Update Documentation

• Add smart overlay removal system for handling popups and modals
• Improve screenshot functionality with configurable timing controls
• Implement URL normalization and enhanced link processing
• Add custom base directory support for cache storage
• Refine external content filtering and social media domain handling

This commit significantly improves the crawler's ability to handle modern
websites by automatically removing intrusive overlays and providing better
screenshot capabilities. URL handling is now more robust with proper
normalization and duplicate detection. The cache system is more flexible
with customizable base directory support.

Breaking changes: None
Issue numbers: None

.gitignore

@@ -189,4 +189,21 @@ a.txt
 .lambda_function.py
 ec2*
 
-update_changelog.sh
+update_changelog.sh
+
+.DS_Store
+docs/.DS_Store
+tmp/
+test_env/
+**/.DS_Store
+**/.DS_Store
+
+todo.md
+git_changes.py
+git_changes.md
+pypi_build.sh
+git_issues.py
+git_issues.md
+
+.tests/
+.issues/

CHANGELOG.md

@@ -1,5 +1,299 @@
 # Changelog
 
+## [v0.3.73] - 2024-10-24
+
+### Added
+- Smart overlay removal system in AsyncPlaywrightCrawlerStrategy:
+  - Automatic removal of popups, modals, and cookie notices
+  - Detection and removal of fixed/sticky position elements
+  - Cleaning of empty block elements
+  - Configurable via `remove_overlay_elements` parameter
+- Enhanced screenshot capabilities:
+  - Added `screenshot_wait_for` parameter to control timing
+  - Improved screenshot handling with existing page context
+  - Better error handling with fallback error images
+- New URL normalization utilities:
+  - `normalize_url` function for consistent URL formatting
+  - `is_external_url` function for better link classification
+- Custom base directory support for cache storage:
+  - New `base_directory` parameter in AsyncWebCrawler
+  - Allows specifying alternative locations for `.crawl4ai` folder
+
+### Enhanced
+- Link handling improvements:
+  - Better duplicate link detection
+  - Enhanced internal/external link classification
+  - Improved handling of special URL protocols
+  - Support for anchor links and protocol-relative URLs
+- Configuration refinements:
+  - Streamlined social media domain list
+  - More focused external content filtering
+- LLM extraction strategy:
+  - Added support for separate API base URL via `api_base` parameter
+  - Better handling of base URLs in configuration
+
+### Fixed
+- Screenshot functionality:
+  - Resolved issues with screenshot timing and context
+  - Improved error handling and recovery
+- Link processing:
+  - Fixed URL normalization edge cases
+  - Better handling of invalid URLs
+  - Improved error messages for link processing failures
+
+### Developer Notes
+- The overlay removal system uses advanced JavaScript injection for better compatibility
+- URL normalization handles special cases like mailto:, tel:, and protocol-relative URLs
+- Screenshot system now reuses existing page context for better performance
+- Link processing maintains separate dictionaries for internal and external links to ensure uniqueness
+
+## [v0.3.72] - 2024-10-22
+
+### Added
+- New `ContentCleaningStrategy` class:
+  - Smart content extraction based on text density and element scoring
+  - Automatic removal of boilerplate content
+  - DOM tree analysis for better content identification
+  - Configurable thresholds for content detection
+- Advanced proxy support:
+  - Added `proxy_config` option for authenticated proxy connections
+  - Support for username/password in proxy configuration
+- New content output formats:
+  - `fit_markdown`: Optimized markdown output with main content focus
+  - `fit_html`: Clean HTML with only essential content
+
+### Enhanced
+- Image source detection:
+  - Support for multiple image source attributes (`src`, `data-src`, `srcset`, etc.)
+  - Automatic fallback through potential source attributes
+  - Smart handling of srcset attribute
+- External content handling:
+  - Made external link exclusion optional (disabled by default)
+  - Improved detection and handling of social media links
+  - Better control over external image filtering
+
+### Fixed
+- Image extraction reliability with multiple source attribute checks
+- External link and image handling logic for better accuracy
+
+### Developer Notes
+- The new `ContentCleaningStrategy` uses configurable thresholds for customization
+- Proxy configuration now supports more complex authentication scenarios
+- Content extraction process now provides both regular and optimized outputs
+
+## [v0.3.72] - 2024-10-20
+
+### Fixed
+- Added support for parsing Base64 encoded images in WebScrappingStrategy
+
+### Added
+- Forked and integrated a customized version of the html2text library for more control over Markdown generation
+- New configuration options for controlling external content:
+  - Ability to exclude all external links
+  - Option to specify domains to exclude (default includes major social media platforms)
+  - Control over excluding external images
+
+### Changed
+- Improved Markdown generation process:
+  - Added fine-grained control over character escaping in Markdown output
+  - Enhanced handling of code blocks and pre-formatted text
+- Updated `AsyncPlaywrightCrawlerStrategy.close()` method to use a shorter sleep time (0.5 seconds instead of 500)
+- Enhanced flexibility in `CosineStrategy` with a more generic `load_HF_embedding_model` function
+
+### Improved
+- Optimized content scraping and processing for better efficiency
+- Enhanced error handling and logging in various components
+
+### Developer Notes
+- The customized html2text library is now located within the crawl4ai package
+- New configuration options are available in the `config.py` file for external content handling
+- The `WebScrappingStrategy` class has been updated to accommodate new external content exclusion options
+
+## [v0.3.71] - 2024-10-19
+
+### Added
+- New chunking strategies:
+  - `OverlappingWindowChunking`: Allows for overlapping chunks of text, useful for maintaining context between chunks.
+  - Enhanced `SlidingWindowChunking`: Improved to handle edge cases and last chunks more effectively.
+
+### Changed
+- Updated `CHUNK_TOKEN_THRESHOLD` in config to 2048 tokens (2^11) for better compatibility with most LLM models.
+- Improved `AsyncPlaywrightCrawlerStrategy.close()` method to use a shorter sleep time (0.5 seconds instead of 500), significantly reducing wait time when closing the crawler.
+- Enhanced flexibility in `CosineStrategy`:
+  - Now uses a more generic `load_HF_embedding_model` function, allowing for easier swapping of embedding models.
+- Updated `JsonCssExtractionStrategy` and `JsonXPATHExtractionStrategy` for better JSON-based extraction.
+
+### Fixed
+- Addressed potential issues with the sliding window chunking strategy to ensure all text is properly chunked.
+
+### Developer Notes
+- Added more comprehensive docstrings to chunking strategies for better code documentation.
+- Removed hardcoded device setting in `CosineStrategy`, now using the automatically detected device.
+- Added a new example in `quickstart_async.py` for generating a knowledge graph from crawled content.
+
+These updates aim to provide more flexibility in text processing, improve performance, and enhance the overall capabilities of the crawl4ai library. The new chunking strategies, in particular, offer more options for handling large texts in various scenarios.
+
+## [v0.3.71] - 2024-10-18
+
+### Changes
+1. **Version Update**:
+   - Updated version number from 0.3.7 to 0.3.71.
+
+2. **Crawler Enhancements**:
+   - Added `sleep_on_close` option to AsyncPlaywrightCrawlerStrategy for delayed browser closure.
+   - Improved context creation with additional options:
+     - Enabled `accept_downloads` and `java_script_enabled`.
+     - Added a cookie to enable cookies by default.
+
+3. **Error Handling Improvements**:
+   - Enhanced error messages in AsyncWebCrawler's `arun` method.
+   - Updated error reporting format for better visibility and consistency.
+
+4. **Performance Optimization**:
+   - Commented out automatic page and context closure in `crawl` method to potentially improve performance in certain scenarios.
+
+### Documentation
+- Updated quickstart notebook:
+  - Changed installation command to use the released package instead of GitHub repository.
+  - Updated kernel display name.
+
+### Developer Notes
+- Minor code refactoring and cleanup.
+
+## [v0.3.7] - 2024-10-17
+
+### New Features
+1. **Enhanced Browser Stealth**: 
+   - Implemented `playwright_stealth` for improved bot detection avoidance.
+   - Added `StealthConfig` for fine-tuned control over stealth parameters.
+
+2. **User Simulation**:
+   - New `simulate_user` option to mimic human-like interactions (mouse movements, clicks, keyboard presses).
+
+3. **Navigator Override**:
+   - Added `override_navigator` option to modify navigator properties, further improving bot detection evasion.
+
+4. **Improved iframe Handling**:
+   - New `process_iframes` parameter to extract and integrate iframe content into the main page.
+
+5. **Flexible Browser Selection**:
+   - Support for choosing between Chromium, Firefox, and WebKit browsers.
+
+6. **Include Links in Markdown**:
+    - Added support for including links in Markdown content, by definin g a new flag `include_links_on_markdown` in `crawl` method.   
+
+### Improvements
+1. **Better Error Handling**:
+   - Enhanced error reporting in WebScrappingStrategy with detailed error messages and suggestions.
+   - Added console message and error logging for better debugging.
+
+2. **Image Processing Enhancements**:
+   - Improved image dimension updating and filtering logic.
+
+3. **Crawling Flexibility**:
+   - Added support for custom viewport sizes.
+   - Implemented delayed content retrieval with `delay_before_return_html` parameter.
+
+4. **Performance Optimization**:
+   - Adjusted default semaphore count for parallel crawling.
+
+### Bug Fixes
+- Fixed an issue where the HTML content could be empty after processing.
+
+### Examples
+- Added new example `crawl_with_user_simulation()` demonstrating the use of user simulation and navigator override features.
+
+### Developer Notes
+- Refactored code for better maintainability and readability.
+- Updated browser launch arguments for improved compatibility and performance.
+
+## [v0.3.6] - 2024-10-12 
+
+### 1. Improved Crawling Control
+- **New Hook**: Added `before_retrieve_html` hook in `AsyncPlaywrightCrawlerStrategy`.
+- **Delayed HTML Retrieval**: Introduced `delay_before_return_html` parameter to allow waiting before retrieving HTML content.
+  - Useful for pages with delayed content loading.
+- **Flexible Timeout**: `smart_wait` function now uses `page_timeout` (default 60 seconds) instead of a fixed 30-second timeout.
+  - Provides better handling for slow-loading pages.
+- **How to use**: Set `page_timeout=your_desired_timeout` (in milliseconds) when calling `crawler.arun()`.
+
+### 2. Browser Type Selection
+- Added support for different browser types (Chromium, Firefox, WebKit).
+- Users can now specify the browser type when initializing AsyncWebCrawler.
+- **How to use**: Set `browser_type="firefox"` or `browser_type="webkit"` when initializing AsyncWebCrawler.
+
+### 3. Screenshot Capture
+- Added ability to capture screenshots during crawling.
+- Useful for debugging and content verification.
+- **How to use**: Set `screenshot=True` when calling `crawler.arun()`.
+
+### 4. Enhanced LLM Extraction Strategy
+- Added support for multiple LLM providers (OpenAI, Hugging Face, Ollama).
+- **Custom Arguments**: Added support for passing extra arguments to LLM providers via `extra_args` parameter.
+- **Custom Headers**: Users can now pass custom headers to the extraction strategy.
+- **How to use**: Specify the desired provider and custom arguments when using `LLMExtractionStrategy`.
+
+### 5. iframe Content Extraction
+- New feature to process and extract content from iframes.
+- **How to use**: Set `process_iframes=True` in the crawl method.
+
+### 6. Delayed Content Retrieval
+- Introduced `get_delayed_content` method in `AsyncCrawlResponse`.
+- Allows retrieval of content after a specified delay, useful for dynamically loaded content.
+- **How to use**: Access `result.get_delayed_content(delay_in_seconds)` after crawling.
+
+## Improvements and Optimizations
+
+### 1. AsyncWebCrawler Enhancements
+- **Flexible Initialization**: Now accepts arbitrary keyword arguments, passed directly to the crawler strategy.
+- Allows for more customized setups.
+
+### 2. Image Processing Optimization
+- Enhanced image handling in WebScrappingStrategy.
+- Added filtering for small, invisible, or irrelevant images.
+- Improved image scoring system for better content relevance.
+- Implemented JavaScript-based image dimension updating for more accurate representation.
+
+### 3. Database Schema Auto-updates
+- Automatic database schema updates ensure compatibility with the latest version.
+
+### 4. Enhanced Error Handling and Logging
+- Improved error messages and logging for easier debugging.
+
+### 5. Content Extraction Refinements
+- Refined HTML sanitization process.
+- Improved handling of base64 encoded images.
+- Enhanced Markdown conversion process.
+- Optimized content extraction algorithms.
+
+### 6. Utility Function Enhancements
+- `perform_completion_with_backoff` function now supports additional arguments for more customized API calls to LLM providers.
+
+## Bug Fixes
+- Fixed an issue where image tags were being prematurely removed during content extraction.
+
+## Examples and Documentation
+- Updated `quickstart_async.py` with examples of:
+  - Using custom headers in LLM extraction.
+  - Different LLM provider usage (OpenAI, Hugging Face, Ollama).
+  - Custom browser type usage.
+
+## Developer Notes
+- Refactored code for better maintainability, flexibility, and performance.
+- Enhanced type hinting throughout the codebase for improved development experience.
+- Expanded error handling for more robust operation.
+
+These updates significantly enhance the flexibility, accuracy, and robustness of crawl4ai, providing users with more control and options for their web crawling and content extraction tasks.
+
+## [v0.3.5] - 2024-09-02
+
+Enhance AsyncWebCrawler with smart waiting and screenshot capabilities
+
+- Implement smart_wait function in AsyncPlaywrightCrawlerStrategy
+- Add screenshot support to AsyncCrawlResponse and AsyncWebCrawler
+- Improve error handling and timeout management in crawling process
+- Fix typo in CrawlResult model (responser_headers -> response_headers)
+
 ## [v0.2.77] - 2024-08-04
 
 Significant improvements in text processing and performance:

CONTRIBUTORS.md

@@ -6,12 +6,14 @@ We would like to thank the following people for their contributions to Crawl4AI:
 
 - [Unclecode](https://github.com/unclecode) - Project Creator and Main Developer
 - [Nasrin](https://github.com/ntohidi) - Project Manager and Developer
+- [Aravind Karnam](https://github.com/aravindkarnam) - Developer
 
 ## Community Contributors
 
-- [Aravind Karnam](https://github.com/aravindkarnam) - Developed textual description extraction feature
 - [FractalMind](https://github.com/FractalMind) - Created the first official Docker Hub image and fixed Dockerfile errors
 - [ketonkss4](https://github.com/ketonkss4) - Identified Selenium's new capabilities, helping reduce dependencies
+- [jonymusky](https://github.com/jonymusky) - Javascript execution documentation, and wait_for
+- [datehoer](https://github.com/datehoer) - Add browser prxy support
 
 ## Other Contributors
 
@@ -19,7 +21,6 @@ We would like to thank the following people for their contributions to Crawl4AI:
 - [Shiv Kumar](https://github.com/shivkumar0757)
 - [QIN2DIM](https://github.com/QIN2DIM)
 
-
 ## Acknowledgements
 
 We also want to thank all the users who have reported bugs, suggested features, or helped in any other way to make Crawl4AI better.

