDP (Chrome DevTools Protocol)
2. The browser process continues running after your script exits
- This means it's ready for the next crawl
- You can manage it via the CLI commands
3. During installation, Crawl4AI attempts to create a builtin browser automatically
## Example
See the [builtin_browser_example.py](builtin_browser_example.py) file for a complete example.
Run it with:
```bash
python builtin_browser_example.py
```
## When to Use
The builtin browser is ideal for:
- Scripts that run frequently
- Development and testing workflows
- Applications that need to minimize startup time
- Systems where you want to manage browser instances centrally
You might not want to use it when:
- Running one-off scripts
- When you need different browser configurations for different tasks
- In environments where persistent processes are not allowed
## Troubleshooting
If you encounter issues:
1. Check the browser status:
```
crwl browser status
```
2. Try restarting it:
```
crwl browser restart
```
3. If problems persist, stop it and let Crawl4AI start a fresh one:
```
crwl browser stop
```

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@@ -5,7 +5,7 @@ prices, ratings, and other details using CSS selectors.
"""
from crawl4ai import AsyncWebCrawler
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
import json

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@@ -5,7 +5,7 @@ prices, ratings, and other details using CSS selectors.
"""
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
import json
from playwright.async_api import Page, BrowserContext

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@@ -5,7 +5,7 @@ prices, ratings, and other details using CSS selectors.
"""
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
import json

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import asyncio
import time
from crawl4ai.async_webcrawler import AsyncWebCrawler, CacheMode
from crawl4ai.async_configs import CrawlerRunConfig
from crawl4ai.async_dispatcher import MemoryAdaptiveDispatcher, RateLimiter
VERBOSE = False
async def crawl_sequential(urls):
config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, verbose=VERBOSE)
results = []
start_time = time.perf_counter()
async with AsyncWebCrawler() as crawler:
for url in urls:
result_container = await crawler.arun(url=url, config=config)
results.append(result_container[0])
total_time = time.perf_counter() - start_time
return total_time, results
async def crawl_parallel_dispatcher(urls):
config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, verbose=VERBOSE)
# Dispatcher with rate limiter enabled (default behavior)
dispatcher = MemoryAdaptiveDispatcher(
rate_limiter=RateLimiter(base_delay=(1.0, 3.0), max_delay=60.0, max_retries=3),
max_session_permit=50,
)
start_time = time.perf_counter()
async with AsyncWebCrawler() as crawler:
result_container = await crawler.arun_many(urls=urls, config=config, dispatcher=dispatcher)
results = []
if isinstance(result_container, list):
results = result_container
else:
async for res in result_container:
results.append(res)
total_time = time.perf_counter() - start_time
return total_time, results
async def crawl_parallel_no_rate_limit(urls):
config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, verbose=VERBOSE)
# Dispatcher with no rate limiter and a high session permit to avoid queuing
dispatcher = MemoryAdaptiveDispatcher(
rate_limiter=None,
max_session_permit=len(urls) # allow all URLs concurrently
)
start_time = time.perf_counter()
async with AsyncWebCrawler() as crawler:
result_container = await crawler.arun_many(urls=urls, config=config, dispatcher=dispatcher)
results = []
if isinstance(result_container, list):
results = result_container
else:
async for res in result_container:
results.append(res)
total_time = time.perf_counter() - start_time
return total_time, results
async def main():
urls = ["https://example.com"] * 100
print(f"Crawling {len(urls)} URLs sequentially...")
seq_time, seq_results = await crawl_sequential(urls)
print(f"Sequential crawling took: {seq_time:.2f} seconds\n")
print(f"Crawling {len(urls)} URLs in parallel using arun_many with dispatcher (with rate limit)...")
disp_time, disp_results = await crawl_parallel_dispatcher(urls)
print(f"Parallel (dispatcher with rate limiter) took: {disp_time:.2f} seconds\n")
print(f"Crawling {len(urls)} URLs in parallel using dispatcher with no rate limiter...")
no_rl_time, no_rl_results = await crawl_parallel_no_rate_limit(urls)
print(f"Parallel (dispatcher without rate limiter) took: {no_rl_time:.2f} seconds\n")
print("Crawl4ai - Crawling Comparison")
print("--------------------------------------------------------")
print(f"Sequential crawling took: {seq_time:.2f} seconds")
print(f"Parallel (dispatcher with rate limiter) took: {disp_time:.2f} seconds")
print(f"Parallel (dispatcher without rate limiter) took: {no_rl_time:.2f} seconds")
if __name__ == "__main__":
asyncio.run(main())

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<!DOCTYPE html>
<html>
<head>
<title>Append-Only Scroll (Traditional Infinite Scroll)</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 20px;
background-color: #f5f5f5;
}
h1 {
color: #333;
text-align: center;
}
.posts-container {
max-width: 800px;
margin: 0 auto;
background: white;
border: 1px solid #ddd;
border-radius: 5px;
padding: 20px;
}
.post {
background: #f9f9f9;
padding: 15px;
margin-bottom: 15px;
border-radius: 5px;
border: 1px solid #eee;
}
.post-title {
font-size: 18px;
font-weight: bold;
color: #2c3e50;
margin-bottom: 10px;
}
.post-content {
color: #555;
line-height: 1.6;
}
.loading {
text-align: center;
padding: 20px;
color: #888;
}
</style>
</head>
<body>
<h1>Traditional Infinite Scroll Demo</h1>
<p style="text-align: center; color: #666;">This appends new content without removing old content</p>
<div class="posts-container"></div>
<script>
// Traditional infinite scroll - APPENDS content
const container = document.querySelector('.posts-container');
const totalPosts = 200;
const postsPerPage = 20;
let loadedPosts = 0;
let isLoading = false;
// Generate fake post data
function generatePost(index) {
return {
id: index,
title: `Post Title #${index + 1}`,
content: `This is the content of post ${index + 1}. In traditional infinite scroll, new content is appended to existing content. The DOM keeps growing. Post ID: ${index}`
};
}
// Load more posts - APPENDS to existing content
function loadMorePosts() {
if (isLoading || loadedPosts >= totalPosts) return;
isLoading = true;
// Show loading indicator
const loadingDiv = document.createElement('div');
loadingDiv.className = 'loading';
loadingDiv.textContent = 'Loading more posts...';
container.appendChild(loadingDiv);
// Simulate network delay
setTimeout(() => {
// Remove loading indicator
container.removeChild(loadingDiv);
// Add new posts
const fragment = document.createDocumentFragment();
const endIndex = Math.min(loadedPosts + postsPerPage, totalPosts);
for (let i = loadedPosts; i < endIndex; i++) {
const post = generatePost(i);
const postElement = document.createElement('div');
postElement.className = 'post';
postElement.setAttribute('data-post-id', post.id);
postElement.innerHTML = `
<div class="post-title">${post.title}</div>
<div class="post-content">${post.content}</div>
`;
fragment.appendChild(postElement);
}
// APPEND new posts to existing ones
container.appendChild(fragment);
loadedPosts = endIndex;
isLoading = false;
console.log(`Loaded ${loadedPosts} of ${totalPosts} posts`);
}, 300);
}
// Initial load
loadMorePosts();
// Load more on scroll
window.addEventListener('scroll', () => {
const scrollBottom = window.innerHeight + window.scrollY;
const threshold = document.body.offsetHeight - 500;
if (scrollBottom >= threshold) {
loadMorePosts();
}
});
</script>
</body>
</html>