Dockerfile

@@ -1,67 +0,0 @@
-# First stage: Build and install dependencies
-FROM python:3.10-slim-bookworm
-
-# Set the working directory in the container
-WORKDIR /usr/src/app
-
-# Define build arguments
-ARG INSTALL_OPTION=default
-
-# Install build dependencies
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-    wget \
-    git \
-    curl \
-    unzip \
-    gnupg \
-    xvfb \
-    ca-certificates \
-    apt-transport-https \
-    software-properties-common && \
-    rm -rf /var/lib/apt/lists/*    
-
-# Copy the application code
-COPY . .
-
-# Install Crawl4AI using the local setup.py with the specified option
-# and download models only for torch, transformer, or all options
-RUN if [ "$INSTALL_OPTION" = "all" ]; then \
-        pip install --no-cache-dir .[all] && \
-        crawl4ai-download-models; \
-    elif [ "$INSTALL_OPTION" = "torch" ]; then \
-        pip install --no-cache-dir .[torch] && \
-        crawl4ai-download-models; \
-    elif [ "$INSTALL_OPTION" = "transformer" ]; then \
-        pip install --no-cache-dir .[transformer] && \
-        crawl4ai-download-models; \
-    else \
-        pip install --no-cache-dir .; \
-    fi
-
-# Install Google Chrome
-RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - && \
-    sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list' && \
-    apt-get update && \
-    apt-get install -y google-chrome-stable
-
-# Set environment to use Chrome properly
-ENV CHROME_BIN=/usr/bin/google-chrome \
-    DISPLAY=:99 \
-    DBUS_SESSION_BUS_ADDRESS=/dev/null \
-    PYTHONUNBUFFERED=1
-
-# Ensure the PATH environment variable includes the location of the installed packages
-ENV PATH=/opt/conda/bin:$PATH   
-
-# Make port 80 available to the world outside this container
-EXPOSE 80
-
-# Install mkdocs
-RUN pip install mkdocs mkdocs-terminal
-
-# Call mkdocs to build the documentation
-RUN mkdocs build
-
-# Run uvicorn
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]

Dockerfile_mac

@@ -1,44 +0,0 @@
-# Use an official Python runtime as a parent image
-FROM python:3.10-slim
-
-# Set the working directory in the container
-WORKDIR /usr/src/app
-
-# Copy the current directory contents into the container at /usr/src/app
-COPY . .
-
-# Install any needed packages specified in requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Install dependencies for Chrome and ChromeDriver
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    wget \
-    xvfb \
-    unzip \
-    curl \
-    gnupg2 \
-    ca-certificates \
-    apt-transport-https \
-    software-properties-common \
-    && wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \
-    && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list \
-    && apt-get update \
-    && apt-get install -y google-chrome-stable \
-    && rm -rf /var/lib/apt/lists/* \
-    && apt install chromium-chromedriver -y
-
-# Install spacy library using pip
-RUN pip install spacy
-
-# Set display port and dbus env to avoid hanging
-ENV DISPLAY=:99
-ENV DBUS_SESSION_BUS_ADDRESS=/dev/null
-
-# Make port 80 available to the world outside this container
-EXPOSE 80
-
-# Define environment variable
-ENV PYTHONUNBUFFERED 1
-
-# Run uvicorn
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]

MANIFEST.in

@@ -0,0 +1 @@
+include requirements.txt

README.md

@@ -1,4 +1,4 @@
-# Crawl4AI v0.2.77 🕷️🤖
+# Crawl4AI (Async Version) 🕷️🤖
 
 [![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
 [![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
@@ -6,216 +6,369 @@
 [![GitHub Pull Requests](https://img.shields.io/github/issues-pr/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/pulls)
 [![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
 
-Crawl4AI simplifies web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
+Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
 
-#### [v0.2.77] - 2024-08-02
+## New in 0.3.72 ✨
 
-Major improvements in functionality, performance, and cross-platform compatibility! 🚀
+- 📄 Fit markdown generation for extracting main article content.
+- 🪄 Magic mode for comprehensive anti-bot detection bypass.
+- 🌐 Enhanced multi-browser support with seamless switching (Chromium, Firefox, WebKit)
+- 📚 New chunking strategies(Sliding window, Overlapping window, Flexible size control)
+- 💾 Improved caching system for better performance
+- ⚡ Optimized batch processing with automatic rate limiting
 
-- 🐳 **Docker enhancements**:
-  - Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
-- 🌐 **Official Docker Hub image**:
-  - Launched our first official image on Docker Hub for streamlined deployment (unclecode/crawl4ai).
-- 🔧 **Selenium upgrade**:
-  - Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
-- 🖼️ **Image description**:
-  - Implemented ability to generate textual descriptions for extracted images from web pages.
-- ⚡ **Performance boost**:
-  - Various improvements to enhance overall speed and performance.
-  
 ## Try it Now!
 
-✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sJPAmeLj5PMrg2VgOwMJ2ubGIcK0cJeX?usp=sharing)
+✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1REChY6fXQf-EaVYLv0eHEWvzlYxGm0pd?usp=sharing)
 
-✨ visit our [Documentation Website](https://crawl4ai.com/mkdocs/)
-
-✨ Check [Demo](https://crawl4ai.com/mkdocs/demo)
+✨ Visit our [Documentation Website](https://crawl4ai.com/mkdocs/)
 
 ## Features ✨
 
 - 🆓 Completely free and open-source
+- 🚀 Blazing fast performance, outperforming many paid services
 - 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown)
+- 🌐 Multi-browser support (Chromium, Firefox, WebKit)
 - 🌍 Supports crawling multiple URLs simultaneously
 - 🎨 Extracts and returns all media tags (Images, Audio, and Video)
 - 🔗 Extracts all external and internal links
 - 📚 Extracts metadata from the page
-- 🔄 Custom hooks for authentication, headers, and page modifications before crawling
+- 🔄 Custom hooks for authentication, headers, and page modifications
 - 🕵️ User-agent customization
-- 🖼️ Takes screenshots of the page
+- 🖼️ Takes screenshots of pages with enhanced error handling
 - 📜 Executes multiple custom JavaScripts before crawling
+- 📊 Generates structured output without LLM using JsonCssExtractionStrategy
 - 📚 Various chunking strategies: topic-based, regex, sentence, and more
 - 🧠 Advanced extraction strategies: cosine clustering, LLM, and more
-- 🎯 CSS selector support
+- 🎯 CSS selector support for precise data extraction
 - 📝 Passes instructions/keywords to refine extraction
+- 🔒 Proxy support with authentication for enhanced access
+- 🔄 Session management for complex multi-page crawling
+- 🌐 Asynchronous architecture for improved performance
+- 🖼️ Improved image processing with lazy-loading detection
+- 🕰️ Enhanced handling of delayed content loading
+- 🔑 Custom headers support for LLM interactions
+- 🖼️ iframe content extraction for comprehensive analysis
+- ⏱️ Flexible timeout and delayed content retrieval options
 
-# Crawl4AI
+## Installation 🛠️
 
-## 🌟 Shoutout to Contributors of v0.2.77!
-
-A big thank you to the amazing contributors who've made this release possible:
-
-- [@aravindkarnam](https://github.com/aravindkarnam) for the new image description feature
-- [@FractalMind](https://github.com/FractalMind) for our official Docker Hub image
-- [@ketonkss4](https://github.com/ketonkss4) for helping streamline our Selenium setup
-
-Your contributions are driving Crawl4AI forward! 🚀
-
-## Cool Examples 🚀
-
-### Quick Start
-
-```python
-from crawl4ai import WebCrawler
-
-# Create an instance of WebCrawler
-crawler = WebCrawler()
-
-# Warm up the crawler (load necessary models)
-crawler.warmup()
-
-# Run the crawler on a URL
-result = crawler.run(url="https://www.nbcnews.com/business")
-
-# Print the extracted content
-print(result.markdown)
-```
-
-## How to install 🛠 
+Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
 
 ### Using pip 🐍
+
+Choose the installation option that best fits your needs:
+
+#### Basic Installation
+
+For basic web crawling and scraping tasks:
+
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai
+```
+
+By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
+
+👉 Note: When you install Crawl4AI, the setup script should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:
+
+1. Through the command line:
+   ```bash
+   playwright install
+   ```
+
+2. If the above doesn't work, try this more specific command:
+   ```bash
+   python -m playwright install chromium
+   ```
+
+This second method has proven to be more reliable in some cases.
+
+#### Installation with Synchronous Version
+
+If you need the synchronous version using Selenium:
+
+```bash
+pip install crawl4ai[sync]
+```
+
+#### Development Installation
+
+For contributors who plan to modify the source code:
+
+```bash
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e .
 ```
 
 ### Using Docker 🐳
 
-```bash
-# For Mac users (M1/M2)
-# docker build --platform linux/amd64 -t crawl4ai .
-docker build -t crawl4ai .
-docker run -d -p 8000:80 crawl4ai
-```
+We're in the process of creating Docker images and pushing them to Docker Hub. This will provide an easy way to run Crawl4AI in a containerized environment. Stay tuned for updates!
 
-### Using Docker Hub 🐳
+For more detailed installation instructions and options, please refer to our [Installation Guide](https://crawl4ai.com/mkdocs/installation).
 
-```bash
-docker pull unclecode/crawl4ai:latest
-docker run -d -p 8000:80 unclecode/crawl4ai:latest
-```
-
-
-## Speed-First Design 🚀
-
-Perhaps the most important design principle for this library is speed. We need to ensure it can handle many links and resources in parallel as quickly as possible. By combining this speed with fast LLMs like Groq, the results will be truly amazing.
+## Quick Start 🚀
 
 ```python
-import time
-from crawl4ai.web_crawler import WebCrawler
-crawler = WebCrawler()
-crawler.warmup()
+import asyncio
+from crawl4ai import AsyncWebCrawler
 
-start = time.time()
-url = r"https://www.nbcnews.com/business"
-result = crawler.run( url, word_count_threshold=10, bypass_cache=True)
-end = time.time()
-print(f"Time taken: {end - start}")
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(url="https://www.nbcnews.com/business")
+        print(result.markdown)
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
-Let's take a look the calculated time for the above code snippet:
+## Advanced Usage 🔬
 
-```bash
-[LOG] 🚀 Crawling done, success: True, time taken: 1.3623387813568115 seconds
-[LOG] 🚀 Content extracted, success: True, time taken: 0.05715131759643555 seconds
-[LOG] 🚀 Extraction, time taken: 0.05750393867492676 seconds.
-Time taken: 1.439958095550537
+### Executing JavaScript and Using CSS Selectors
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        js_code = ["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"]
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            css_selector=".wide-tease-item__description",
+            bypass_cache=True
+        )
+        print(result.extracted_content)
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
-Fetching the content from the page took 1.3623 seconds, and extracting the content took 0.0575 seconds. 🚀
 
-### Extract Structured Data from Web Pages 📊
+### Using a Proxy
 
-Crawl all OpenAI models and their fees from the official page.
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    async with AsyncWebCrawler(verbose=True, proxy="http://127.0.0.1:7890") as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            bypass_cache=True
+        )
+        print(result.markdown)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Extracting Structured Data without LLM
+
+The `JsonCssExtractionStrategy` allows for precise extraction of structured data from web pages using CSS selectors.
+
+```python
+import asyncio
+import json
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def extract_news_teasers():
+    schema = {
+        "name": "News Teaser Extractor",
+        "baseSelector": ".wide-tease-item__wrapper",
+        "fields": [
+            {
+                "name": "category",
+                "selector": ".unibrow span[data-testid='unibrow-text']",
+                "type": "text",
+            },
+            {
+                "name": "headline",
+                "selector": ".wide-tease-item__headline",
+                "type": "text",
+            },
+            {
+                "name": "summary",
+                "selector": ".wide-tease-item__description",
+                "type": "text",
+            },
+            {
+                "name": "time",
+                "selector": "[data-testid='wide-tease-date']",
+                "type": "text",
+            },
+            {
+                "name": "image",
+                "type": "nested",
+                "selector": "picture.teasePicture img",
+                "fields": [
+                    {"name": "src", "type": "attribute", "attribute": "src"},
+                    {"name": "alt", "type": "attribute", "attribute": "alt"},
+                ],
+            },
+            {
+                "name": "link",
+                "selector": "a[href]",
+                "type": "attribute",
+                "attribute": "href",
+            },
+        ],
+    }
+
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            extraction_strategy=extraction_strategy,
+            bypass_cache=True,
+        )
+
+        assert result.success, "Failed to crawl the page"
+
+        news_teasers = json.loads(result.extracted_content)
+        print(f"Successfully extracted {len(news_teasers)} news teasers")
+        print(json.dumps(news_teasers[0], indent=2))
+
+if __name__ == "__main__":
+    asyncio.run(extract_news_teasers())
+```
+
+For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/full_details/advanced_jsoncss_extraction.md) section in the documentation.
+
+### Extracting Structured Data with OpenAI
 
 ```python
 import os
-from crawl4ai import WebCrawler
+import asyncio
+from crawl4ai import AsyncWebCrawler
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 from pydantic import BaseModel, Field
 
 class OpenAIModelFee(BaseModel):
     model_name: str = Field(..., description="Name of the OpenAI model.")
     input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
-    output_fee: str = Field(..., description="Fee for output token ßfor the OpenAI model.")
+    output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
 
-url = 'https://openai.com/api/pricing/'
-crawler = WebCrawler()
-crawler.warmup()
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url='https://openai.com/api/pricing/',
+            word_count_threshold=1,
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'), 
+                schema=OpenAIModelFee.schema(),
+                extraction_type="schema",
+                instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
+                Do not miss any models in the entire content. One extracted model JSON format should look like this: 
+                {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
+            ),            
+            bypass_cache=True,
+        )
+        print(result.extracted_content)
 
-result = crawler.run(
-        url=url,
-        word_count_threshold=1,
-        extraction_strategy= LLMExtractionStrategy(
-            provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
-            schema=OpenAIModelFee.schema(),
-            extraction_type="schema",
-            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
-            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
-            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
-        ),            
-        bypass_cache=True,
-    )
-
-print(result.extracted_content)
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
-### Execute JS, Filter Data with CSS Selector, and Clustering
+### Session Management and Dynamic Content Crawling
+
+Crawl4AI excels at handling complex scenarios, such as crawling multiple pages with dynamic content loaded via JavaScript. Here's an example of crawling GitHub commits across multiple pages:
 
 ```python
-from crawl4ai import WebCrawler
-from crawl4ai.chunking_strategy import CosineStrategy
+import asyncio
+import re
+from bs4 import BeautifulSoup
+from crawl4ai import AsyncWebCrawler
 
-js_code = ["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"]
+async def crawl_typescript_commits():
+    first_commit = ""
+    async def on_execution_started(page):
+        nonlocal first_commit 
+        try:
+            while True:
+                await page.wait_for_selector('li.Box-sc-g0xbh4-0 h4')
+                commit = await page.query_selector('li.Box-sc-g0xbh4-0 h4')
+                commit = await commit.evaluate('(element) => element.textContent')
+                commit = re.sub(r'\s+', '', commit)
+                if commit and commit != first_commit:
+                    first_commit = commit
+                    break
+                await asyncio.sleep(0.5)
+        except Exception as e:
+            print(f"Warning: New content didn't appear after JavaScript execution: {e}")
 
-crawler = WebCrawler()
-crawler.warmup()
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        crawler.crawler_strategy.set_hook('on_execution_started', on_execution_started)
 
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    js=js_code,
-    css_selector="p",
-    extraction_strategy=CosineStrategy(semantic_filter="technology")
-)
+        url = "https://github.com/microsoft/TypeScript/commits/main"
+        session_id = "typescript_commits_session"
+        all_commits = []
 
-print(result.extracted_content)
+        js_next_page = """
+        const button = document.querySelector('a[data-testid="pagination-next-button"]');
+        if (button) button.click();
+        """
+
+        for page in range(3):  # Crawl 3 pages
+            result = await crawler.arun(
+                url=url,
+                session_id=session_id,
+                css_selector="li.Box-sc-g0xbh4-0",
+                js=js_next_page if page > 0 else None,
+                bypass_cache=True,
+                js_only=page > 0
+            )
+
+            assert result.success, f"Failed to crawl page {page + 1}"
+
+            soup = BeautifulSoup(result.cleaned_html, 'html.parser')
+            commits = soup.select("li")
+            all_commits.extend(commits)
+
+            print(f"Page {page + 1}: Found {len(commits)} commits")
+
+        await crawler.crawler_strategy.kill_session(session_id)
+        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
+
+if __name__ == "__main__":
+    asyncio.run(crawl_typescript_commits())
 ```
 
-### Extract Structured Data from Web Pages With Proxy and BaseUrl
+This example demonstrates Crawl4AI's ability to handle complex scenarios where content is loaded asynchronously. It crawls multiple pages of GitHub commits, executing JavaScript to load new content and using custom hooks to ensure data is loaded before proceeding.
 
-```python
-from crawl4ai import WebCrawler
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
+For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/full_details/session_based_crawling.md) section in the documentation.
 
-def create_crawler():
-    crawler = WebCrawler(verbose=True, proxy="http://127.0.0.1:7890")
-    crawler.warmup()
-    return crawler
 
-crawler = create_crawler()
+## Speed Comparison 🚀
 
-crawler.warmup()
+Crawl4AI is designed with speed as a primary focus. Our goal is to provide the fastest possible response with high-quality data extraction, minimizing abstractions between the data and the user.
 
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    extraction_strategy=LLMExtractionStrategy(
-        provider="openai/gpt-4o",
-        api_token="sk-",
-        base_url="https://api.openai.com/v1"
-    )
-)
+We've conducted a speed comparison between Crawl4AI and Firecrawl, a paid service. The results demonstrate Crawl4AI's superior performance:
 
-print(result.markdown)
 ```
+Firecrawl:
+Time taken: 7.02 seconds
+Content length: 42074 characters
+Images found: 49
+
+Crawl4AI (simple crawl):
+Time taken: 1.60 seconds
+Content length: 18238 characters
+Images found: 49
+
+Crawl4AI (with JavaScript execution):
+Time taken: 4.64 seconds
+Content length: 40869 characters
+Images found: 89
+```
+
+As you can see, Crawl4AI outperforms Firecrawl significantly:
+- Simple crawl: Crawl4AI is over 4 times faster than Firecrawl.
+- With JavaScript execution: Even when executing JavaScript to load more content (doubling the number of images found), Crawl4AI is still faster than Firecrawl's simple crawl.
+
+You can find the full comparison code in our repository at `docs/examples/crawl4ai_vs_firecrawl.py`.
 
 ## Documentation 📚
 

README.sync.md

@@ -0,0 +1,244 @@
+# Crawl4AI v0.2.77 🕷️🤖
+
+[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
+[![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
+[![GitHub Issues](https://img.shields.io/github/issues/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/issues)
+[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/pulls)
+[![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
+
+Crawl4AI simplifies web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
+
+#### [v0.2.77] - 2024-08-02
+
+Major improvements in functionality, performance, and cross-platform compatibility! 🚀
+
+- 🐳 **Docker enhancements**:
+  - Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
+- 🌐 **Official Docker Hub image**:
+  - Launched our first official image on Docker Hub for streamlined deployment (unclecode/crawl4ai).
+- 🔧 **Selenium upgrade**:
+  - Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
+- 🖼️ **Image description**:
+  - Implemented ability to generate textual descriptions for extracted images from web pages.
+- ⚡ **Performance boost**:
+  - Various improvements to enhance overall speed and performance.
+  
+## Try it Now!
+
+✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sJPAmeLj5PMrg2VgOwMJ2ubGIcK0cJeX?usp=sharing)
+
+✨ visit our [Documentation Website](https://crawl4ai.com/mkdocs/)
+
+✨ Check [Demo](https://crawl4ai.com/mkdocs/demo)
+
+## Features ✨
+
+- 🆓 Completely free and open-source
+- 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown)
+- 🌍 Supports crawling multiple URLs simultaneously
+- 🎨 Extracts and returns all media tags (Images, Audio, and Video)
+- 🔗 Extracts all external and internal links
+- 📚 Extracts metadata from the page
+- 🔄 Custom hooks for authentication, headers, and page modifications before crawling
+- 🕵️ User-agent customization
+- 🖼️ Takes screenshots of the page
+- 📜 Executes multiple custom JavaScripts before crawling
+- 📚 Various chunking strategies: topic-based, regex, sentence, and more
+- 🧠 Advanced extraction strategies: cosine clustering, LLM, and more
+- 🎯 CSS selector support
+- 📝 Passes instructions/keywords to refine extraction
+
+# Crawl4AI
+
+## 🌟 Shoutout to Contributors of v0.2.77!
+
+A big thank you to the amazing contributors who've made this release possible:
+
+- [@aravindkarnam](https://github.com/aravindkarnam) for the new image description feature
+- [@FractalMind](https://github.com/FractalMind) for our official Docker Hub image
+- [@ketonkss4](https://github.com/ketonkss4) for helping streamline our Selenium setup
+
+Your contributions are driving Crawl4AI forward! 🚀
+
+## Cool Examples 🚀
+
+### Quick Start
+
+```python
+from crawl4ai import WebCrawler
+
+# Create an instance of WebCrawler
+crawler = WebCrawler()
+
+# Warm up the crawler (load necessary models)
+crawler.warmup()
+
+# Run the crawler on a URL
+result = crawler.run(url="https://www.nbcnews.com/business")
+
+# Print the extracted content
+print(result.markdown)
+```
+
+## How to install 🛠 
+
+### Using pip 🐍
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+```
+
+### Using Docker 🐳
+
+```bash
+# For Mac users (M1/M2)
+# docker build --platform linux/amd64 -t crawl4ai .
+docker build -t crawl4ai .
+docker run -d -p 8000:80 crawl4ai
+```
+
+### Using Docker Hub 🐳
+
+```bash
+docker pull unclecode/crawl4ai:latest
+docker run -d -p 8000:80 unclecode/crawl4ai:latest
+```
+
+
+## Speed-First Design 🚀
+
+Perhaps the most important design principle for this library is speed. We need to ensure it can handle many links and resources in parallel as quickly as possible. By combining this speed with fast LLMs like Groq, the results will be truly amazing.
+
+```python
+import time
+from crawl4ai.web_crawler import WebCrawler
+crawler = WebCrawler()
+crawler.warmup()
+
+start = time.time()
+url = r"https://www.nbcnews.com/business"
+result = crawler.run( url, word_count_threshold=10, bypass_cache=True)
+end = time.time()
+print(f"Time taken: {end - start}")
+```
+
+Let's take a look the calculated time for the above code snippet:
+
+```bash
+[LOG] 🚀 Crawling done, success: True, time taken: 1.3623387813568115 seconds
+[LOG] 🚀 Content extracted, success: True, time taken: 0.05715131759643555 seconds
+[LOG] 🚀 Extraction, time taken: 0.05750393867492676 seconds.
+Time taken: 1.439958095550537
+```
+Fetching the content from the page took 1.3623 seconds, and extracting the content took 0.0575 seconds. 🚀
+
+### Extract Structured Data from Web Pages 📊
+
+Crawl all OpenAI models and their fees from the official page.
+
+```python
+import os
+from crawl4ai import WebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from pydantic import BaseModel, Field
+
+class OpenAIModelFee(BaseModel):
+    model_name: str = Field(..., description="Name of the OpenAI model.")
+    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+    output_fee: str = Field(..., description="Fee for output token ßfor the OpenAI model.")
+
+url = 'https://openai.com/api/pricing/'
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+        url=url,
+        word_count_threshold=1,
+        extraction_strategy= LLMExtractionStrategy(
+            provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
+            schema=OpenAIModelFee.schema(),
+            extraction_type="schema",
+            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
+            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
+            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
+        ),            
+        bypass_cache=True,
+    )
+
+print(result.extracted_content)
+```
+
+### Execute JS, Filter Data with CSS Selector, and Clustering
+
+```python
+from crawl4ai import WebCrawler
+from crawl4ai.chunking_strategy import CosineStrategy
+
+js_code = ["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"]
+
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    js=js_code,
+    css_selector="p",
+    extraction_strategy=CosineStrategy(semantic_filter="technology")
+)
+
+print(result.extracted_content)
+```
+
+### Extract Structured Data from Web Pages With Proxy and BaseUrl
+
+```python
+from crawl4ai import WebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+def create_crawler():
+    crawler = WebCrawler(verbose=True, proxy="http://127.0.0.1:7890")
+    crawler.warmup()
+    return crawler
+
+crawler = create_crawler()
+
+crawler.warmup()
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=LLMExtractionStrategy(
+        provider="openai/gpt-4o",
+        api_token="sk-",
+        base_url="https://api.openai.com/v1"
+    )
+)
+
+print(result.markdown)
+```
+
+## Documentation 📚
+
+For detailed documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://crawl4ai.com/mkdocs/).
+
+## Contributing 🤝
+
+We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md) for more information.
+
+## License 📄
+
+Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
+
+## Contact 📧
+
+For questions, suggestions, or feedback, feel free to reach out:
+
+- GitHub: [unclecode](https://github.com/unclecode)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+Happy Crawling! 🕸️🚀
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

crawl4ai/__init__.py

@@ -1 +1,30 @@
-from .web_crawler import WebCrawler
+# __init__.py
+
+from .async_webcrawler import AsyncWebCrawler
+from .models import CrawlResult
+
+__version__ = "0.3.72"
+
+__all__ = [
+    "AsyncWebCrawler",
+    "CrawlResult",
+]
+
+def is_sync_version_installed():
+    try:
+        import selenium
+        return True
+    except ImportError:
+        return False
+
+if is_sync_version_installed():
+    try:
+        from .web_crawler import WebCrawler
+        __all__.append("WebCrawler")
+    except ImportError:
+        import warnings
+        print("Warning: Failed to import WebCrawler even though selenium is installed. This might be due to other missing dependencies.")
+else:
+    WebCrawler = None
+    import warnings
+    print("Warning: Synchronous WebCrawler is not available. Install crawl4ai[sync] for synchronous support. However, please note that the synchronous version will be deprecated soon.")