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<!DOCTYPE html>
<html>
<head>
<title>Instagram-like Grid Virtual Scroll</title>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;
margin: 0;
padding: 20px;
background-color: #fafafa;
}
h1 {
text-align: center;
color: #262626;
font-weight: 300;
}
.feed-container {
max-width: 935px;
margin: 0 auto;
height: 800px;
overflow-y: auto;
background: white;
border: 1px solid #dbdbdb;
border-radius: 3px;
}
.grid {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 28px;
padding: 28px;
}
.post {
aspect-ratio: 1;
background: #f0f0f0;
border-radius: 3px;
position: relative;
overflow: hidden;
cursor: pointer;
}
.post:hover .overlay {
opacity: 1;
}
.post img {
width: 100%;
height: 100%;
object-fit: cover;
}
.overlay {
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: rgba(0, 0, 0, 0.3);
display: flex;
align-items: center;
justify-content: center;
color: white;
font-size: 14px;
opacity: 0;
transition: opacity 0.2s;
}
.stats {
display: flex;
gap: 20px;
}
</style>
</head>
<body>
<h1>Instagram Grid Virtual Scroll</h1>
<p style="text-align: center; color: #8e8e8e;">Grid layout with virtual scrolling - only visible rows are rendered</p>
<div class="feed-container">
<div class="grid" id="grid"></div>
</div>
<script>
// Instagram-like grid virtual scroll
const grid = document.getElementById('grid');
const container = document.querySelector('.feed-container');
const totalPosts = 999; // Instagram style count
const postsPerRow = 3;
const rowsPerPage = 4; // 12 posts per page
const postsPerPage = postsPerRow * rowsPerPage;
let currentStartIndex = 0;
// Generate fake Instagram post data
const allPosts = [];
for (let i = 0; i < totalPosts; i++) {
allPosts.push({
id: i,
likes: Math.floor(Math.random() * 10000),
comments: Math.floor(Math.random() * 500),
imageNumber: (i % 10) + 1 // Cycle through 10 placeholder images
});
}
// Render grid - REPLACES content for performance
function renderGrid(startIndex) {
const posts = [];
const endIndex = Math.min(startIndex + postsPerPage, totalPosts);
for (let i = startIndex; i < endIndex; i++) {
const post = allPosts[i];
posts.push(`
<div class="post" data-post-id="${post.id}">
<img src="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='400' height='400'%3E%3Crect width='400' height='400' fill='%23${Math.floor(Math.random()*16777215).toString(16)}'/%3E%3Ctext x='50%25' y='50%25' text-anchor='middle' dy='.3em' font-family='Arial' font-size='48' fill='white'%3E${post.id + 1}%3C/text%3E%3C/svg%3E" alt="Post ${post.id + 1}">
<div class="overlay">
<div class="stats">
<span>❤️ ${post.likes.toLocaleString()}</span>
<span>💬 ${post.comments}</span>
</div>
</div>
</div>
`);
}
// REPLACE grid content (virtual scroll)
grid.innerHTML = posts.join('');
currentStartIndex = startIndex;
}
// Initial render
renderGrid(0);
// Handle scroll
let scrollTimeout;
container.addEventListener('scroll', () => {
clearTimeout(scrollTimeout);
scrollTimeout = setTimeout(() => {
const scrollTop = container.scrollTop;
const scrollHeight = container.scrollHeight;
const clientHeight = container.clientHeight;
// Calculate which "page" we should show
const scrollPercentage = scrollTop / (scrollHeight - clientHeight);
const targetIndex = Math.floor(scrollPercentage * (totalPosts - postsPerPage) / postsPerPage) * postsPerPage;
// When scrolled to bottom, show next page
if (scrollTop + clientHeight >= scrollHeight - 100) {
const nextIndex = currentStartIndex + postsPerPage;
if (nextIndex < totalPosts) {
renderGrid(nextIndex);
container.scrollTop = 100; // Reset scroll for continuous experience
}
}
}, 50);
});
</script>
</body>
</html>

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<!DOCTYPE html>
<html>
<head>
<title>News Feed with Mixed Scroll Behavior</title>
<style>
body {
font-family: Georgia, serif;
margin: 0;
padding: 20px;
background-color: #f8f8f8;
}
h1 {
text-align: center;
color: #1a1a1a;
font-size: 32px;
margin-bottom: 10px;
}
.description {
text-align: center;
color: #666;
margin-bottom: 20px;
}
#newsContainer {
max-width: 900px;
margin: 0 auto;
height: 700px;
overflow-y: auto;
background: white;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
padding: 20px;
}
.article {
margin-bottom: 30px;
padding-bottom: 30px;
border-bottom: 1px solid #e0e0e0;
}
.article:last-child {
border-bottom: none;
}
.article-header {
margin-bottom: 15px;
}
.category {
display: inline-block;
background: #ff6b6b;
color: white;
padding: 4px 12px;
font-size: 12px;
text-transform: uppercase;
border-radius: 3px;
margin-bottom: 10px;
}
.headline {
font-size: 24px;
font-weight: bold;
color: #1a1a1a;
margin: 10px 0;
line-height: 1.3;
}
.meta {
color: #888;
font-size: 14px;
margin-bottom: 15px;
}
.content {
font-size: 16px;
line-height: 1.8;
color: #333;
}
.featured {
background: #fff9e6;
padding: 20px;
border-radius: 5px;
margin-bottom: 30px;
}
.featured .category {
background: #ffa500;
}
</style>
</head>
<body>
<h1>📰 Dynamic News Feed</h1>
<p class="description">Mixed behavior: Featured articles stay, regular articles use virtual scroll</p>
<div id="newsContainer"></div>
<script>
const container = document.getElementById('newsContainer');
const totalArticles = 100;
const articlesPerPage = 5;
let currentRegularIndex = 0;
// Categories for variety
const categories = ['Politics', 'Technology', 'Business', 'Science', 'Sports', 'Entertainment'];
// Generate article data
const featuredArticles = [];
const regularArticles = [];
// 3 featured articles that always stay
for (let i = 0; i < 3; i++) {
featuredArticles.push({
id: `featured-${i}`,
category: 'Featured',
headline: `Breaking: Major Story ${i + 1} That Stays Visible`,
date: new Date().toLocaleDateString(),
content: `This is featured article ${i + 1}. Featured articles remain in the DOM and are not replaced during scrolling. They provide important persistent content.`
});
}
// Regular articles that get virtualized
for (let i = 0; i < totalArticles; i++) {
regularArticles.push({
id: `article-${i}`,
category: categories[i % categories.length],
headline: `${categories[i % categories.length]} News: Article ${i + 1} of ${totalArticles}`,
date: new Date(Date.now() - i * 86400000).toLocaleDateString(),
content: `This is regular article ${i + 1}. These articles are replaced as you scroll to maintain performance. Only a subset is shown at any time. Article ID: ${i}`
});
}
// Render articles - Featured stay, regular ones are replaced
function renderArticles(regularStartIndex) {
const html = [];
// Always show featured articles
featuredArticles.forEach(article => {
html.push(`
<div class="article featured" data-article-id="${article.id}">
<div class="article-header">
<span class="category">${article.category}</span>
<h2 class="headline">${article.headline}</h2>
<div class="meta">📅 ${article.date}</div>
</div>
<div class="content">${article.content}</div>
</div>
`);
});
// Add divider
html.push('<div style="text-align: center; color: #999; margin: 20px 0;">— Latest News —</div>');
// Show current page of regular articles (virtual scroll)
const endIndex = Math.min(regularStartIndex + articlesPerPage, totalArticles);
for (let i = regularStartIndex; i < endIndex; i++) {
const article = regularArticles[i];
html.push(`
<div class="article" data-article-id="${article.id}">
<div class="article-header">
<span class="category" style="background: ${getCategoryColor(article.category)}">${article.category}</span>
<h2 class="headline">${article.headline}</h2>
<div class="meta">📅 ${article.date}</div>
</div>
<div class="content">${article.content}</div>
</div>
`);
}
container.innerHTML = html.join('');
currentRegularIndex = regularStartIndex;
}
function getCategoryColor(category) {
const colors = {
'Politics': '#e74c3c',
'Technology': '#3498db',
'Business': '#2ecc71',
'Science': '#9b59b6',
'Sports': '#f39c12',
'Entertainment': '#e91e63'
};
return colors[category] || '#95a5a6';
}
// Initial render
renderArticles(0);
// Handle scroll
container.addEventListener('scroll', () => {
const scrollTop = container.scrollTop;
const scrollHeight = container.scrollHeight;
const clientHeight = container.clientHeight;
// When near bottom, load next page of regular articles
if (scrollTop + clientHeight >= scrollHeight - 200) {
const nextIndex = currentRegularIndex + articlesPerPage;
if (nextIndex < totalArticles) {
renderArticles(nextIndex);
// Scroll to where regular articles start
const regularStart = document.querySelector('.article:not(.featured)');
if (regularStart) {
container.scrollTop = regularStart.offsetTop - 100;
}
}
}
});
</script>
</body>
</html>

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<!DOCTYPE html>
<html>
<head>
<title>Twitter-like Virtual Scroll</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 20px;
background-color: #f0f2f5;
}
h1 {
color: #1da1f2;
text-align: center;
}
#timeline {
max-width: 600px;
margin: 0 auto;
height: 600px;
overflow-y: auto;
background: white;
border: 1px solid #e1e8ed;
border-radius: 10px;
}
.tweet {
padding: 15px;
border-bottom: 1px solid #e1e8ed;
min-height: 80px;
}
.tweet:hover {
background-color: #f7f9fa;
}
.author {
font-weight: bold;
color: #14171a;
margin-bottom: 5px;
}
.content {
color: #14171a;
line-height: 1.5;
}
.stats {
color: #657786;
font-size: 14px;
margin-top: 10px;
}
</style>
</head>
<body>
<h1>Virtual Scroll Demo - Twitter Style</h1>
<p style="text-align: center; color: #666;">This simulates Twitter's timeline where content is replaced as you scroll</p>
<div id="timeline"></div>
<script>
// Simulate Twitter-like virtual scrolling where DOM elements are replaced
const timeline = document.getElementById('timeline');
const totalTweets = 500;
const tweetsPerPage = 10;
let currentIndex = 0;
// Generate fake tweet data
const allTweets = [];
for (let i = 0; i < totalTweets; i++) {
allTweets.push({
id: i,
author: `User_${i + 1}`,
content: `This is tweet #${i + 1} of ${totalTweets}. Virtual scrolling replaces DOM elements to maintain performance. Unique content ID: ${i}`,
likes: Math.floor(Math.random() * 1000),
retweets: Math.floor(Math.random() * 500)
});
}
// Render tweets - REPLACES content
function renderTweets(startIndex) {
const tweets = [];
const endIndex = Math.min(startIndex + tweetsPerPage, totalTweets);
for (let i = startIndex; i < endIndex; i++) {
const tweet = allTweets[i];
tweets.push(`
<div class="tweet" data-tweet-id="${tweet.id}">
<div class="author">@${tweet.author}</div>
<div class="content">${tweet.content}</div>
<div class="stats">❤️ ${tweet.likes} | 🔁 ${tweet.retweets}</div>
</div>
`);
}
// REPLACE entire content (virtual scroll behavior)
timeline.innerHTML = tweets.join('');
currentIndex = startIndex;
}
// Initial render
renderTweets(0);
// Handle scroll
timeline.addEventListener('scroll', () => {
const scrollTop = timeline.scrollTop;
const scrollHeight = timeline.scrollHeight;
const clientHeight = timeline.clientHeight;
// When near bottom, load next page
if (scrollTop + clientHeight >= scrollHeight - 100) {
const nextIndex = currentIndex + tweetsPerPage;
if (nextIndex < totalTweets) {
renderTweets(nextIndex);
// Small scroll adjustment for continuous scrolling
timeline.scrollTop = 50;
}
}
});
</script>
</body>
</html>