crawl4ai/async_crawler_strategy copy.py

@@ -0,0 +1,558 @@
+import asyncio
+import base64
+import time
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Any, List, Optional, Awaitable
+import os
+from playwright.async_api import async_playwright, Page, Browser, Error
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+from pathlib import Path
+from playwright.async_api import ProxySettings
+from pydantic import BaseModel
+import hashlib
+import json
+import uuid
+from playwright_stealth import stealth_async
+
+class AsyncCrawlResponse(BaseModel):
+    html: str
+    response_headers: Dict[str, str]
+    status_code: int
+    screenshot: Optional[str] = None
+    get_delayed_content: Optional[Callable[[Optional[float]], Awaitable[str]]] = None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+class AsyncCrawlerStrategy(ABC):
+    @abstractmethod
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        pass
+    
+    @abstractmethod
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        pass
+    
+    @abstractmethod
+    async def take_screenshot(self, url: str) -> str:
+        pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
+    
+    @abstractmethod
+    def set_hook(self, hook_type: str, hook: Callable):
+        pass
+
+class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
+    def __init__(self, use_cached_html=False, js_code=None, **kwargs):
+        self.use_cached_html = use_cached_html
+        self.user_agent = kwargs.get(
+            "user_agent",
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+            "(KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        )
+        self.proxy = kwargs.get("proxy")
+        self.headless = kwargs.get("headless", True)
+        self.browser_type = kwargs.get("browser_type", "chromium")
+        self.headers = kwargs.get("headers", {})
+        self.sessions = {}
+        self.session_ttl = 1800 
+        self.js_code = js_code
+        self.verbose = kwargs.get("verbose", False)
+        self.playwright = None
+        self.browser = None
+        self.hooks = {
+            'on_browser_created': None,
+            'on_user_agent_updated': None,
+            'on_execution_started': None,
+            'before_goto': None,
+            'after_goto': None,
+            'before_return_html': None,
+            'before_retrieve_html': None
+        }
+
+    async def __aenter__(self):
+        await self.start()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        await self.close()
+
+    async def start(self):
+        if self.playwright is None:
+            self.playwright = await async_playwright().start()
+        if self.browser is None:
+            browser_args = {
+                "headless": self.headless,
+                "args": [
+                    "--disable-gpu",
+                    "--no-sandbox",
+                    "--disable-dev-shm-usage",
+                    "--disable-blink-features=AutomationControlled",
+                    "--disable-infobars",
+                    "--window-position=0,0",
+                    "--ignore-certificate-errors",
+                    "--ignore-certificate-errors-spki-list",
+                    # "--headless=new",  # Use the new headless mode
+                ]
+            }
+            
+            # Add proxy settings if a proxy is specified
+            if self.proxy:
+                proxy_settings = ProxySettings(server=self.proxy)
+                browser_args["proxy"] = proxy_settings
+                
+            # Select the appropriate browser based on the browser_type
+            if self.browser_type == "firefox":
+                self.browser = await self.playwright.firefox.launch(**browser_args)
+            elif self.browser_type == "webkit":
+                self.browser = await self.playwright.webkit.launch(**browser_args)
+            else:
+                self.browser = await self.playwright.chromium.launch(**browser_args)
+
+            await self.execute_hook('on_browser_created', self.browser)
+
+    async def close(self):
+        if self.browser:
+            await self.browser.close()
+            self.browser = None
+        if self.playwright:
+            await self.playwright.stop()
+            self.playwright = None
+
+    def __del__(self):
+        if self.browser or self.playwright:
+            asyncio.get_event_loop().run_until_complete(self.close())
+
+    def set_hook(self, hook_type: str, hook: Callable):
+        if hook_type in self.hooks:
+            self.hooks[hook_type] = hook
+        else:
+            raise ValueError(f"Invalid hook type: {hook_type}")
+
+    async def execute_hook(self, hook_type: str, *args):
+        hook = self.hooks.get(hook_type)
+        if hook:
+            if asyncio.iscoroutinefunction(hook):
+                return await hook(*args)
+            else:
+                return hook(*args)
+        return args[0] if args else None
+
+    def update_user_agent(self, user_agent: str):
+        self.user_agent = user_agent
+
+    def set_custom_headers(self, headers: Dict[str, str]):
+        self.headers = headers
+
+    async def kill_session(self, session_id: str):
+        if session_id in self.sessions:
+            context, page, _ = self.sessions[session_id]
+            await page.close()
+            await context.close()
+            del self.sessions[session_id]
+
+    def _cleanup_expired_sessions(self):
+        current_time = time.time()
+        expired_sessions = [
+            sid for sid, (_, _, last_used) in self.sessions.items() 
+            if current_time - last_used > self.session_ttl
+        ]
+        for sid in expired_sessions:
+            asyncio.create_task(self.kill_session(sid))
+            
+    async def smart_wait(self, page: Page, wait_for: str, timeout: float = 30000):
+        wait_for = wait_for.strip()
+        
+        if wait_for.startswith('js:'):
+            # Explicitly specified JavaScript
+            js_code = wait_for[3:].strip()
+            return await self.csp_compliant_wait(page, js_code, timeout)
+        elif wait_for.startswith('css:'):
+            # Explicitly specified CSS selector
+            css_selector = wait_for[4:].strip()
+            try:
+                await page.wait_for_selector(css_selector, timeout=timeout)
+            except Error as e:
+                if 'Timeout' in str(e):
+                    raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{css_selector}'")
+                else:
+                    raise ValueError(f"Invalid CSS selector: '{css_selector}'")
+        else:
+            # Auto-detect based on content
+            if wait_for.startswith('()') or wait_for.startswith('function'):
+                # It's likely a JavaScript function
+                return await self.csp_compliant_wait(page, wait_for, timeout)
+            else:
+                # Assume it's a CSS selector first
+                try:
+                    await page.wait_for_selector(wait_for, timeout=timeout)
+                except Error as e:
+                    if 'Timeout' in str(e):
+                        raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{wait_for}'")
+                    else:
+                        # If it's not a timeout error, it might be an invalid selector
+                        # Let's try to evaluate it as a JavaScript function as a fallback
+                        try:
+                            return await self.csp_compliant_wait(page, f"() => {{{wait_for}}}", timeout)
+                        except Error:
+                            raise ValueError(f"Invalid wait_for parameter: '{wait_for}'. "
+                                             "It should be either a valid CSS selector, a JavaScript function, "
+                                             "or explicitly prefixed with 'js:' or 'css:'.")
+    
+    async def csp_compliant_wait(self, page: Page, user_wait_function: str, timeout: float = 30000):
+        wrapper_js = f"""
+        async () => {{
+            const userFunction = {user_wait_function};
+            const startTime = Date.now();
+            while (true) {{
+                if (await userFunction()) {{
+                    return true;
+                }}
+                if (Date.now() - startTime > {timeout}) {{
+                    throw new Error('Timeout waiting for condition');
+                }}
+                await new Promise(resolve => setTimeout(resolve, 100));
+            }}
+        }}
+        """
+        
+        try:
+            await page.evaluate(wrapper_js)
+        except TimeoutError:
+            raise TimeoutError(f"Timeout after {timeout}ms waiting for condition")
+        except Exception as e:
+            raise RuntimeError(f"Error in wait condition: {str(e)}")
+
+    async def process_iframes(self, page):
+        # Find all iframes
+        iframes = await page.query_selector_all('iframe')
+        
+        for i, iframe in enumerate(iframes):
+            try:
+                # Add a unique identifier to the iframe
+                await iframe.evaluate(f'(element) => element.id = "iframe-{i}"')
+                
+                # Get the frame associated with this iframe
+                frame = await iframe.content_frame()
+                
+                if frame:
+                    # Wait for the frame to load
+                    await frame.wait_for_load_state('load', timeout=30000)  # 30 seconds timeout
+                    
+                    # Extract the content of the iframe's body
+                    iframe_content = await frame.evaluate('() => document.body.innerHTML')
+                    
+                    # Generate a unique class name for this iframe
+                    class_name = f'extracted-iframe-content-{i}'
+                    
+                    # Replace the iframe with a div containing the extracted content
+                    _iframe = iframe_content.replace('`', '\\`')
+                    await page.evaluate(f"""
+                        () => {{
+                            const iframe = document.getElementById('iframe-{i}');
+                            const div = document.createElement('div');
+                            div.innerHTML = `{_iframe}`;
+                            div.className = '{class_name}';
+                            iframe.replaceWith(div);
+                        }}
+                    """)
+                else:
+                    print(f"Warning: Could not access content frame for iframe {i}")
+            except Exception as e:
+                print(f"Error processing iframe {i}: {str(e)}")
+
+        # Return the page object
+        return page  
+    
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        response_headers = {}
+        status_code = None
+        
+        self._cleanup_expired_sessions()
+        session_id = kwargs.get("session_id")
+        if session_id:
+            context, page, _ = self.sessions.get(session_id, (None, None, None))
+            if not context:
+                context = await self.browser.new_context(
+                    user_agent=self.user_agent,
+                    viewport={"width": 1920, "height": 1080},
+                    proxy={"server": self.proxy} if self.proxy else None
+                )
+                await context.set_extra_http_headers(self.headers)
+                page = await context.new_page()
+                self.sessions[session_id] = (context, page, time.time())
+        else:
+            context = await self.browser.new_context(
+                user_agent=self.user_agent,
+                viewport={"width": 1920, "height": 1080},
+                proxy={"server": self.proxy} if self.proxy else None
+            )
+            await context.set_extra_http_headers(self.headers)
+            
+            if kwargs.get("override_navigator", False):
+                # Inject scripts to override navigator properties
+                await context.add_init_script("""
+                    // Pass the Permissions Test.
+                    const originalQuery = window.navigator.permissions.query;
+                    window.navigator.permissions.query = (parameters) => (
+                        parameters.name === 'notifications' ?
+                            Promise.resolve({ state: Notification.permission }) :
+                            originalQuery(parameters)
+                    );
+                    Object.defineProperty(navigator, 'webdriver', {
+                        get: () => undefined
+                    });
+                    window.navigator.chrome = {
+                        runtime: {},
+                        // Add other properties if necessary
+                    };
+                    Object.defineProperty(navigator, 'plugins', {
+                        get: () => [1, 2, 3, 4, 5],
+                    });
+                    Object.defineProperty(navigator, 'languages', {
+                        get: () => ['en-US', 'en'],
+                    });
+                    Object.defineProperty(document, 'hidden', {
+                        get: () => false
+                    });
+                    Object.defineProperty(document, 'visibilityState', {
+                        get: () => 'visible'
+                    });
+                """)
+            
+            page = await context.new_page()
+
+        try:
+            if self.verbose:
+                print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                if os.path.exists(cache_file_path):
+                    html = ""
+                    with open(cache_file_path, "r") as f:
+                        html = f.read()
+                    # retrieve response headers and status code from cache
+                    with open(cache_file_path + ".meta", "r") as f:
+                        meta = json.load(f)
+                        response_headers = meta.get("response_headers", {})
+                        status_code = meta.get("status_code")
+                    response = AsyncCrawlResponse(
+                        html=html, response_headers=response_headers, status_code=status_code
+                    )
+                    return response
+
+            if not kwargs.get("js_only", False):
+                await self.execute_hook('before_goto', page)
+                
+                response = await page.goto("about:blank")
+                await stealth_async(page)
+                response = await page.goto(
+                    url, wait_until="domcontentloaded", timeout=kwargs.get("page_timeout", 60000)
+                )
+                
+                # await stealth_async(page)
+                # response = await page.goto("about:blank")
+                # await stealth_async(page)
+                # await page.evaluate(f"window.location.href = '{url}'")
+                
+                await self.execute_hook('after_goto', page)
+                
+                # Get status code and headers
+                status_code = response.status
+                response_headers = response.headers
+            else:
+                status_code = 200
+                response_headers = {}
+
+            await page.wait_for_selector('body')
+            await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+
+            js_code = kwargs.get("js_code", kwargs.get("js", self.js_code))
+            if js_code:
+                if isinstance(js_code, str):
+                    await page.evaluate(js_code)
+                elif isinstance(js_code, list):
+                    for js in js_code:
+                        await page.evaluate(js)
+                
+                await page.wait_for_load_state('networkidle')
+                # Check for on execution event
+                await self.execute_hook('on_execution_started', page)
+                
+            if kwargs.get("simulate_user", False):
+                # Simulate user interactions
+                await page.mouse.move(100, 100)
+                await page.mouse.down()
+                await page.mouse.up()
+                await page.keyboard.press('ArrowDown')
+
+            # Handle the wait_for parameter
+            wait_for = kwargs.get("wait_for")
+            if wait_for:
+                try:
+                    await self.smart_wait(page, wait_for, timeout=kwargs.get("page_timeout", 60000))
+                except Exception as e:
+                    raise RuntimeError(f"Wait condition failed: {str(e)}")
+
+
+            
+            # Update image dimensions
+            update_image_dimensions_js = """
+            () => {
+                return new Promise((resolve) => {
+                    const filterImage = (img) => {
+                        // Filter out images that are too small
+                        if (img.width < 100 && img.height < 100) return false;
+                        
+                        // Filter out images that are not visible
+                        const rect = img.getBoundingClientRect();
+                        if (rect.width === 0 || rect.height === 0) return false;
+                        
+                        // Filter out images with certain class names (e.g., icons, thumbnails)
+                        if (img.classList.contains('icon') || img.classList.contains('thumbnail')) return false;
+                        
+                        // Filter out images with certain patterns in their src (e.g., placeholder images)
+                        if (img.src.includes('placeholder') || img.src.includes('icon')) return false;
+                        
+                        return true;
+                    };
+
+                    const images = Array.from(document.querySelectorAll('img')).filter(filterImage);
+                    let imagesLeft = images.length;
+                    
+                    if (imagesLeft === 0) {
+                        resolve();
+                        return;
+                    }
+
+                    const checkImage = (img) => {
+                        if (img.complete && img.naturalWidth !== 0) {
+                            img.setAttribute('width', img.naturalWidth);
+                            img.setAttribute('height', img.naturalHeight);
+                            imagesLeft--;
+                            if (imagesLeft === 0) resolve();
+                        }
+                    };
+
+                    images.forEach(img => {
+                        checkImage(img);
+                        if (!img.complete) {
+                            img.onload = () => {
+                                checkImage(img);
+                            };
+                            img.onerror = () => {
+                                imagesLeft--;
+                                if (imagesLeft === 0) resolve();
+                            };
+                        }
+                    });
+
+                    // Fallback timeout of 5 seconds
+                    setTimeout(() => resolve(), 5000);
+                });
+            }
+            """
+            await page.evaluate(update_image_dimensions_js)
+
+            # Wait a bit for any onload events to complete
+            await page.wait_for_timeout(100)
+
+            # Process iframes
+            if kwargs.get("process_iframes", False):
+                page = await self.process_iframes(page)
+            
+            await self.execute_hook('before_retrieve_html', page)
+            # Check if delay_before_return_html is set then wait for that time
+            delay_before_return_html = kwargs.get("delay_before_return_html")
+            if delay_before_return_html:
+                await asyncio.sleep(delay_before_return_html)
+                
+            html = await page.content()
+            await self.execute_hook('before_return_html', page, html)
+            
+            # Check if kwargs has screenshot=True then take screenshot
+            screenshot_data = None
+            if kwargs.get("screenshot"):
+                screenshot_data = await self.take_screenshot(url)            
+
+            if self.verbose:
+                print(f"[LOG] ✅ Crawled {url} successfully!")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                with open(cache_file_path, "w", encoding="utf-8") as f:
+                    f.write(html)
+                # store response headers and status code in cache
+                with open(cache_file_path + ".meta", "w", encoding="utf-8") as f:
+                    json.dump({
+                        "response_headers": response_headers,
+                        "status_code": status_code
+                    }, f)
+
+            async def get_delayed_content(delay: float = 5.0) -> str:
+                if self.verbose:
+                    print(f"[LOG] Waiting for {delay} seconds before retrieving content for {url}")
+                await asyncio.sleep(delay)
+                return await page.content()
+                
+            response = AsyncCrawlResponse(
+                html=html, 
+                response_headers=response_headers, 
+                status_code=status_code,
+                screenshot=screenshot_data,
+                get_delayed_content=get_delayed_content
+            )
+            return response
+        except Error as e:
+            raise Error(f"Failed to crawl {url}: {str(e)}")
+        finally:
+            if not session_id:
+                await page.close()
+                await context.close()
+
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        semaphore_count = kwargs.get('semaphore_count', 5)  # Adjust as needed
+        semaphore = asyncio.Semaphore(semaphore_count)
+
+        async def crawl_with_semaphore(url):
+            async with semaphore:
+                return await self.crawl(url, **kwargs)
+
+        tasks = [crawl_with_semaphore(url) for url in urls]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        return [result if not isinstance(result, Exception) else str(result) for result in results]
+
+    async def take_screenshot(self, url: str, wait_time=1000) -> str:
+        async with await self.browser.new_context(user_agent=self.user_agent) as context:
+            page = await context.new_page()
+            try:
+                await page.goto(url, wait_until="domcontentloaded", timeout=30000)
+                # Wait for a specified time (default is 1 second)
+                await page.wait_for_timeout(wait_time)
+                screenshot = await page.screenshot(full_page=True)
+                return base64.b64encode(screenshot).decode('utf-8')
+            except Exception as e:
+                error_message = f"Failed to take screenshot: {str(e)}"
+                print(error_message)
+
+                # Generate an error image
+                img = Image.new('RGB', (800, 600), color='black')
+                draw = ImageDraw.Draw(img)
+                font = ImageFont.load_default()
+                draw.text((10, 10), error_message, fill=(255, 255, 255), font=font)
+                
+                buffered = BytesIO()
+                img.save(buffered, format="JPEG")
+                return base64.b64encode(buffered.getvalue()).decode('utf-8')
+            finally:
+                await page.close()
+

crawl4ai/async_crawler_strategy.py

@@ -0,0 +1,722 @@
+import asyncio
+import base64
+import time
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Any, List, Optional, Awaitable
+import os
+from playwright.async_api import async_playwright, Page, Browser, Error
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+from pathlib import Path
+from playwright.async_api import ProxySettings
+from pydantic import BaseModel
+import hashlib
+import json
+import uuid
+from playwright_stealth import StealthConfig, stealth_async
+
+stealth_config = StealthConfig(
+    webdriver=True,
+    chrome_app=True,
+    chrome_csi=True,
+    chrome_load_times=True,
+    chrome_runtime=True,
+    navigator_languages=True,
+    navigator_plugins=True,
+    navigator_permissions=True,
+    webgl_vendor=True,
+    outerdimensions=True,
+    navigator_hardware_concurrency=True,
+    media_codecs=True,
+)
+
+
+class AsyncCrawlResponse(BaseModel):
+    html: str
+    response_headers: Dict[str, str]
+    status_code: int
+    screenshot: Optional[str] = None
+    get_delayed_content: Optional[Callable[[Optional[float]], Awaitable[str]]] = None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+class AsyncCrawlerStrategy(ABC):
+    @abstractmethod
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        pass
+    
+    @abstractmethod
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        pass
+    
+    @abstractmethod
+    async def take_screenshot(self, **kwargs) -> str:
+        pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
+    
+    @abstractmethod
+    def set_hook(self, hook_type: str, hook: Callable):
+        pass
+
+class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
+    def __init__(self, use_cached_html=False, js_code=None, **kwargs):
+        self.use_cached_html = use_cached_html
+        self.user_agent = kwargs.get(
+            "user_agent",
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+            "(KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        )
+        self.proxy = kwargs.get("proxy")
+        self.proxy_config = kwargs.get("proxy_config")
+        self.headless = kwargs.get("headless", True)
+        self.browser_type = kwargs.get("browser_type", "chromium")
+        self.headers = kwargs.get("headers", {})
+        self.sessions = {}
+        self.session_ttl = 1800 
+        self.js_code = js_code
+        self.verbose = kwargs.get("verbose", False)
+        self.playwright = None
+        self.browser = None
+        self.sleep_on_close = kwargs.get("sleep_on_close", False)
+        self.hooks = {
+            'on_browser_created': None,
+            'on_user_agent_updated': None,
+            'on_execution_started': None,
+            'before_goto': None,
+            'after_goto': None,
+            'before_return_html': None,
+            'before_retrieve_html': None
+        }
+
+    async def __aenter__(self):
+        await self.start()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        await self.close()
+
+    async def start(self):
+        if self.playwright is None:
+            self.playwright = await async_playwright().start()
+        if self.browser is None:
+            browser_args = {
+                "headless": self.headless,
+                "args": [
+                    "--disable-gpu",
+                    "--no-sandbox",
+                    "--disable-dev-shm-usage",
+                    "--disable-blink-features=AutomationControlled",
+                    "--disable-infobars",
+                    "--window-position=0,0",
+                    "--ignore-certificate-errors",
+                    "--ignore-certificate-errors-spki-list",
+                    # "--headless=new",  # Use the new headless mode
+                ]
+            }
+            
+            # Add proxy settings if a proxy is specified
+            if self.proxy:
+                proxy_settings = ProxySettings(server=self.proxy)
+                browser_args["proxy"] = proxy_settings
+            elif self.proxy_config:
+                proxy_settings = ProxySettings(server=self.proxy_config.get("server"), username=self.proxy_config.get("username"), password=self.proxy_config.get("password"))
+                browser_args["proxy"] = proxy_settings
+                
+            # Select the appropriate browser based on the browser_type
+            if self.browser_type == "firefox":
+                self.browser = await self.playwright.firefox.launch(**browser_args)
+            elif self.browser_type == "webkit":
+                self.browser = await self.playwright.webkit.launch(**browser_args)
+            else:
+                self.browser = await self.playwright.chromium.launch(**browser_args)
+
+            await self.execute_hook('on_browser_created', self.browser)
+
+    async def close(self):
+        if self.sleep_on_close:
+            await asyncio.sleep(0.5)
+        if self.browser:
+            await self.browser.close()
+            self.browser = None
+        if self.playwright:
+            await self.playwright.stop()
+            self.playwright = None
+
+    def __del__(self):
+        if self.browser or self.playwright:
+            asyncio.get_event_loop().run_until_complete(self.close())
+
+    def set_hook(self, hook_type: str, hook: Callable):
+        if hook_type in self.hooks:
+            self.hooks[hook_type] = hook
+        else:
+            raise ValueError(f"Invalid hook type: {hook_type}")
+
+    async def execute_hook(self, hook_type: str, *args):
+        hook = self.hooks.get(hook_type)
+        if hook:
+            if asyncio.iscoroutinefunction(hook):
+                return await hook(*args)
+            else:
+                return hook(*args)
+        return args[0] if args else None
+
+    def update_user_agent(self, user_agent: str):
+        self.user_agent = user_agent
+
+    def set_custom_headers(self, headers: Dict[str, str]):
+        self.headers = headers
+
+    async def kill_session(self, session_id: str):
+        if session_id in self.sessions:
+            context, page, _ = self.sessions[session_id]
+            await page.close()
+            await context.close()
+            del self.sessions[session_id]
+
+    def _cleanup_expired_sessions(self):
+        current_time = time.time()
+        expired_sessions = [
+            sid for sid, (_, _, last_used) in self.sessions.items() 
+            if current_time - last_used > self.session_ttl
+        ]
+        for sid in expired_sessions:
+            asyncio.create_task(self.kill_session(sid))
+            
+    async def smart_wait(self, page: Page, wait_for: str, timeout: float = 30000):
+        wait_for = wait_for.strip()
+        
+        if wait_for.startswith('js:'):
+            # Explicitly specified JavaScript
+            js_code = wait_for[3:].strip()
+            return await self.csp_compliant_wait(page, js_code, timeout)
+        elif wait_for.startswith('css:'):
+            # Explicitly specified CSS selector
+            css_selector = wait_for[4:].strip()
+            try:
+                await page.wait_for_selector(css_selector, timeout=timeout)
+            except Error as e:
+                if 'Timeout' in str(e):
+                    raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{css_selector}'")
+                else:
+                    raise ValueError(f"Invalid CSS selector: '{css_selector}'")
+        else:
+            # Auto-detect based on content
+            if wait_for.startswith('()') or wait_for.startswith('function'):
+                # It's likely a JavaScript function
+                return await self.csp_compliant_wait(page, wait_for, timeout)
+            else:
+                # Assume it's a CSS selector first
+                try:
+                    await page.wait_for_selector(wait_for, timeout=timeout)
+                except Error as e:
+                    if 'Timeout' in str(e):
+                        raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{wait_for}'")
+                    else:
+                        # If it's not a timeout error, it might be an invalid selector
+                        # Let's try to evaluate it as a JavaScript function as a fallback
+                        try:
+                            return await self.csp_compliant_wait(page, f"() => {{{wait_for}}}", timeout)
+                        except Error:
+                            raise ValueError(f"Invalid wait_for parameter: '{wait_for}'. "
+                                             "It should be either a valid CSS selector, a JavaScript function, "
+                                             "or explicitly prefixed with 'js:' or 'css:'.")
+    
+    async def csp_compliant_wait(self, page: Page, user_wait_function: str, timeout: float = 30000):
+        wrapper_js = f"""
+        async () => {{
+            const userFunction = {user_wait_function};
+            const startTime = Date.now();
+            while (true) {{
+                if (await userFunction()) {{
+                    return true;
+                }}
+                if (Date.now() - startTime > {timeout}) {{
+                    throw new Error('Timeout waiting for condition');
+                }}
+                await new Promise(resolve => setTimeout(resolve, 100));
+            }}
+        }}
+        """
+        
+        try:
+            await page.evaluate(wrapper_js)
+        except TimeoutError:
+            raise TimeoutError(f"Timeout after {timeout}ms waiting for condition")
+        except Exception as e:
+            raise RuntimeError(f"Error in wait condition: {str(e)}")
+
+    async def process_iframes(self, page):
+        # Find all iframes
+        iframes = await page.query_selector_all('iframe')
+        
+        for i, iframe in enumerate(iframes):
+            try:
+                # Add a unique identifier to the iframe
+                await iframe.evaluate(f'(element) => element.id = "iframe-{i}"')
+                
+                # Get the frame associated with this iframe
+                frame = await iframe.content_frame()
+                
+                if frame:
+                    # Wait for the frame to load
+                    await frame.wait_for_load_state('load', timeout=30000)  # 30 seconds timeout
+                    
+                    # Extract the content of the iframe's body
+                    iframe_content = await frame.evaluate('() => document.body.innerHTML')
+                    
+                    # Generate a unique class name for this iframe
+                    class_name = f'extracted-iframe-content-{i}'
+                    
+                    # Replace the iframe with a div containing the extracted content
+                    _iframe = iframe_content.replace('`', '\\`')
+                    await page.evaluate(f"""
+                        () => {{
+                            const iframe = document.getElementById('iframe-{i}');
+                            const div = document.createElement('div');
+                            div.innerHTML = `{_iframe}`;
+                            div.className = '{class_name}';
+                            iframe.replaceWith(div);
+                        }}
+                    """)
+                else:
+                    print(f"Warning: Could not access content frame for iframe {i}")
+            except Exception as e:
+                print(f"Error processing iframe {i}: {str(e)}")
+
+        # Return the page object
+        return page  
+    
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        response_headers = {}
+        status_code = None
+        
+        self._cleanup_expired_sessions()
+        session_id = kwargs.get("session_id")
+        if session_id:
+            context, page, _ = self.sessions.get(session_id, (None, None, None))
+            if not context:
+                context = await self.browser.new_context(
+                    user_agent=self.user_agent,
+                    viewport={"width": 1920, "height": 1080},
+                    proxy={"server": self.proxy} if self.proxy else None,
+                    accept_downloads=True,
+                    java_script_enabled=True
+                )
+                await context.add_cookies([{"name": "cookiesEnabled", "value": "true", "url": url}])
+                await context.set_extra_http_headers(self.headers)
+                page = await context.new_page()
+                self.sessions[session_id] = (context, page, time.time())
+        else:
+            context = await self.browser.new_context(
+                user_agent=self.user_agent,
+                viewport={"width": 1920, "height": 1080},
+                proxy={"server": self.proxy} if self.proxy else None
+            )
+            await context.set_extra_http_headers(self.headers)
+            
+            if kwargs.get("override_navigator", False) or kwargs.get("simulate_user", False) or kwargs.get("magic", False):
+                # Inject scripts to override navigator properties
+                await context.add_init_script("""
+                    // Pass the Permissions Test.
+                    const originalQuery = window.navigator.permissions.query;
+                    window.navigator.permissions.query = (parameters) => (
+                        parameters.name === 'notifications' ?
+                            Promise.resolve({ state: Notification.permission }) :
+                            originalQuery(parameters)
+                    );
+                    Object.defineProperty(navigator, 'webdriver', {
+                        get: () => undefined
+                    });
+                    window.navigator.chrome = {
+                        runtime: {},
+                        // Add other properties if necessary
+                    };
+                    Object.defineProperty(navigator, 'plugins', {
+                        get: () => [1, 2, 3, 4, 5],
+                    });
+                    Object.defineProperty(navigator, 'languages', {
+                        get: () => ['en-US', 'en'],
+                    });
+                    Object.defineProperty(document, 'hidden', {
+                        get: () => false
+                    });
+                    Object.defineProperty(document, 'visibilityState', {
+                        get: () => 'visible'
+                    });
+                """)
+            
+            page = await context.new_page()
+            # await stealth_async(page) #, stealth_config)
+
+        # Add console message and error logging
+        if kwargs.get("log_console", False):
+            page.on("console", lambda msg: print(f"Console: {msg.text}"))
+            page.on("pageerror", lambda exc: print(f"Page Error: {exc}"))
+        
+        try:
+            if self.verbose:
+                print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                if os.path.exists(cache_file_path):
+                    html = ""
+                    with open(cache_file_path, "r") as f:
+                        html = f.read()
+                    # retrieve response headers and status code from cache
+                    with open(cache_file_path + ".meta", "r") as f:
+                        meta = json.load(f)
+                        response_headers = meta.get("response_headers", {})
+                        status_code = meta.get("status_code")
+                    response = AsyncCrawlResponse(
+                        html=html, response_headers=response_headers, status_code=status_code
+                    )
+                    return response
+
+            if not kwargs.get("js_only", False):
+                await self.execute_hook('before_goto', page)
+                
+                response = await page.goto(
+                    url, wait_until="domcontentloaded", timeout=kwargs.get("page_timeout", 60000)
+                )
+                
+                # response = await page.goto("about:blank")
+                # await page.evaluate(f"window.location.href = '{url}'")
+                
+                await self.execute_hook('after_goto', page)
+                
+                # Get status code and headers
+                status_code = response.status
+                response_headers = response.headers
+            else:
+                status_code = 200
+                response_headers = {}
+
+            await page.wait_for_selector('body')
+            
+            await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+
+            js_code = kwargs.get("js_code", kwargs.get("js", self.js_code))
+            if js_code:
+                if isinstance(js_code, str):
+                    await page.evaluate(js_code)
+                elif isinstance(js_code, list):
+                    for js in js_code:
+                        await page.evaluate(js)
+                
+                await page.wait_for_load_state('networkidle')
+                # Check for on execution event
+                await self.execute_hook('on_execution_started', page)
+                
+            if kwargs.get("simulate_user", False) or kwargs.get("magic", False):
+                # Simulate user interactions
+                await page.mouse.move(100, 100)
+                await page.mouse.down()
+                await page.mouse.up()
+                await page.keyboard.press('ArrowDown')
+
+            # Handle the wait_for parameter
+            wait_for = kwargs.get("wait_for")
+            if wait_for:
+                try:
+                    await self.smart_wait(page, wait_for, timeout=kwargs.get("page_timeout", 60000))
+                except Exception as e:
+                    raise RuntimeError(f"Wait condition failed: {str(e)}")
+
+            # Update image dimensions
+            update_image_dimensions_js = """
+            () => {
+                return new Promise((resolve) => {
+                    const filterImage = (img) => {
+                        // Filter out images that are too small
+                        if (img.width < 100 && img.height < 100) return false;
+                        
+                        // Filter out images that are not visible
+                        const rect = img.getBoundingClientRect();
+                        if (rect.width === 0 || rect.height === 0) return false;
+                        
+                        // Filter out images with certain class names (e.g., icons, thumbnails)
+                        if (img.classList.contains('icon') || img.classList.contains('thumbnail')) return false;
+                        
+                        // Filter out images with certain patterns in their src (e.g., placeholder images)
+                        if (img.src.includes('placeholder') || img.src.includes('icon')) return false;
+                        
+                        return true;
+                    };
+
+                    const images = Array.from(document.querySelectorAll('img')).filter(filterImage);
+                    let imagesLeft = images.length;
+                    
+                    if (imagesLeft === 0) {
+                        resolve();
+                        return;
+                    }
+
+                    const checkImage = (img) => {
+                        if (img.complete && img.naturalWidth !== 0) {
+                            img.setAttribute('width', img.naturalWidth);
+                            img.setAttribute('height', img.naturalHeight);
+                            imagesLeft--;
+                            if (imagesLeft === 0) resolve();
+                        }
+                    };
+
+                    images.forEach(img => {
+                        checkImage(img);
+                        if (!img.complete) {
+                            img.onload = () => {
+                                checkImage(img);
+                            };
+                            img.onerror = () => {
+                                imagesLeft--;
+                                if (imagesLeft === 0) resolve();
+                            };
+                        }
+                    });
+
+                    // Fallback timeout of 5 seconds
+                    // setTimeout(() => resolve(), 5000);
+                    resolve();
+                });
+            }
+            """
+            await page.evaluate(update_image_dimensions_js)
+
+            # Wait a bit for any onload events to complete
+            await page.wait_for_timeout(100)
+
+            # Process iframes
+            if kwargs.get("process_iframes", False):
+                page = await self.process_iframes(page)
+            
+            await self.execute_hook('before_retrieve_html', page)
+            # Check if delay_before_return_html is set then wait for that time
+            delay_before_return_html = kwargs.get("delay_before_return_html")
+            if delay_before_return_html:
+                await asyncio.sleep(delay_before_return_html)
+                
+            # Check for remove_overlay_elements parameter
+            if kwargs.get("remove_overlay_elements", False):
+                await self.remove_overlay_elements(page)
+            
+            html = await page.content()
+            await self.execute_hook('before_return_html', page, html)
+            
+            # Check if kwargs has screenshot=True then take screenshot
+            screenshot_data = None
+            if kwargs.get("screenshot"):
+                # Check we have screenshot_wait_for parameter, if we have simply wait for that time
+                screenshot_wait_for = kwargs.get("screenshot_wait_for")
+                if screenshot_wait_for:
+                    await asyncio.sleep(screenshot_wait_for)
+                screenshot_data = await self.take_screenshot(page)          
+
+            if self.verbose:
+                print(f"[LOG] ✅ Crawled {url} successfully!")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                with open(cache_file_path, "w", encoding="utf-8") as f:
+                    f.write(html)
+                # store response headers and status code in cache
+                with open(cache_file_path + ".meta", "w", encoding="utf-8") as f:
+                    json.dump({
+                        "response_headers": response_headers,
+                        "status_code": status_code
+                    }, f)
+
+            async def get_delayed_content(delay: float = 5.0) -> str:
+                if self.verbose:
+                    print(f"[LOG] Waiting for {delay} seconds before retrieving content for {url}")
+                await asyncio.sleep(delay)
+                return await page.content()
+                
+            response = AsyncCrawlResponse(
+                html=html, 
+                response_headers=response_headers, 
+                status_code=status_code,
+                screenshot=screenshot_data,
+                get_delayed_content=get_delayed_content
+            )
+            return response
+        except Error as e:
+            raise Error(f"[ERROR] 🚫 crawl(): Failed to crawl {url}: {str(e)}")
+        # finally:
+        #     if not session_id:
+        #         await page.close()
+        #         await context.close()
+
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        semaphore_count = kwargs.get('semaphore_count', 5)  # Adjust as needed
+        semaphore = asyncio.Semaphore(semaphore_count)
+
+        async def crawl_with_semaphore(url):
+            async with semaphore:
+                return await self.crawl(url, **kwargs)
+
+        tasks = [crawl_with_semaphore(url) for url in urls]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        return [result if not isinstance(result, Exception) else str(result) for result in results]
+
+    async def remove_overlay_elements(self, page: Page) -> None:
+        """
+        Removes popup overlays, modals, cookie notices, and other intrusive elements from the page.
+        
+        Args:
+            page (Page): The Playwright page instance
+        """
+        remove_overlays_js = """
+        async () => {
+            // Function to check if element is visible
+            const isVisible = (elem) => {
+                const style = window.getComputedStyle(elem);
+                return style.display !== 'none' && 
+                       style.visibility !== 'hidden' && 
+                       style.opacity !== '0';
+            };
+
+            // Common selectors for popups and overlays
+            const commonSelectors = [
+                // Close buttons first
+                'button[class*="close" i]', 'button[class*="dismiss" i]', 
+                'button[aria-label*="close" i]', 'button[title*="close" i]',
+                'a[class*="close" i]', 'span[class*="close" i]',
+                
+                // Cookie notices
+                '[class*="cookie-banner" i]', '[id*="cookie-banner" i]',
+                '[class*="cookie-consent" i]', '[id*="cookie-consent" i]',
+                
+                // Newsletter/subscription dialogs
+                '[class*="newsletter" i]', '[class*="subscribe" i]',
+                
+                // Generic popups/modals
+                '[class*="popup" i]', '[class*="modal" i]', 
+                '[class*="overlay" i]', '[class*="dialog" i]',
+                '[role="dialog"]', '[role="alertdialog"]'
+            ];
+
+            // Try to click close buttons first
+            for (const selector of commonSelectors.slice(0, 6)) {
+                const closeButtons = document.querySelectorAll(selector);
+                for (const button of closeButtons) {
+                    if (isVisible(button)) {
+                        try {
+                            button.click();
+                            await new Promise(resolve => setTimeout(resolve, 100));
+                        } catch (e) {
+                            console.log('Error clicking button:', e);
+                        }
+                    }
+                }
+            }
+
+            // Remove remaining overlay elements
+            const removeOverlays = () => {
+                // Find elements with high z-index
+                const allElements = document.querySelectorAll('*');
+                for (const elem of allElements) {
+                    const style = window.getComputedStyle(elem);
+                    const zIndex = parseInt(style.zIndex);
+                    const position = style.position;
+                    
+                    if (
+                        isVisible(elem) && 
+                        (zIndex > 999 || position === 'fixed' || position === 'absolute') &&
+                        (
+                            elem.offsetWidth > window.innerWidth * 0.5 ||
+                            elem.offsetHeight > window.innerHeight * 0.5 ||
+                            style.backgroundColor.includes('rgba') ||
+                            parseFloat(style.opacity) < 1
+                        )
+                    ) {
+                        elem.remove();
+                    }
+                }
+
+                // Remove elements matching common selectors
+                for (const selector of commonSelectors) {
+                    const elements = document.querySelectorAll(selector);
+                    elements.forEach(elem => {
+                        if (isVisible(elem)) {
+                            elem.remove();
+                        }
+                    });
+                }
+            };
+
+            // Remove overlay elements
+            removeOverlays();
+
+            // Remove any fixed/sticky position elements at the top/bottom
+            const removeFixedElements = () => {
+                const elements = document.querySelectorAll('*');
+                elements.forEach(elem => {
+                    const style = window.getComputedStyle(elem);
+                    if (
+                        (style.position === 'fixed' || style.position === 'sticky') &&
+                        isVisible(elem)
+                    ) {
+                        elem.remove();
+                    }
+                });
+            };
+
+            removeFixedElements();
+            
+            // Remove empty block elements as: div, p, span, etc.
+            const removeEmptyBlockElements = () => {
+                const blockElements = document.querySelectorAll('div, p, span, section, article, header, footer, aside, nav, main, ul, ol, li, dl, dt, dd, h1, h2, h3, h4, h5, h6');
+                blockElements.forEach(elem => {
+                    if (elem.innerText.trim() === '') {
+                        elem.remove();
+                    }
+                });
+            };
+
+            // Remove margin-right and padding-right from body (often added by modal scripts)
+            document.body.style.marginRight = '0px';
+            document.body.style.paddingRight = '0px';
+            document.body.style.overflow = 'auto';
+
+            // Wait a bit for any animations to complete
+            await new Promise(resolve => setTimeout(resolve, 100));
+        }
+        """
+        
+        try:
+            await page.evaluate(remove_overlays_js)
+            await page.wait_for_timeout(500)  # Wait for any animations to complete
+        except Exception as e:
+            if self.verbose:
+                print(f"Warning: Failed to remove overlay elements: {str(e)}")
+
+    async def take_screenshot(self, page: Page) -> str:
+        try:
+            # The page is already loaded, just take the screenshot
+            screenshot = await page.screenshot(full_page=True)
+            return base64.b64encode(screenshot).decode('utf-8')
+        except Exception as e:
+            error_message = f"Failed to take screenshot: {str(e)}"
+            print(error_message)
+
+            # Generate an error image
+            img = Image.new('RGB', (800, 600), color='black')
+            draw = ImageDraw.Draw(img)
+            font = ImageFont.load_default()
+            draw.text((10, 10), error_message, fill=(255, 255, 255), font=font)
+            
+            buffered = BytesIO()
+            img.save(buffered, format="JPEG")
+            return base64.b64encode(buffered.getvalue()).decode('utf-8')
+        finally:
+            await page.close()
+