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#!/usr/bin/env python3
"""
Builtin Browser Example
This example demonstrates how to use Crawl4AI's builtin browser feature,
which simplifies the browser management process. With builtin mode:
- No need to manually start or connect to a browser
- No need to manage CDP URLs or browser processes
- Automatically connects to an existing browser or launches one if needed
- Browser persists between script runs, reducing startup time
- No explicit cleanup or close() calls needed
The example also demonstrates "auto-starting" where you don't need to explicitly
call start() method on the crawler.
"""
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
import time
async def crawl_with_builtin_browser():
"""
Simple example of crawling with the builtin browser.
Key features:
1. browser_mode="builtin" in BrowserConfig
2. No explicit start() call needed
3. No explicit close() needed
"""
print("\n=== Crawl4AI Builtin Browser Example ===\n")
# Create a browser configuration with builtin mode
browser_config = BrowserConfig(
browser_mode="builtin", # This is the key setting!
headless=True # Can run headless for background operation
)
# Create crawler run configuration
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS, # Skip cache for this demo
screenshot=True, # Take a screenshot
verbose=True # Show verbose logging
)
# Create the crawler instance
# Note: We don't need to use "async with" context manager
crawler = AsyncWebCrawler(config=browser_config)
# Start crawling several URLs - no explicit start() needed!
# The crawler will automatically connect to the builtin browser
print("\n➡️ Crawling first URL...")
t0 = time.time()
result1 = await crawler.arun(
url="https://crawl4ai.com",
config=crawler_config
)
t1 = time.time()
print(f"✅ First URL crawled in {t1-t0:.2f} seconds")
print(f" Got {len(result1.markdown.raw_markdown)} characters of content")
print(f" Title: {result1.metadata.get('title', 'No title')}")
# Try another URL - the browser is already running, so this should be faster
print("\n➡️ Crawling second URL...")
t0 = time.time()
result2 = await crawler.arun(
url="https://example.com",
config=crawler_config
)
t1 = time.time()
print(f"✅ Second URL crawled in {t1-t0:.2f} seconds")
print(f" Got {len(result2.markdown.raw_markdown)} characters of content")
print(f" Title: {result2.metadata.get('title', 'No title')}")
# The builtin browser continues running in the background
# No need to explicitly close it
print("\n🔄 The builtin browser remains running for future use")
print(" You can use 'crwl browser status' to check its status")
print(" or 'crwl browser stop' to stop it when completely done")
async def main():
"""Run the example"""
await crawl_with_builtin_browser()
if __name__ == "__main__":
asyncio.run(main())

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# Amazon R2D2 Product Search Example
A real-world demonstration of Crawl4AI's multi-step crawling with LLM-generated automation scripts.
## 🎯 What This Example Shows
This example demonstrates advanced Crawl4AI features:
- **LLM-Generated Scripts**: Automatically create C4A-Script from HTML snippets
- **Multi-Step Crawling**: Navigate through multiple pages using session persistence
- **Structured Data Extraction**: Extract product data using JSON CSS schemas
- **Visual Automation**: Watch the browser perform the search (headless=False)
## 🚀 How It Works
### 1. **Script Generation Phase**
The example uses `C4ACompiler.generate_script()` to analyze Amazon's HTML and create:
- **Search Script**: Automates filling the search box and clicking search
- **Extraction Schema**: Defines how to extract product information
### 2. **Crawling Workflow**
```
Homepage → Execute Search Script → Extract Products → Save Results
```
All steps use the same `session_id` to maintain browser state.
### 3. **Data Extraction**
Products are extracted with:
- Title, price, rating, reviews
- Delivery information
- Sponsored/Small Business badges
- Direct product URLs
## 📁 Files
- `amazon_r2d2_search.py` - Main example script
- `header.html` - Amazon search bar HTML (provided)
- `product.html` - Product card HTML (provided)
- **Generated files:**
- `generated_search_script.c4a` - Auto-generated search automation
- `generated_product_schema.json` - Auto-generated extraction rules
- `extracted_products.json` - Final scraped data
- `search_results_screenshot.png` - Visual proof of results
## 🏃 Running the Example
1. **Prerequisites**
```bash
# Ensure Crawl4AI is installed
pip install crawl4ai
# Set up LLM API key (for script generation)
export OPENAI_API_KEY="your-key-here"
```
2. **Run the scraper**
```bash
python amazon_r2d2_search.py
```
3. **Watch the magic!**
- Browser window opens (not headless)
- Navigates to Amazon.com
- Searches for "r2d2"
- Extracts all products
- Saves results to JSON
## 📊 Sample Output
```json
[
{
"title": "Death Star BB8 R2D2 Golf Balls with 20 Printed tees",
"price": "29.95",
"rating": "4.7",
"reviews_count": "184",
"delivery": "FREE delivery Thu, Jun 19",
"url": "https://www.amazon.com/Death-Star-R2D2-Balls-Printed/dp/B081XSYZMS",
"is_sponsored": true,
"small_business": true
},
...
]
```
## 🔍 Key Features Demonstrated
### Session Persistence
```python
# Same session_id across multiple arun() calls
config = CrawlerRunConfig(
session_id="amazon_r2d2_session",
# ... other settings
)
```
### LLM Script Generation
```python
# Generate automation from natural language + HTML
script = C4ACompiler.generate_script(
html=header_html,
query="Find search box, type 'r2d2', click search",
mode="c4a"
)
```
### JSON CSS Extraction
```python
# Structured data extraction with CSS selectors
schema = {
"baseSelector": "[data-component-type='s-search-result']",
"fields": [
{"name": "title", "selector": "h2 a span", "type": "text"},
{"name": "price", "selector": ".a-price-whole", "type": "text"}
]
}
```
## 🛠️ Customization
### Search Different Products
Change the search term in the script generation:
```python
search_goal = """
...
3. Type "star wars lego" into the search box
...
"""
```
### Extract More Data
Add fields to the extraction schema:
```python
"fields": [
# ... existing fields
{"name": "prime", "selector": ".s-prime", "type": "exists"},
{"name": "image_url", "selector": "img.s-image", "type": "attribute", "attribute": "src"}
]
```
### Use Different Sites
Adapt the approach for other e-commerce sites by:
1. Providing their HTML snippets
2. Adjusting the search goals
3. Updating the extraction schema
## 🎓 Learning Points
1. **No Manual Scripting**: LLM generates all automation code
2. **Session Management**: Maintain state across page navigations
3. **Robust Extraction**: Handle dynamic content and multiple products
4. **Error Handling**: Graceful fallbacks if generation fails
## 🐛 Troubleshooting
- **"No products found"**: Check if Amazon's HTML structure changed
- **"Script generation failed"**: Ensure LLM API key is configured
- **"Page timeout"**: Increase wait times in the config
- **"Session lost"**: Ensure same session_id is used consistently
## 📚 Next Steps
- Try searching for different products
- Add pagination to get more results
- Extract product details pages
- Compare prices across different sellers
- Build a price monitoring system
---
This example shows the power of combining LLM intelligence with web automation. The scripts adapt to HTML changes and natural language instructions make automation accessible to everyone!