crawl4ai/async_database.py

@@ -0,0 +1,114 @@
+import os
+from pathlib import Path
+import aiosqlite
+import asyncio
+from typing import Optional, Tuple
+
+DB_PATH = os.path.join(Path.home(), ".crawl4ai")
+os.makedirs(DB_PATH, exist_ok=True)
+DB_PATH = os.path.join(DB_PATH, "crawl4ai.db")
+
+class AsyncDatabaseManager:
+    def __init__(self):
+        self.db_path = DB_PATH
+
+    async def ainit_db(self):
+        async with aiosqlite.connect(self.db_path) as db:
+            await db.execute('''
+                CREATE TABLE IF NOT EXISTS crawled_data (
+                    url TEXT PRIMARY KEY,
+                    html TEXT,
+                    cleaned_html TEXT,
+                    markdown TEXT,
+                    extracted_content TEXT,
+                    success BOOLEAN,
+                    media TEXT DEFAULT "{}",
+                    links TEXT DEFAULT "{}",
+                    metadata TEXT DEFAULT "{}",
+                    screenshot TEXT DEFAULT ""
+                )
+            ''')
+            await db.commit()
+        await self.update_db_schema()
+
+    async def update_db_schema(self):
+        async with aiosqlite.connect(self.db_path) as db:
+            # Check if the 'media' column exists
+            cursor = await db.execute("PRAGMA table_info(crawled_data)")
+            columns = await cursor.fetchall()
+            column_names = [column[1] for column in columns]
+            
+            if 'media' not in column_names:
+                await self.aalter_db_add_column('media')
+            
+            # Check for other missing columns and add them if necessary
+            for column in ['links', 'metadata', 'screenshot']:
+                if column not in column_names:
+                    await self.aalter_db_add_column(column)
+
+    async def aalter_db_add_column(self, new_column: str):
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
+                await db.commit()
+            print(f"Added column '{new_column}' to the database.")
+        except Exception as e:
+            print(f"Error altering database to add {new_column} column: {e}")
+
+    async def aget_cached_url(self, url: str) -> Optional[Tuple[str, str, str, str, str, str, str, bool, str]]:
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                async with db.execute('SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?', (url,)) as cursor:
+                    return await cursor.fetchone()
+        except Exception as e:
+            print(f"Error retrieving cached URL: {e}")
+            return None
+
+    async def acache_url(self, url: str, html: str, cleaned_html: str, markdown: str, extracted_content: str, success: bool, media: str = "{}", links: str = "{}", metadata: str = "{}", screenshot: str = ""):
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute('''
+                    INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    ON CONFLICT(url) DO UPDATE SET
+                        html = excluded.html,
+                        cleaned_html = excluded.cleaned_html,
+                        markdown = excluded.markdown,
+                        extracted_content = excluded.extracted_content,
+                        success = excluded.success,
+                        media = excluded.media,      
+                        links = excluded.links,    
+                        metadata = excluded.metadata,      
+                        screenshot = excluded.screenshot
+                ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
+                await db.commit()
+        except Exception as e:
+            print(f"Error caching URL: {e}")
+
+    async def aget_total_count(self) -> int:
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                async with db.execute('SELECT COUNT(*) FROM crawled_data') as cursor:
+                    result = await cursor.fetchone()
+                    return result[0] if result else 0
+        except Exception as e:
+            print(f"Error getting total count: {e}")
+            return 0
+
+    async def aclear_db(self):
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute('DELETE FROM crawled_data')
+                await db.commit()
+        except Exception as e:
+            print(f"Error clearing database: {e}")
+
+    async def aflush_db(self):
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute('DROP TABLE IF EXISTS crawled_data')
+                await db.commit()
+        except Exception as e:
+            print(f"Error flushing database: {e}")
+
+async_db_manager = AsyncDatabaseManager()

crawl4ai/async_webcrawler.py

@@ -0,0 +1,283 @@
+import os
+import time
+from pathlib import Path
+from typing import Optional
+import json
+import asyncio
+from .models import CrawlResult
+from .async_database import async_db_manager
+from .chunking_strategy import *
+from .extraction_strategy import *
+from .async_crawler_strategy import AsyncCrawlerStrategy, AsyncPlaywrightCrawlerStrategy, AsyncCrawlResponse
+from .content_scrapping_strategy import WebScrappingStrategy
+from .config import MIN_WORD_THRESHOLD, IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
+from .utils import (
+    sanitize_input_encode,
+    InvalidCSSSelectorError,
+    format_html
+)
+
+
+class AsyncWebCrawler:
+    def __init__(
+        self,
+        crawler_strategy: Optional[AsyncCrawlerStrategy] = None,
+        always_by_pass_cache: bool = False,
+        base_directory: str = str(Path.home()),
+        **kwargs,
+    ):
+        self.crawler_strategy = crawler_strategy or AsyncPlaywrightCrawlerStrategy(
+            **kwargs
+        )
+        self.always_by_pass_cache = always_by_pass_cache
+        # self.crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
+        self.crawl4ai_folder = os.path.join(base_directory, ".crawl4ai")
+        os.makedirs(self.crawl4ai_folder, exist_ok=True)
+        os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
+        self.ready = False
+        self.verbose = kwargs.get("verbose", False)
+
+    async def __aenter__(self):
+        await self.crawler_strategy.__aenter__()
+        await self.awarmup()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        await self.crawler_strategy.__aexit__(exc_type, exc_val, exc_tb)
+
+    async def awarmup(self):
+        if self.verbose:
+            print("[LOG] 🌤️  Warming up the AsyncWebCrawler")
+        await async_db_manager.ainit_db()
+        await self.arun(
+            url="https://google.com/",
+            word_count_threshold=5,
+            bypass_cache=False,
+            verbose=False,
+        )
+        self.ready = True
+        if self.verbose:
+            print("[LOG] 🌞 AsyncWebCrawler is ready to crawl")
+
+    async def arun(
+        self,
+        url: str,
+        word_count_threshold=MIN_WORD_THRESHOLD,
+        extraction_strategy: ExtractionStrategy = None,
+        chunking_strategy: ChunkingStrategy = RegexChunking(),
+        bypass_cache: bool = False,
+        css_selector: str = None,
+        screenshot: bool = False,
+        user_agent: str = None,
+        verbose=True,
+        **kwargs,
+    ) -> CrawlResult:
+        try:
+            extraction_strategy = extraction_strategy or NoExtractionStrategy()
+            extraction_strategy.verbose = verbose
+            if not isinstance(extraction_strategy, ExtractionStrategy):
+                raise ValueError("Unsupported extraction strategy")
+            if not isinstance(chunking_strategy, ChunkingStrategy):
+                raise ValueError("Unsupported chunking strategy")
+            
+            word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
+
+            async_response: AsyncCrawlResponse = None
+            cached = None
+            screenshot_data = None
+            extracted_content = None
+            if not bypass_cache and not self.always_by_pass_cache:
+                cached = await async_db_manager.aget_cached_url(url)
+
+            if kwargs.get("warmup", True) and not self.ready:
+                return None
+
+            if cached:
+                html = sanitize_input_encode(cached[1])
+                extracted_content = sanitize_input_encode(cached[4])
+                if screenshot:
+                    screenshot_data = cached[9]
+                    if not screenshot_data:
+                        cached = None
+
+            if not cached or not html:
+                t1 = time.time()
+                if user_agent:
+                    self.crawler_strategy.update_user_agent(user_agent)
+                async_response: AsyncCrawlResponse = await self.crawler_strategy.crawl(url, screenshot=screenshot, **kwargs)
+                html = sanitize_input_encode(async_response.html)
+                screenshot_data = async_response.screenshot
+                t2 = time.time()
+                if verbose:
+                    print(
+                        f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds"
+                    )
+
+            crawl_result = await self.aprocess_html(
+                url,
+                html,
+                extracted_content,
+                word_count_threshold,
+                extraction_strategy,
+                chunking_strategy,
+                css_selector,
+                screenshot_data,
+                verbose,
+                bool(cached),
+                async_response=async_response,
+                **kwargs,
+            )
+            crawl_result.status_code = async_response.status_code if async_response else 200
+            crawl_result.response_headers = async_response.response_headers if async_response else {}
+            crawl_result.success = bool(html)
+            crawl_result.session_id = kwargs.get("session_id", None)
+            return crawl_result
+        except Exception as e:
+            if not hasattr(e, "msg"):
+                e.msg = str(e)
+            print(f"[ERROR] 🚫 arun(): Failed to crawl {url}, error: {e.msg}")
+            return CrawlResult(url=url, html="", markdown = f"[ERROR] 🚫 arun(): Failed to crawl {url}, error: {e.msg}", success=False, error_message=e.msg)
+
+    async def arun_many(
+        self,
+        urls: List[str],
+        word_count_threshold=MIN_WORD_THRESHOLD,
+        extraction_strategy: ExtractionStrategy = None,
+        chunking_strategy: ChunkingStrategy = RegexChunking(),
+        bypass_cache: bool = False,
+        css_selector: str = None,
+        screenshot: bool = False,
+        user_agent: str = None,
+        verbose=True,
+        **kwargs,
+    ) -> List[CrawlResult]:
+        tasks = [
+            self.arun(
+                url,
+                word_count_threshold,
+                extraction_strategy,
+                chunking_strategy,
+                bypass_cache,
+                css_selector,
+                screenshot,
+                user_agent,
+                verbose,
+                **kwargs
+            )
+            for url in urls
+        ]
+        return await asyncio.gather(*tasks)
+
+
+    async def aprocess_html(
+        self,
+        url: str,
+        html: str,
+        extracted_content: str,
+        word_count_threshold: int,
+        extraction_strategy: ExtractionStrategy,
+        chunking_strategy: ChunkingStrategy,
+        css_selector: str,
+        screenshot: str,
+        verbose: bool,
+        is_cached: bool,
+        **kwargs,
+    ) -> CrawlResult:
+        t = time.time()
+        # Extract content from HTML
+        try:
+            t1 = time.time()
+            scrapping_strategy = WebScrappingStrategy()
+            # result = await scrapping_strategy.ascrap(
+            result = scrapping_strategy.scrap(
+                url,
+                html,
+                word_count_threshold=word_count_threshold,
+                css_selector=css_selector,
+                only_text=kwargs.get("only_text", False),
+                image_description_min_word_threshold=kwargs.get(
+                    "image_description_min_word_threshold", IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
+                ),
+                **kwargs,
+            )
+            if verbose:
+                print(
+                    f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds"
+                )
+
+            if result is None:
+                raise ValueError(f"Process HTML, Failed to extract content from the website: {url}")
+        except InvalidCSSSelectorError as e:
+            raise ValueError(str(e))
+        except Exception as e:
+            raise ValueError(f"Process HTML, Failed to extract content from the website: {url}, error: {str(e)}")
+
+        cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
+        markdown = sanitize_input_encode(result.get("markdown", ""))
+        fit_markdown = sanitize_input_encode(result.get("fit_markdown", ""))
+        fit_html = sanitize_input_encode(result.get("fit_html", ""))
+        media = result.get("media", [])
+        links = result.get("links", [])
+        metadata = result.get("metadata", {})
+
+        if extracted_content is None and extraction_strategy and chunking_strategy:
+            if verbose:
+                print(
+                    f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {self.__class__.__name__}"
+                )
+
+            # Check if extraction strategy is type of JsonCssExtractionStrategy
+            if isinstance(extraction_strategy, JsonCssExtractionStrategy) or isinstance(extraction_strategy, JsonCssExtractionStrategy):
+                extraction_strategy.verbose = verbose
+                extracted_content = extraction_strategy.run(url, [html])
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
+            else:
+                sections = chunking_strategy.chunk(markdown)
+                extracted_content = extraction_strategy.run(url, sections)
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
+
+        if verbose:
+            print(
+                f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds."
+            )
+
+        screenshot = None if not screenshot else screenshot
+
+        if not is_cached:
+            await async_db_manager.acache_url(
+                url,
+                html,
+                cleaned_html,
+                markdown,
+                extracted_content,
+                True,
+                json.dumps(media),
+                json.dumps(links),
+                json.dumps(metadata),
+                screenshot=screenshot,
+            )
+
+        return CrawlResult(
+            url=url,
+            html=html,
+            cleaned_html=format_html(cleaned_html),
+            markdown=markdown,
+            fit_markdown=fit_markdown,
+            fit_html= fit_html,
+            media=media,
+            links=links,
+            metadata=metadata,
+            screenshot=screenshot,
+            extracted_content=extracted_content,
+            success=True,
+            error_message="",
+        )
+
+    async def aclear_cache(self):
+        await async_db_manager.aclear_db()
+
+    async def aflush_cache(self):
+        await async_db_manager.aflush_db()
+
+    async def aget_cache_size(self):
+        return await async_db_manager.aget_total_count()