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#!/usr/bin/env python3
"""
Amazon R2D2 Product Search Example using Crawl4AI
This example demonstrates:
1. Using LLM to generate C4A-Script from HTML snippets
2. Multi-step crawling with session persistence
3. JSON CSS extraction for structured product data
4. Complete workflow: homepage → search → extract products
Requirements:
- Crawl4AI with generate_script support
- LLM API key (configured in environment)
"""
import asyncio
import json
import os
from pathlib import Path
from typing import List, Dict, Any
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai import JsonCssExtractionStrategy
from crawl4ai.script.c4a_compile import C4ACompiler
class AmazonR2D2Scraper:
def __init__(self):
self.base_dir = Path(__file__).parent
self.search_script_path = self.base_dir / "generated_search_script.js"
self.schema_path = self.base_dir / "generated_product_schema.json"
self.results_path = self.base_dir / "extracted_products.json"
self.session_id = "amazon_r2d2_session"
async def generate_search_script(self) -> str:
"""Generate JavaScript for Amazon search interaction"""
print("🔧 Generating search script from header.html...")
# Check if already generated
if self.search_script_path.exists():
print("✅ Using cached search script")
return self.search_script_path.read_text()
# Read the header HTML
header_html = (self.base_dir / "header.html").read_text()
# Generate script using LLM
search_goal = """
Find the search box and search button, then:
1. Wait for the search box to be visible
2. Click on the search box to focus it
3. Clear any existing text
4. Type "r2d2" into the search box
5. Click the search submit button
6. Wait for navigation to complete and search results to appear
"""
try:
script = C4ACompiler.generate_script(
html=header_html,
query=search_goal,
mode="js"
)
# Save for future use
self.search_script_path.write_text(script)
print("✅ Search script generated and saved!")
print(f"📄 Script:\n{script}")
return script
except Exception as e:
print(f"❌ Error generating search script: {e}")
async def generate_product_schema(self) -> Dict[str, Any]:
"""Generate JSON CSS extraction schema from product HTML"""
print("\n🔧 Generating product extraction schema...")
# Check if already generated
if self.schema_path.exists():
print("✅ Using cached extraction schema")
return json.loads(self.schema_path.read_text())
# Read the product HTML
product_html = (self.base_dir / "product.html").read_text()
# Generate extraction schema using LLM
schema_goal = """
Create a JSON CSS extraction schema to extract:
- Product title (from the h2 element)
- Price (the dollar amount)
- Rating (star rating value)
- Number of reviews
- Delivery information
- Product URL (from the main product link)
- Whether it's sponsored
- Small business badge if present
The schema should handle multiple products on a search results page.
"""
try:
# Generate JavaScript that returns the schema
schema = JsonCssExtractionStrategy.generate_schema(
html=product_html,
query=schema_goal,
)
# Save for future use
self.schema_path.write_text(json.dumps(schema, indent=2))
print("✅ Extraction schema generated and saved!")
print(f"📄 Schema fields: {[f['name'] for f in schema['fields']]}")
return schema
except Exception as e:
print(f"❌ Error generating schema: {e}")
async def crawl_amazon(self):
"""Main crawling logic with 2 calls using same session"""
print("\n🚀 Starting Amazon R2D2 product search...")
# Generate scripts and schemas
search_script = await self.generate_search_script()
product_schema = await self.generate_product_schema()
# Configure browser (headless=False to see the action)
browser_config = BrowserConfig(
headless=False,
verbose=True,
viewport_width=1920,
viewport_height=1080
)
async with AsyncWebCrawler(config=browser_config) as crawler:
print("\n📍 Step 1: Navigate to Amazon and search for R2D2")
# FIRST CALL: Navigate to Amazon and execute search
search_config = CrawlerRunConfig(
session_id=self.session_id,
js_code= f"(() => {{ {search_script} }})()", # Execute generated JS
wait_for=".s-search-results", # Wait for search results
extraction_strategy=JsonCssExtractionStrategy(schema=product_schema),
delay_before_return_html=3.0 # Give time for results to load
)
results = await crawler.arun(
url="https://www.amazon.com",
config=search_config
)
if not results.success:
print("❌ Failed to search Amazon")
print(f"Error: {results.error_message}")
return
print("✅ Search completed successfully!")
print("✅ Product extraction completed!")
# Extract and save results
print("\n📍 Extracting product data")
if results[0].extracted_content:
products = json.loads(results[0].extracted_content)
print(f"🔍 Found {len(products)} products in search results")
print(f"✅ Extracted {len(products)} R2D2 products")
# Save results
self.results_path.write_text(
json.dumps(products, indent=2)
)
print(f"💾 Results saved to: {self.results_path}")
# Print sample results
print("\n📊 Sample Results:")
for i, product in enumerate(products[:3], 1):
print(f"\n{i}. {product['title'][:60]}...")
print(f" Price: ${product['price']}")
print(f" Rating: {product['rating']} ({product['number_of_reviews']} reviews)")
print(f" {'🏪 Small Business' if product['small_business_badge'] else ''}")
print(f" {'📢 Sponsored' if product['sponsored'] else ''}")
else:
print("❌ No products extracted")
async def main():
"""Run the Amazon scraper"""
scraper = AmazonR2D2Scraper()
await scraper.crawl_amazon()
print("\n🎉 Amazon R2D2 search example completed!")
print("Check the generated files:")
print(" - generated_search_script.js")
print(" - generated_product_schema.json")
print(" - extracted_products.json")
print(" - search_results_screenshot.png")
if __name__ == "__main__":
asyncio.run(main())

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@@ -0,0 +1,114 @@
[
{
"title": "Death Star BB8 R2D2 Golf Balls with 20 Printed tees \u2022 Great Gift IDEA from Moms, DADS and Kids -",
"price": "$29.95",
"rating": "4.7 out of 5 stars",
"number_of_reviews": "184",
"delivery_info": "FREE delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfYXRmOjIwMDA2NzY0ODgwMjc5ODo6MDo6&url=%2FDeath-Star-R2D2-Balls-Printed%2Fdp%2FB081XSYZMS%2Fref%3Dsr_1_1_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-1-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9hdGY%26psc%3D1",
"sponsored": "Sponsored",
"small_business_badge": "Small Business"
},
{
"title": "TEENKON French Press Insulated 304 Stainless Steel Coffee Maker, 32 Oz Robot R2D2 Hand Home Coffee Presser, with Filter Screen for Brew Coffee and Tea (White)",
"price": "$49.99",
"rating": "4.3 out of 5 stars",
"number_of_reviews": "82",
"delivery_info": "Delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfbXRmOjMwMDAzNzc4Njg4MDAwMjo6MDo6&url=%2FTEENKON-French-Insulated-Stainless-Presser%2Fdp%2FB0CD3HH5PN%2Fref%3Dsr_1_17_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-17-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9tdGY%26psc%3D1",
"sponsored": "Sponsored"
},
{
"title": "3D Illusion LED Night Light,7 Colors Gradual Changing Touch Switch USB Table Lamp for Holiday Gifts or Home Decorations (R2-D2)",
"price": "$9.97",
"rating": "4.3 out of 5 stars",
"number_of_reviews": "235",
"delivery_info": "Delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfbXRmOjIwMDA0NjMwMTQwODA4MTo6MDo6&url=%2FIllusion-Gradual-Changing-Holiday-Decorations%2Fdp%2FB089NMBKF2%2Fref%3Dsr_1_18_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-18-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9tdGY%26psc%3D1",
"sponsored": "Sponsored"
},
{
"title": "Paladone Star Wars R2-D2 Headlamp with Droid Sounds, Officially Licensed Disney Star Wars Head Lamp and Reading Light",
"price": "$21.99",
"rating": "4.1 out of 5 stars",
"number_of_reviews": "66",
"delivery_info": "FREE delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfbXRmOjMwMDI1NjA0MDQwMTUwMjo6MDo6&url=%2FSounds-Officially-Licensed-Headlamp-Flashlight%2Fdp%2FB09RTDZF8J%2Fref%3Dsr_1_19_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-19-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9tdGY%26psc%3D1",
"sponsored": "Sponsored"
},
{
"title": "4 Pcs Set Star Wars Kylo Ren BB8 Stormtrooper R2D2 Silicone Travel Luggage Baggage Identification Labels ID Tag for Bag Suitcase Plane Cruise Ships with Belt Strap",
"price": "$16.99",
"rating": "4.7 out of 5 stars",
"number_of_reviews": "3,414",
"delivery_info": "FREE delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfbXRmOjIwMDAyMzk3ODkwMzIxMTo6MDo6&url=%2FFinex-Set-Suitcase-Adjustable-Stormtrooper%2Fdp%2FB01D1CBFJS%2Fref%3Dsr_1_24_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-24-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9tdGY%26psc%3D1",
"sponsored": "Sponsored",
"small_business_badge": "Small Business"
},
{
"title": "Papyrus Star Wars Birthday Card Assortment, Darth Vader, Storm Trooper, and R2-D2 (3-Count)",
"price": "$23.16",
"rating": "4.8 out of 5 stars",
"number_of_reviews": "328",
"delivery_info": "FREE delivery",
"product_url": "/sspa/click?ie=UTF8&spc=MToxNDMzMjA0MzA4MzEzMjAxOjE3NDkzMDI3NDY6c3BfbXRmOjMwMDcwNzI4MjA1MzcwMjo6MDo6&url=%2FPapyrus-Birthday-Assortment-Characters-3-Count%2Fdp%2FB07YT2ZPKX%2Fref%3Dsr_1_25_sspa%3Fdib%3DeyJ2IjoiMSJ9.iiJYY01upNMdD4BNNt8CYLZEIMXulNkcBlKEMJlr_U_h9eSGqChxwcIiCKUbJeEO_plLkXZvB7Yx-v4UDOCdiUFI-sHFgcTznXrP7tdD8xHpRaMKmaBDWMCAFwzPmVcgK_6Q9qIRoN4sp8tunKX26j5EC_8LiK-D5QximGkE8i8f-R5GhSUo__DaSkAP1cnzxUtSESfA8fYfewsZ1iSol9_zohE6r1ZZeawnWHPmDTkLqzCW3uK44EnvJbPFvzMlpiKcs9p9Eh9w5Rc5rrumMihdaWkC63B0cz5jU-S2Ieg._D8d5nv3hOExHPbZ04L-vaC7YwJjEZM-vu5AED5sz0U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749302746%26sr%3D8-25-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9tdGY%26psc%3D1",
"sponsored": "Sponsored"
},
{
"title": "STAR WARS R2-D2 Artoo 3D Top Motion Lamp, Mood Light | 18 Inches",
"price": "$69.99",
"rating": "4.5 out of 5 stars",
"number_of_reviews": "520",
"delivery_info": "FREE delivery",
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View File

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View File

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View File

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View File

@@ -0,0 +1,206 @@
<div class="sg-col-inner">
<div cel_widget_id="MAIN-SEARCH_RESULTS-2"
class="s-widget-container s-spacing-small s-widget-container-height-small celwidget slot=MAIN template=SEARCH_RESULTS widgetId=search-results_1"
data-csa-c-pos="1" data-csa-c-item-id="amzn1.asin.1.B081XSYZMS" data-csa-op-log-render="" data-csa-c-type="item"
data-csa-c-id="dp9zuy-vyww1v-brlmmq-fmgitb" data-cel-widget="MAIN-SEARCH_RESULTS-2">
<div data-component-type="s-impression-logger"
data-component-props="{&quot;percentageShownToFire&quot;:&quot;50&quot;,&quot;batchable&quot;:true,&quot;requiredElementSelector&quot;:&quot;.s-image:visible&quot;,&quot;url&quot;:&quot;https://unagi-na.amazon.com/1/events/com.amazon.eel.SponsoredProductsEventTracking.prod?qualifier=1749299833&amp;id=1740514893473797&amp;widgetName=sp_atf&amp;adId=200067648802798&amp;eventType=1&amp;adIndex=0&quot;}"
class="rush-component s-expand-height" data-component-id="6">
<div data-component-type="s-impression-counter"
data-component-props="{&quot;presenceCounterName&quot;:&quot;sp_delivered&quot;,&quot;testElementSelector&quot;:&quot;.s-image&quot;,&quot;hiddenCounterName&quot;:&quot;sp_hidden&quot;}"
class="rush-component s-featured-result-item s-expand-height" data-component-id="7">
<span class="a-declarative" data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw" data-action="puis-card-container-declarative"
data-csa-c-func-deps="aui-da-puis-card-container-declarative"
data-csa-c-item-id="amzn1.asin.B081XSYZMS" data-csa-c-posx="1" data-csa-c-type="item"
data-csa-c-owner="puis" data-csa-c-id="88w0j1-kcbf5g-80v4i9-96cv88">
<div class="puis-card-container s-card-container s-overflow-hidden aok-relative puis-expand-height puis-include-content-margin puis puis-v2dwi5hq8xzthf26x0gg1mcl2oj s-latency-cf-section puis-card-border"
data-cy="asin-faceout-container">
<div class="a-section a-spacing-base">
<div class="s-product-image-container aok-relative s-text-center s-image-overlay-grey puis-image-overlay-grey s-padding-left-small s-padding-right-small puis-spacing-small s-height-equalized puis puis-v2dwi5hq8xzthf26x0gg1mcl2oj"
data-cy="image-container" style="padding-top: 0px !important;"><span
data-component-type="s-product-image" class="rush-component"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw"><a aria-hidden="true"
class="a-link-normal s-no-outline" tabindex="-1"
href="/sspa/click?ie=UTF8&amp;spc=MToxNzQwNTE0ODkzNDczNzk3OjE3NDkyOTk4MzM6c3BfYXRmOjIwMDA2NzY0ODgwMjc5ODo6MDo6&amp;url=%2FDeath-Star-R2D2-Balls-Printed%2Fdp%2FB081XSYZMS%2Fref%3Dsr_1_1_sspa%3Fcrid%3D3C1EXMXN59Q9G%26dib%3DeyJ2IjoiMSJ9.7tBl5bhZh59L9qIPZUe9SLa2fy_HvzboxuQxvrRcAc0VUXayi9fxQFsMLyFplDE9vMkIJbP76AVpa-5-fxhNza3DqhX4tss4NlB49WPi_dA00Hw6O8qK5pDzdetYlhGgOyXOLBe7mTG9oJ5W0wcvQhEVoX9mpJk_SGeqRLWGA0dBSjYCZtiyrY8_B-DP53S7fbYwiSYtq-g7sQDXKVadRpGvUyKq7yxA0SLsU42uvoqSGb0qcd6udL1wbnTEkKmwNjNSb7xIUb-8PyE7DTPMt1ScJksn70sFQMJNkM2aK5M.x9_jYvKPnSibV1d0umUStZBxlSTSXrzVIFKqFzS8c-U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749299833%26sprefix%3Dr2d2%252Caps%252C548%26sr%3D8-1-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9hdGY%26psc%3D1">
<div class="a-section aok-relative s-image-square-aspect"><img class="s-image"
src="https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL320_.jpg"
srcset="https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL320_.jpg 1x, https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL480_FMwebp_QL65_.jpg 1.5x, https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL640_FMwebp_QL65_.jpg 2x, https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL800_FMwebp_QL65_.jpg 2.5x, https://m.media-amazon.com/images/I/61kAC69zQUL._AC_UL960_FMwebp_QL65_.jpg 3x"
alt="Sponsored Ad - Death Star BB8 R2D2 Golf Balls with 20 Printed tees • Great Gift IDEA from Moms, DADS and Kids -"
aria-hidden="true" data-image-index="1" data-image-load=""
data-image-latency="s-product-image" data-image-source-density="1">
</div>
</a></span></div>
<div class="a-section a-spacing-small puis-padding-left-small puis-padding-right-small">
<div data-cy="title-recipe"
class="a-section a-spacing-none a-spacing-top-small s-title-instructions-style">
<div class="a-row a-spacing-micro"><span class="a-declarative"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw" data-action="a-popover"
data-csa-c-func-deps="aui-da-a-popover"
data-a-popover="{&quot;name&quot;:&quot;sp-info-popover-B081XSYZMS&quot;,&quot;position&quot;:&quot;triggerVertical&quot;,&quot;popoverLabel&quot;:&quot;View Sponsored information or leave ad feedback&quot;,&quot;closeButtonLabel&quot;:&quot;Close popup&quot;,&quot;closeButton&quot;:&quot;true&quot;,&quot;dataStrategy&quot;:&quot;preload&quot;}"
data-csa-c-type="widget" data-csa-c-id="wqddan-z1l67e-lissct-rciw65"><a
href="javascript:void(0)" role="button" style="text-decoration: none;"
class="puis-label-popover puis-sponsored-label-text"><span
class="puis-label-popover-default"><span
aria-label="View Sponsored information or leave ad feedback"
class="a-color-secondary">Sponsored</span></span><span
class="puis-label-popover-hover"><span aria-hidden="true"
class="a-color-base">Sponsored</span></span> <span
class="aok-inline-block puis-sponsored-label-info-icon"></span></a></span>
<div class="a-popover-preload" id="a-popover-sp-info-popover-B081XSYZMS">
<div class="puis puis-v2dwi5hq8xzthf26x0gg1mcl2oj"><span>Youre seeing this
ad based on the products relevance to your search query.</span>
<div class="a-row"><span class="a-declarative"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw"
data-action="s-safe-ajax-modal-trigger"
data-csa-c-func-deps="aui-da-s-safe-ajax-modal-trigger"
data-s-safe-ajax-modal-trigger="{&quot;header&quot;:&quot;Leave feedback&quot;,&quot;dataStrategy&quot;:&quot;ajax&quot;,&quot;ajaxUrl&quot;:&quot;/af/sp-loom/feedback-form?pl=%7B%22adPlacementMetaData%22%3A%7B%22searchTerms%22%3A%22cjJkMg%3D%3D%22%2C%22pageType%22%3A%22Search%22%2C%22feedbackType%22%3A%22sponsoredProductsLoom%22%2C%22slotName%22%3A%22TOP%22%7D%2C%22adCreativeMetaData%22%3A%7B%22adProgramId%22%3A1024%2C%22adCreativeDetails%22%3A%5B%7B%22asin%22%3A%22B081XSYZMS%22%2C%22title%22%3A%22Death+Star+BB8+R2D2+Golf+Balls+with+20+Printed+tees+%E2%80%A2+Great+Gift+IDEA+from+Moms%2C+DADS+and+Kids+-%22%2C%22priceInfo%22%3A%7B%22amount%22%3A29.95%2C%22currencyCode%22%3A%22USD%22%7D%2C%22sku%22%3A%22starwars3pk20tees%22%2C%22adId%22%3A%22A03790291PREH7M3Q3SVS%22%2C%22campaignId%22%3A%22A01050612Q0SQZ2PTMGO9%22%2C%22advertiserIdNS%22%3Anull%2C%22selectionSignals%22%3Anull%7D%5D%7D%7D&quot;}"
data-csa-c-type="widget"
data-csa-c-id="ygslsp-ir23ei-7k9x6z-73l1tp"><a
class="a-link-normal s-underline-text s-underline-link-text s-link-style"
href="#"><span>Leave ad feedback</span> </a> </span></div>
</div>
</div>
</div><a class="a-link-normal s-line-clamp-4 s-link-style a-text-normal"
href="/sspa/click?ie=UTF8&amp;spc=MToxNzQwNTE0ODkzNDczNzk3OjE3NDkyOTk4MzM6c3BfYXRmOjIwMDA2NzY0ODgwMjc5ODo6MDo6&amp;url=%2FDeath-Star-R2D2-Balls-Printed%2Fdp%2FB081XSYZMS%2Fref%3Dsr_1_1_sspa%3Fcrid%3D3C1EXMXN59Q9G%26dib%3DeyJ2IjoiMSJ9.7tBl5bhZh59L9qIPZUe9SLa2fy_HvzboxuQxvrRcAc0VUXayi9fxQFsMLyFplDE9vMkIJbP76AVpa-5-fxhNza3DqhX4tss4NlB49WPi_dA00Hw6O8qK5pDzdetYlhGgOyXOLBe7mTG9oJ5W0wcvQhEVoX9mpJk_SGeqRLWGA0dBSjYCZtiyrY8_B-DP53S7fbYwiSYtq-g7sQDXKVadRpGvUyKq7yxA0SLsU42uvoqSGb0qcd6udL1wbnTEkKmwNjNSb7xIUb-8PyE7DTPMt1ScJksn70sFQMJNkM2aK5M.x9_jYvKPnSibV1d0umUStZBxlSTSXrzVIFKqFzS8c-U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749299833%26sprefix%3Dr2d2%252Caps%252C548%26sr%3D8-1-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9hdGY%26psc%3D1">
<h2 aria-label="Sponsored Ad - Death Star BB8 R2D2 Golf Balls with 20 Printed tees • Great Gift IDEA from Moms, DADS and Kids -"
class="a-size-base-plus a-spacing-none a-color-base a-text-normal">
<span>Death Star BB8 R2D2 Golf Balls with 20 Printed tees • Great Gift IDEA
from Moms, DADS and Kids -</span></h2>
</a>
</div>
<div data-cy="reviews-block" class="a-section a-spacing-none a-spacing-top-micro">
<div class="a-row a-size-small"><span class="a-declarative"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw" data-action="a-popover"
data-csa-c-func-deps="aui-da-a-popover"
data-a-popover="{&quot;position&quot;:&quot;triggerBottom&quot;,&quot;popoverLabel&quot;:&quot;4.7 out of 5 stars, rating details&quot;,&quot;url&quot;:&quot;/review/widgets/average-customer-review/popover/ref=acr_search__popover?ie=UTF8&amp;asin=B081XSYZMS&amp;ref_=acr_search__popover&amp;contextId=search&quot;,&quot;closeButton&quot;:true,&quot;closeButtonLabel&quot;:&quot;&quot;}"
data-csa-c-type="widget" data-csa-c-id="oykdvt-8s1ebj-2kegf2-7ii7tp"><a
aria-label="4.7 out of 5 stars, rating details"
href="javascript:void(0)" role="button"
class="a-popover-trigger a-declarative"><i
data-cy="reviews-ratings-slot" aria-hidden="true"
class="a-icon a-icon-star-small a-star-small-4-5"><span
class="a-icon-alt">4.7 out of 5 stars</span></i><i
class="a-icon a-icon-popover"></i></a></span> <span
data-component-type="s-client-side-analytics" class="rush-component"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw" data-component-id="8">
<div style="display: inline-block"
class="s-csa-instrumentation-wrapper alf-search-csa-instrumentation-wrapper"
data-csa-c-type="alf-af-component"
data-csa-c-content-id="alf-customer-ratings-count-component"
data-csa-c-slot-id="alf-reviews" data-csa-op-log-render=""
data-csa-c-layout="GRID" data-csa-c-asin="B081XSYZMS"
data-csa-c-id="6l5wc4-ngelan-hd9x4t-d4a2k7"><a aria-label="184 ratings"
class="a-link-normal s-underline-text s-underline-link-text s-link-style"
href="/sspa/click?ie=UTF8&amp;spc=MToxNzQwNTE0ODkzNDczNzk3OjE3NDkyOTk4MzM6c3BfYXRmOjIwMDA2NzY0ODgwMjc5ODo6MDo6&amp;url=%2FDeath-Star-R2D2-Balls-Printed%2Fdp%2FB081XSYZMS%2Fref%3Dsr_1_1_sspa%3Fcrid%3D3C1EXMXN59Q9G%26dib%3DeyJ2IjoiMSJ9.7tBl5bhZh59L9qIPZUe9SLa2fy_HvzboxuQxvrRcAc0VUXayi9fxQFsMLyFplDE9vMkIJbP76AVpa-5-fxhNza3DqhX4tss4NlB49WPi_dA00Hw6O8qK5pDzdetYlhGgOyXOLBe7mTG9oJ5W0wcvQhEVoX9mpJk_SGeqRLWGA0dBSjYCZtiyrY8_B-DP53S7fbYwiSYtq-g7sQDXKVadRpGvUyKq7yxA0SLsU42uvoqSGb0qcd6udL1wbnTEkKmwNjNSb7xIUb-8PyE7DTPMt1ScJksn70sFQMJNkM2aK5M.x9_jYvKPnSibV1d0umUStZBxlSTSXrzVIFKqFzS8c-U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749299833%26sprefix%3Dr2d2%252Caps%252C548%26sr%3D8-1-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9hdGY%26psc%3D1#customerReviews"><span
aria-hidden="true"
class="a-size-base s-underline-text">184</span> </a> </div>
</span></div>
<div class="a-row a-size-base"><span class="a-size-base a-color-secondary">50+
bought in past month</span></div>
</div>
<div data-cy="price-recipe"
class="a-section a-spacing-none a-spacing-top-small s-price-instructions-style">
<div class="a-row a-size-base a-color-base">
<div class="a-row"><span id="price-link" class="aok-offscreen">Price, product
page</span><a aria-describedby="price-link"
class="a-link-normal s-no-hover s-underline-text s-underline-link-text s-link-style a-text-normal"
href="/sspa/click?ie=UTF8&amp;spc=MToxNzQwNTE0ODkzNDczNzk3OjE3NDkyOTk4MzM6c3BfYXRmOjIwMDA2NzY0ODgwMjc5ODo6MDo6&amp;url=%2FDeath-Star-R2D2-Balls-Printed%2Fdp%2FB081XSYZMS%2Fref%3Dsr_1_1_sspa%3Fcrid%3D3C1EXMXN59Q9G%26dib%3DeyJ2IjoiMSJ9.7tBl5bhZh59L9qIPZUe9SLa2fy_HvzboxuQxvrRcAc0VUXayi9fxQFsMLyFplDE9vMkIJbP76AVpa-5-fxhNza3DqhX4tss4NlB49WPi_dA00Hw6O8qK5pDzdetYlhGgOyXOLBe7mTG9oJ5W0wcvQhEVoX9mpJk_SGeqRLWGA0dBSjYCZtiyrY8_B-DP53S7fbYwiSYtq-g7sQDXKVadRpGvUyKq7yxA0SLsU42uvoqSGb0qcd6udL1wbnTEkKmwNjNSb7xIUb-8PyE7DTPMt1ScJksn70sFQMJNkM2aK5M.x9_jYvKPnSibV1d0umUStZBxlSTSXrzVIFKqFzS8c-U%26dib_tag%3Dse%26keywords%3Dr2d2%26qid%3D1749299833%26sprefix%3Dr2d2%252Caps%252C548%26sr%3D8-1-spons%26sp_csd%3Dd2lkZ2V0TmFtZT1zcF9hdGY%26psc%3D1"><span
class="a-price" data-a-size="xl" data-a-color="base"><span
class="a-offscreen">$29.95</span><span aria-hidden="true"><span
class="a-price-symbol">$</span><span
class="a-price-whole">29<span
class="a-price-decimal">.</span></span><span
class="a-price-fraction">95</span></span></span></a></div>
<div class="a-row"></div>
</div>
</div>
<div data-cy="delivery-recipe" class="a-section a-spacing-none a-spacing-top-micro">
<div class="a-row a-size-base a-color-secondary s-align-children-center"><span
aria-label="FREE delivery Thu, Jun 19 to Malaysia on $49 of eligible items"><span
class="a-color-base">FREE delivery </span><span
class="a-color-base a-text-bold">Thu, Jun 19 </span><span
class="a-color-base">to Malaysia on $49 of eligible items</span></span>
</div>
</div>
<div data-cy="certification-recipe"
class="a-section a-spacing-none a-spacing-top-micro">
<div class="a-row">
<div class="a-section a-spacing-none s-align-children-center">
<div class="a-section a-spacing-none s-pc-faceout-container">
<div>
<div class="s-align-children-center"><span class="a-declarative"
data-version-id="v2dwi5hq8xzthf26x0gg1mcl2oj"
data-render-id="r3o8bgr5zt3kmy2jv4su6fn4kyw"
data-action="s-pc-sidesheet-open"
data-csa-c-func-deps="aui-da-s-pc-sidesheet-open"
data-s-pc-sidesheet-open="{&quot;preloadDomId&quot;:&quot;pc-side-sheet-B081XSYZMS&quot;,&quot;popoverLabel&quot;:&quot;Product certifications&quot;,&quot;interactLoggingMetricsList&quot;:[&quot;provenanceCertifications_desktop_sbe_badge&quot;],&quot;closeButtonLabel&quot;:&quot;Close popup&quot;,&quot;dwellMetric&quot;:&quot;provenanceCertifications_desktop_sbe_badge_t&quot;}"
data-csa-c-type="widget"
data-csa-c-id="hdfxi6-bjlgup-5dql15-88t9ao"><a
data-cy="s-pc-faceout-badge"
class="a-link-normal s-no-underline s-pc-badge s-align-children-center aok-block"
href="javascript:void(0)" role="button">
<div
class="a-section s-pc-attribute-pill-text s-margin-bottom-none s-margin-bottom-none aok-block s-pc-certification-faceout">
<span class="faceout-image-view"></span><img alt=""
src="https://m.media-amazon.com/images/I/111mHoVK0kL._SS200_.png"
class="s-image" height="18px" width="18px">
<span class="a-size-base a-color-base">Small
Business</span>
<div
class="s-margin-bottom-none s-pc-sidesheet-chevron aok-nowrap">
<i class="a-icon a-icon-popover aok-align-center"
role="presentation"></i></div>
</div>
</a></span></div>
</div>
</div>
</div>
<div id="pc-side-sheet-B081XSYZMS"
class="a-section puis puis-v2dwi5hq8xzthf26x0gg1mcl2oj aok-hidden">
<div class="a-section s-pc-container-side-sheet">
<div class="s-align-children-center a-spacing-small">
<div class="s-align-children-center s-pc-certification"
role="heading" aria-level="2"><span
class="faceout-image-view"></span>
<div alt="" style="height: 24px; width: 24px;"
class="a-image-wrapper a-lazy-loaded a-manually-loaded s-image"
data-a-image-source="https://m.media-amazon.com/images/I/111mHoVK0kL._SS200_.png">
<noscript><img alt=""
src="https://m.media-amazon.com/images/I/111mHoVK0kL._SS200_.png"
height="24px" width="24px" /></noscript></div> <span
class="a-size-medium-plus a-color-base a-text-bold">Small
Business</span>
</div>
</div>
<div class="a-spacing-medium s-pc-link-container"><span
class="a-size-base a-color-secondary">Shop products from small
business brands sold in Amazons store. Discover more about the
small businesses partnering with Amazon and Amazons commitment
to empowering them.</span> <a
class="a-size-base a-link-normal s-link-style"
href="https://www.amazon.com/b/ref=s9_acss_bw_cg_sbp22c_1e1_w/ref=SBE_navbar_5?pf_rd_r=6W5X52VNZRB7GK1E1VX2&amp;pf_rd_p=56621c3d-cff4-45e1-9bf4-79bbeb8006fc&amp;pf_rd_m=ATVPDKIKX0DER&amp;pf_rd_s=merchandised-search-top-3&amp;pf_rd_t=30901&amp;pf_rd_i=17879387011&amp;node=18018208011">Learn
more</a> </div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</span>
</div>
</div>
</div>
</div>

View File

@@ -0,0 +1,217 @@
"""
C4A-Script API Usage Examples
Shows how to use the new Result-based API in various scenarios
"""
from c4a_compile import compile, validate, compile_file
from c4a_result import CompilationResult, ValidationResult
import json
print("C4A-Script API Usage Examples")
print("=" * 80)
# Example 1: Basic compilation
print("\n1. Basic Compilation")
print("-" * 40)
script = """
GO https://example.com
WAIT 2
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
REPEAT (SCROLL DOWN 300, 3)
"""
result = compile(script)
print(f"Success: {result.success}")
print(f"Statements generated: {len(result.js_code) if result.js_code else 0}")
# Example 2: Error handling
print("\n\n2. Error Handling")
print("-" * 40)
error_script = """
GO https://example.com
IF (EXISTS `.modal`) CLICK `.close`
undefined_procedure
"""
result = compile(error_script)
if not result.success:
# Access error details
error = result.first_error
print(f"Error on line {error.line}: {error.message}")
print(f"Error code: {error.code}")
# Show suggestions if available
if error.suggestions:
print("Suggestions:")
for suggestion in error.suggestions:
print(f" - {suggestion.message}")
# Example 3: Validation only
print("\n\n3. Validation (no code generation)")
print("-" * 40)
validation_script = """
PROC validate_form
IF (EXISTS `#email`) THEN TYPE "test@example.com"
PRESS Tab
ENDPROC
validate_form
"""
validation = validate(validation_script)
print(f"Valid: {validation.valid}")
if validation.errors:
print(f"Errors found: {len(validation.errors)}")
# Example 4: JSON output for UI
print("\n\n4. JSON Output for UI Integration")
print("-" * 40)
ui_script = """
CLICK button.submit
"""
result = compile(ui_script)
if not result.success:
# Get JSON for UI
error_json = result.to_dict()
print("Error data for UI:")
print(json.dumps(error_json["errors"][0], indent=2))
# Example 5: File compilation
print("\n\n5. File Compilation")
print("-" * 40)
# Create a test file
test_file = "test_script.c4a"
with open(test_file, "w") as f:
f.write("""
GO https://example.com
WAIT `.content` 5
CLICK `.main-button`
""")
result = compile_file(test_file)
print(f"File compilation: {'Success' if result.success else 'Failed'}")
if result.success:
print(f"Generated {len(result.js_code)} JavaScript statements")
# Clean up
import os
os.remove(test_file)
# Example 6: Batch processing
print("\n\n6. Batch Processing Multiple Scripts")
print("-" * 40)
scripts = [
"GO https://example1.com\nCLICK `.button`",
"GO https://example2.com\nWAIT 2",
"GO https://example3.com\nINVALID_CMD"
]
results = []
for i, script in enumerate(scripts, 1):
result = compile(script)
results.append(result)
status = "" if result.success else ""
print(f"Script {i}: {status}")
# Summary
successful = sum(1 for r in results if r.success)
print(f"\nBatch result: {successful}/{len(scripts)} successful")
# Example 7: Custom error formatting
print("\n\n7. Custom Error Formatting")
print("-" * 40)
def format_error_for_ide(error):
"""Format error for IDE integration"""
return f"{error.source_line}:{error.line}:{error.column}: {error.type.value}: {error.message} [{error.code}]"
error_script = "IF EXISTS `.button` THEN CLICK `.button`"
result = compile(error_script)
if not result.success:
error = result.first_error
print("IDE format:", format_error_for_ide(error))
print("Simple format:", error.simple_message)
print("Full format:", error.formatted_message)
# Example 8: Working with warnings (future feature)
print("\n\n8. Handling Warnings")
print("-" * 40)
# In the future, we might have warnings
result = compile("GO https://example.com\nWAIT 100") # Very long wait
print(f"Success: {result.success}")
print(f"Warnings: {len(result.warnings)}")
# Example 9: Metadata usage
print("\n\n9. Using Metadata")
print("-" * 40)
complex_script = """
PROC helper1
CLICK `.btn1`
ENDPROC
PROC helper2
CLICK `.btn2`
ENDPROC
GO https://example.com
helper1
helper2
"""
result = compile(complex_script)
if result.success:
print(f"Script metadata:")
for key, value in result.metadata.items():
print(f" {key}: {value}")
# Example 10: Integration patterns
print("\n\n10. Integration Patterns")
print("-" * 40)
# Web API endpoint simulation
def api_compile(request_body):
"""Simulate API endpoint"""
script = request_body.get("script", "")
result = compile(script)
response = {
"status": "success" if result.success else "error",
"data": result.to_dict()
}
return response
# CLI tool simulation
def cli_compile(script, output_format="text"):
"""Simulate CLI tool"""
result = compile(script)
if output_format == "json":
return result.to_json()
elif output_format == "simple":
if result.success:
return f"OK: {len(result.js_code)} statements"
else:
return f"ERROR: {result.first_error.simple_message}"
else:
return str(result)
# Test the patterns
api_response = api_compile({"script": "GO https://example.com"})
print(f"API response status: {api_response['status']}")
cli_output = cli_compile("WAIT 2", "simple")
print(f"CLI output: {cli_output}")
print("\n" + "=" * 80)
print("All examples completed successfully!")

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"""
C4A-Script Hello World
A concise example showing how to use the C4A-Script compiler
"""
from c4a_compile import compile
# Define your C4A-Script
script = """
GO https://example.com
WAIT `#content` 5
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
CLICK `button.submit`
"""
# Compile the script
result = compile(script)
# Check if compilation was successful
if result.success:
# Success! Use the generated JavaScript
print("✅ Compilation successful!")
print(f"Generated {len(result.js_code)} JavaScript statements:\n")
for i, js in enumerate(result.js_code, 1):
print(f"{i}. {js}\n")
# In real usage, you'd pass result.js_code to Crawl4AI:
# config = CrawlerRunConfig(js_code=result.js_code)
else:
# Error! Handle the compilation error
print("❌ Compilation failed!")
# Get the first error (there might be multiple)
error = result.first_error
# Show error details
print(f"Error at line {error.line}, column {error.column}")
print(f"Message: {error.message}")
# Show the problematic code
print(f"\nCode: {error.source_line}")
print(" " * (6 + error.column) + "^")
# Show suggestions if available
if error.suggestions:
print("\n💡 How to fix:")
for suggestion in error.suggestions:
print(f" {suggestion.message}")
# For debugging or logging, you can also get JSON
# error_json = result.to_json()

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"""
C4A-Script Hello World - Error Example
Shows how error handling works
"""
from c4a_compile import compile
# Define a script with an error (missing THEN)
script = """
GO https://example.com
WAIT `#content` 5
IF (EXISTS `.cookie-banner`) CLICK `.accept`
CLICK `button.submit`
"""
# Compile the script
result = compile(script)
# Check if compilation was successful
if result.success:
# Success! Use the generated JavaScript
print("✅ Compilation successful!")
print(f"Generated {len(result.js_code)} JavaScript statements:\n")
for i, js in enumerate(result.js_code, 1):
print(f"{i}. {js}\n")
# In real usage, you'd pass result.js_code to Crawl4AI:
# config = CrawlerRunConfig(js_code=result.js_code)
else:
# Error! Handle the compilation error
print("❌ Compilation failed!")
# Get the first error (there might be multiple)
error = result.first_error
# Show error details
print(f"Error at line {error.line}, column {error.column}")
print(f"Message: {error.message}")
# Show the problematic code
print(f"\nCode: {error.source_line}")
print(" " * (6 + error.column) + "^")
# Show suggestions if available
if error.suggestions:
print("\n💡 How to fix:")
for suggestion in error.suggestions:
print(f" {suggestion.message}")
# For debugging or logging, you can also get JSON
# error_json = result.to_json()

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"""
Demonstration of C4A-Script integration with Crawl4AI
Shows various use cases and features
"""
import asyncio
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai import c4a_compile, CompilationResult
async def example_basic_usage():
"""Basic C4A-Script usage with Crawl4AI"""
print("\n" + "="*60)
print("Example 1: Basic C4A-Script Usage")
print("="*60)
# Define your automation script
c4a_script = """
# Wait for page to load
WAIT `body` 2
# Handle cookie banner if present
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept-btn`
# Scroll down to load more content
SCROLL DOWN 500
WAIT 1
# Click load more button if exists
IF (EXISTS `.load-more`) THEN CLICK `.load-more`
"""
# Create crawler config with C4A script
config = CrawlerRunConfig(
url="https://example.com",
c4a_script=c4a_script,
wait_for="css:.content",
verbose=False
)
print("✅ C4A Script compiled successfully!")
print(f"Generated {len(config.js_code)} JavaScript commands")
# In production, you would run:
# async with AsyncWebCrawler() as crawler:
# result = await crawler.arun(config=config)
async def example_form_filling():
"""Form filling with C4A-Script"""
print("\n" + "="*60)
print("Example 2: Form Filling with C4A-Script")
print("="*60)
# Form automation script
form_script = """
# Set form values
SET email = "test@example.com"
SET message = "This is a test message"
# Fill the form
CLICK `#email-input`
TYPE $email
CLICK `#message-textarea`
TYPE $message
# Submit the form
CLICK `button[type="submit"]`
# Wait for success message
WAIT `.success-message` 10
"""
config = CrawlerRunConfig(
url="https://example.com/contact",
c4a_script=form_script
)
print("✅ Form filling script ready")
print("Script will:")
print(" - Fill email field")
print(" - Fill message textarea")
print(" - Submit form")
print(" - Wait for confirmation")
async def example_dynamic_loading():
"""Handle dynamic content loading"""
print("\n" + "="*60)
print("Example 3: Dynamic Content Loading")
print("="*60)
# Script for infinite scroll or pagination
pagination_script = """
# Initial wait
WAIT `.product-list` 5
# Load all products by clicking "Load More" repeatedly
REPEAT (CLICK `.load-more`, `document.querySelector('.load-more') !== null`)
# Alternative: Scroll to load (infinite scroll)
# REPEAT (SCROLL DOWN 1000, `document.querySelectorAll('.product').length < 100`)
# Extract count
EVAL `console.log('Products loaded: ' + document.querySelectorAll('.product').length)`
"""
config = CrawlerRunConfig(
url="https://example.com/products",
c4a_script=pagination_script,
screenshot=True # Capture final state
)
print("✅ Dynamic loading script ready")
print("Script will load all products by repeatedly clicking 'Load More'")
async def example_multi_step_workflow():
"""Complex multi-step workflow with procedures"""
print("\n" + "="*60)
print("Example 4: Multi-Step Workflow with Procedures")
print("="*60)
# Complex workflow with reusable procedures
workflow_script = """
# Define login procedure
PROC login
CLICK `#username`
TYPE "demo_user"
CLICK `#password`
TYPE "demo_pass"
CLICK `#login-btn`
WAIT `.dashboard` 10
ENDPROC
# Define search procedure
PROC search_product
CLICK `.search-box`
TYPE "laptop"
PRESS Enter
WAIT `.search-results` 5
ENDPROC
# Main workflow
GO https://example.com
login
search_product
# Process results
IF (EXISTS `.no-results`) THEN EVAL `console.log('No products found')`
ELSE REPEAT (CLICK `.add-to-cart`, 3)
"""
# Compile to check for errors
result = c4a_compile(workflow_script)
if result.success:
print("✅ Complex workflow compiled successfully!")
print("Workflow includes:")
print(" - Login procedure")
print(" - Product search")
print(" - Conditional cart additions")
config = CrawlerRunConfig(
url="https://example.com",
c4a_script=workflow_script
)
else:
print("❌ Compilation error:")
error = result.first_error
print(f" Line {error.line}: {error.message}")
async def example_error_handling():
"""Demonstrate error handling"""
print("\n" + "="*60)
print("Example 5: Error Handling")
print("="*60)
# Script with intentional error
bad_script = """
WAIT body 2
CLICK button
IF (EXISTS .modal) CLICK .close
"""
try:
config = CrawlerRunConfig(
url="https://example.com",
c4a_script=bad_script
)
except ValueError as e:
print("✅ Error caught as expected:")
print(f" {e}")
# Fixed version
good_script = """
WAIT `body` 2
CLICK `button`
IF (EXISTS `.modal`) THEN CLICK `.close`
"""
config = CrawlerRunConfig(
url="https://example.com",
c4a_script=good_script
)
print("\n✅ Fixed script compiled successfully!")
async def example_combining_with_extraction():
"""Combine C4A-Script with extraction strategies"""
print("\n" + "="*60)
print("Example 6: C4A-Script + Extraction Strategies")
print("="*60)
from crawl4ai import JsonCssExtractionStrategy
# Script to prepare page for extraction
prep_script = """
# Expand all collapsed sections
REPEAT (CLICK `.expand-btn`, `document.querySelectorAll('.expand-btn:not(.expanded)').length > 0`)
# Load all comments
IF (EXISTS `.load-comments`) THEN CLICK `.load-comments`
WAIT `.comments-section` 5
# Close any popups
IF (EXISTS `.popup-close`) THEN CLICK `.popup-close`
"""
# Define extraction schema
schema = {
"name": "article",
"selector": "article.main",
"fields": {
"title": {"selector": "h1", "type": "text"},
"content": {"selector": ".content", "type": "text"},
"comments": {
"selector": ".comment",
"type": "list",
"fields": {
"author": {"selector": ".author", "type": "text"},
"text": {"selector": ".text", "type": "text"}
}
}
}
}
config = CrawlerRunConfig(
url="https://example.com/article",
c4a_script=prep_script,
extraction_strategy=JsonCssExtractionStrategy(schema),
wait_for="css:.comments-section"
)
print("✅ Combined C4A + Extraction ready")
print("Workflow:")
print(" 1. Expand collapsed sections")
print(" 2. Load comments")
print(" 3. Extract structured data")
async def main():
"""Run all examples"""
print("\n🚀 C4A-Script + Crawl4AI Integration Demo\n")
# Run all examples
await example_basic_usage()
await example_form_filling()
await example_dynamic_loading()
await example_multi_step_workflow()
await example_error_handling()
await example_combining_with_extraction()
print("\n" + "="*60)
print("✅ All examples completed successfully!")
print("="*60)
print("\nTo run actual crawls, uncomment the AsyncWebCrawler sections")
print("or create your own scripts using these examples as templates.")
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
"""
Hello World Example: LLM-Generated C4A-Script
This example shows how to use the new generate_script() function to automatically
create C4A-Script automation from natural language descriptions and HTML.
"""
from crawl4ai.script.c4a_compile import C4ACompiler
def main():
print("🤖 C4A-Script Generation Hello World")
print("=" * 50)
# Example 1: Simple login form
html = """
<html>
<body>
<form id="login">
<input id="email" type="email" placeholder="Email">
<input id="password" type="password" placeholder="Password">
<button id="submit">Login</button>
</form>
</body>
</html>
"""
goal = "Fill in email 'user@example.com', password 'secret123', and submit the form"
print("📝 Goal:", goal)
print("🌐 HTML: Simple login form")
print()
# Generate C4A-Script
print("🔧 Generated C4A-Script:")
print("-" * 30)
c4a_script = C4ACompiler.generate_script(
html=html,
query=goal,
mode="c4a"
)
print(c4a_script)
print()
# Generate JavaScript
print("🔧 Generated JavaScript:")
print("-" * 30)
js_script = C4ACompiler.generate_script(
html=html,
query=goal,
mode="js"
)
print(js_script)
print()
# Example 2: Simple button click
html2 = """
<html>
<body>
<div class="content">
<h1>Welcome!</h1>
<button id="start-btn" class="primary">Get Started</button>
</div>
</body>
</html>
"""
goal2 = "Click the 'Get Started' button"
print("=" * 50)
print("📝 Goal:", goal2)
print("🌐 HTML: Simple button")
print()
print("🔧 Generated C4A-Script:")
print("-" * 30)
c4a_script2 = C4ACompiler.generate_script(
html=html2,
query=goal2,
mode="c4a"
)
print(c4a_script2)
print()
print("✅ Done! The LLM automatically converted natural language goals")
print(" into executable automation scripts.")
if __name__ == "__main__":
main()

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