crawl4ai/chunking_strategy.py

@@ -84,6 +84,12 @@ class TopicSegmentationChunking(ChunkingStrategy):
 # Fixed-length word chunks
 class FixedLengthWordChunking(ChunkingStrategy):
     def __init__(self, chunk_size=100, **kwargs):
+        """
+        Initialize the fixed-length word chunking strategy with the given chunk size.
+        
+        Args:
+            chunk_size (int): The size of each chunk in words.
+        """
         self.chunk_size = chunk_size
 
     def chunk(self, text: str) -> list:
@@ -93,14 +99,64 @@ class FixedLengthWordChunking(ChunkingStrategy):
 # Sliding window chunking
 class SlidingWindowChunking(ChunkingStrategy):
     def __init__(self, window_size=100, step=50, **kwargs):
+        """
+        Initialize the sliding window chunking strategy with the given window size and
+        step size.
+        
+        Args:
+            window_size (int): The size of the sliding window in words.
+            step (int): The step size for sliding the window in words.
+        """
         self.window_size = window_size
         self.step = step
 
     def chunk(self, text: str) -> list:
         words = text.split()
         chunks = []
-        for i in range(0, len(words), self.step):
-            chunks.append(' '.join(words[i:i + self.window_size]))
+        
+        if len(words) <= self.window_size:
+            return [text]
+        
+        for i in range(0, len(words) - self.window_size + 1, self.step):
+            chunk = ' '.join(words[i:i + self.window_size])
+            chunks.append(chunk)
+        
+        # Handle the last chunk if it doesn't align perfectly
+        if i + self.window_size < len(words):
+            chunks.append(' '.join(words[-self.window_size:]))
+        
         return chunks
     
 
+class OverlappingWindowChunking(ChunkingStrategy):
+    def __init__(self, window_size=1000, overlap=100, **kwargs):
+        """
+        Initialize the overlapping window chunking strategy with the given window size and
+        overlap size.
+        
+        Args:
+            window_size (int): The size of the window in words.
+            overlap (int): The size of the overlap between consecutive chunks in words.
+        """
+        self.window_size = window_size
+        self.overlap = overlap
+
+    def chunk(self, text: str) -> list:
+        words = text.split()
+        chunks = []
+        
+        if len(words) <= self.window_size:
+            return [text]
+        
+        start = 0
+        while start < len(words):
+            end = start + self.window_size
+            chunk = ' '.join(words[start:end])
+            chunks.append(chunk)
+            
+            if end >= len(words):
+                break
+            
+            start = end - self.overlap
+        
+        return chunks

crawl4ai/config.py

@@ -4,24 +4,23 @@ 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-4-turbo"
+DEFAULT_PROVIDER = "openai/gpt-4o-mini"
 MODEL_REPO_BRANCH = "new-release-0.0.2"
 # Provider-model dictionary, ONLY used when the extraction strategy is LLMExtractionStrategy
 PROVIDER_MODELS = {
     "ollama/llama3": "no-token-needed", # Any model from Ollama no need for API token
     "groq/llama3-70b-8192": os.getenv("GROQ_API_KEY"),
     "groq/llama3-8b-8192": os.getenv("GROQ_API_KEY"),
-    "openai/gpt-3.5-turbo": os.getenv("OPENAI_API_KEY"),
-    "openai/gpt-4-turbo": os.getenv("OPENAI_API_KEY"),
+    "openai/gpt-4o-mini": os.getenv("OPENAI_API_KEY"),
     "openai/gpt-4o": os.getenv("OPENAI_API_KEY"),
     "anthropic/claude-3-haiku-20240307": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-opus-20240229": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-sonnet-20240229": os.getenv("ANTHROPIC_API_KEY"),
+    "anthropic/claude-3-5-sonnet-20240620": os.getenv("ANTHROPIC_API_KEY"),
 }
 
-
 # Chunk token threshold
-CHUNK_TOKEN_THRESHOLD = 500
+CHUNK_TOKEN_THRESHOLD = 2 ** 11 # 2048 tokens
 OVERLAP_RATE = 0.1
 WORD_TOKEN_RATE = 1.3
 
@@ -29,6 +28,20 @@ WORD_TOKEN_RATE = 1.3
 MIN_WORD_THRESHOLD = 1
 IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD = 1
 
+IMPORTANT_ATTRS = ['src', 'href', 'alt', 'title', 'width', 'height'] 
+ONLY_TEXT_ELIGIBLE_TAGS = ['b', 'i', 'u', 'span', 'del', 'ins', 'sub', 'sup', 'strong', 'em', 'code', 'kbd', 'var', 's', 'q', 'abbr', 'cite', 'dfn', 'time', 'small', 'mark']
+SOCIAL_MEDIA_DOMAINS = [
+                            'facebook.com',
+                            'twitter.com',
+                            'x.com',
+                            'linkedin.com',
+                            'instagram.com',
+                            'pinterest.com',
+                            'tiktok.com',
+                            'snapchat.com',
+                            'reddit.com',
+                        ]
+
 # Threshold for the Image extraction - Range is 1 to 6
 # Images are scored based on point based system, to filter based on usefulness. Points are assigned
 # to each image based on the following aspects.

crawl4ai/content_cleaning_strategy.py

@@ -0,0 +1,196 @@
+from bs4 import BeautifulSoup, Tag
+import re
+from typing import Optional
+
+class ContentCleaningStrategy:
+    def __init__(self):
+        # Precompile regex patterns for performance
+        self.negative_patterns = re.compile(r'nav|footer|header|sidebar|ads|comment', re.I)
+        self.positive_patterns = re.compile(r'content|article|main|post', re.I)
+        self.priority_tags = {'article', 'main', 'section', 'div'}
+        self.non_content_tags = {'nav', 'footer', 'header', 'aside'}
+        # Thresholds
+        self.text_density_threshold = 9.0
+        self.min_word_count = 50
+        self.link_density_threshold = 0.2
+        self.max_dom_depth = 10  # To prevent excessive DOM traversal
+
+    def clean(self, clean_html: str) -> str:
+        """
+        Main function that takes cleaned HTML and returns super cleaned HTML.
+
+        Args:
+            clean_html (str): The cleaned HTML content.
+
+        Returns:
+            str: The super cleaned HTML containing only the main content.
+        """
+        try:
+            if not clean_html or not isinstance(clean_html, str):
+                return ''
+            soup = BeautifulSoup(clean_html, 'html.parser')
+            main_content = self.extract_main_content(soup)
+            if main_content:
+                super_clean_element = self.clean_element(main_content)
+                return str(super_clean_element)
+            else:
+                return ''
+        except Exception:
+            # Handle exceptions silently or log them as needed
+            return ''
+
+    def extract_main_content(self, soup: BeautifulSoup) -> Optional[Tag]:
+        """
+        Identifies and extracts the main content element from the HTML.
+
+        Args:
+            soup (BeautifulSoup): The parsed HTML soup.
+
+        Returns:
+            Optional[Tag]: The Tag object containing the main content, or None if not found.
+        """
+        candidates = []
+        for element in soup.find_all(self.priority_tags):
+            if self.is_non_content_tag(element):
+                continue
+            if self.has_negative_class_id(element):
+                continue
+            score = self.calculate_content_score(element)
+            candidates.append((score, element))
+        
+        if not candidates:
+            return None
+
+        # Sort candidates by score in descending order
+        candidates.sort(key=lambda x: x[0], reverse=True)
+        # Select the element with the highest score
+        best_element = candidates[0][1]
+        return best_element
+
+    def calculate_content_score(self, element: Tag) -> float:
+        """
+        Calculates a score for an element based on various heuristics.
+
+        Args:
+            element (Tag): The HTML element to score.
+
+        Returns:
+            float: The content score of the element.
+        """
+        score = 0.0
+
+        if self.is_priority_tag(element):
+            score += 5.0
+        if self.has_positive_class_id(element):
+            score += 3.0
+        if self.has_negative_class_id(element):
+            score -= 3.0
+        if self.is_high_text_density(element):
+            score += 2.0
+        if self.is_low_link_density(element):
+            score += 2.0
+        if self.has_sufficient_content(element):
+            score += 2.0
+        if self.has_headings(element):
+            score += 3.0
+
+        dom_depth = self.calculate_dom_depth(element)
+        score += min(dom_depth, self.max_dom_depth) * 0.5  # Adjust weight as needed
+
+        return score
+
+    def is_priority_tag(self, element: Tag) -> bool:
+        """Checks if the element is a priority tag."""
+        return element.name in self.priority_tags
+
+    def is_non_content_tag(self, element: Tag) -> bool:
+        """Checks if the element is a non-content tag."""
+        return element.name in self.non_content_tags
+
+    def has_negative_class_id(self, element: Tag) -> bool:
+        """Checks if the element has negative indicators in its class or id."""
+        class_id = ' '.join(filter(None, [
+            self.get_attr_str(element.get('class')),
+            element.get('id', '')
+        ]))
+        return bool(self.negative_patterns.search(class_id))
+
+    def has_positive_class_id(self, element: Tag) -> bool:
+        """Checks if the element has positive indicators in its class or id."""
+        class_id = ' '.join(filter(None, [
+            self.get_attr_str(element.get('class')),
+            element.get('id', '')
+        ]))
+        return bool(self.positive_patterns.search(class_id))
+
+    @staticmethod
+    def get_attr_str(attr) -> str:
+        """Converts an attribute value to a string."""
+        if isinstance(attr, list):
+            return ' '.join(attr)
+        elif isinstance(attr, str):
+            return attr
+        else:
+            return ''
+
+    def is_high_text_density(self, element: Tag) -> bool:
+        """Determines if the element has high text density."""
+        text_density = self.calculate_text_density(element)
+        return text_density > self.text_density_threshold
+
+    def calculate_text_density(self, element: Tag) -> float:
+        """Calculates the text density of an element."""
+        text_length = len(element.get_text(strip=True))
+        tag_count = len(element.find_all())
+        tag_count = tag_count or 1  # Prevent division by zero
+        return text_length / tag_count
+
+    def is_low_link_density(self, element: Tag) -> bool:
+        """Determines if the element has low link density."""
+        link_density = self.calculate_link_density(element)
+        return link_density < self.link_density_threshold
+
+    def calculate_link_density(self, element: Tag) -> float:
+        """Calculates the link density of an element."""
+        text = element.get_text(strip=True)
+        if not text:
+            return 0.0
+        link_text = ' '.join(a.get_text(strip=True) for a in element.find_all('a'))
+        return len(link_text) / len(text) if text else 0.0
+
+    def has_sufficient_content(self, element: Tag) -> bool:
+        """Checks if the element has sufficient word count."""
+        word_count = len(element.get_text(strip=True).split())
+        return word_count >= self.min_word_count
+
+    def calculate_dom_depth(self, element: Tag) -> int:
+        """Calculates the depth of an element in the DOM tree."""
+        depth = 0
+        current_element = element
+        while current_element.parent and depth < self.max_dom_depth:
+            depth += 1
+            current_element = current_element.parent
+        return depth
+
+    def has_headings(self, element: Tag) -> bool:
+        """Checks if the element contains heading tags."""
+        return bool(element.find(['h1', 'h2', 'h3']))
+
+    def clean_element(self, element: Tag) -> Tag:
+        """
+        Cleans the selected element by removing unnecessary attributes and nested non-content elements.
+
+        Args:
+            element (Tag): The HTML element to clean.
+
+        Returns:
+            Tag: The cleaned HTML element.
+        """
+        for tag in element.find_all(['script', 'style', 'aside']):
+            tag.decompose()
+        for tag in element.find_all():
+            attrs = dict(tag.attrs)
+            for attr in attrs:
+                if attr in ['style', 'onclick', 'onmouseover', 'align', 'bgcolor']:
+                    del tag.attrs[attr]
+        return element

crawl4ai/content_scrapping_strategy.py

@@ -0,0 +1,456 @@
+from abc import ABC, abstractmethod
+from typing import Dict, Any
+from bs4 import BeautifulSoup
+from concurrent.futures import ThreadPoolExecutor
+import asyncio, requests, re, os
+from .config import *
+from bs4 import element, NavigableString, Comment
+from urllib.parse import urljoin
+from requests.exceptions import InvalidSchema
+from .content_cleaning_strategy import ContentCleaningStrategy
+
+from .utils import (
+    sanitize_input_encode,
+    sanitize_html,
+    extract_metadata,
+    InvalidCSSSelectorError,
+    CustomHTML2Text,
+    normalize_url,
+    is_external_url
+    
+)
+
+class ContentScrappingStrategy(ABC):
+    @abstractmethod
+    def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        pass
+
+    @abstractmethod
+    async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        pass
+
+class WebScrappingStrategy(ContentScrappingStrategy):
+    def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        return self._get_content_of_website_optimized(url, html, is_async=False, **kwargs)
+
+    async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        return await asyncio.to_thread(self._get_content_of_website_optimized, url, html, **kwargs)
+
+    def _get_content_of_website_optimized(self, url: str, html: str, word_count_threshold: int = MIN_WORD_THRESHOLD, css_selector: str = None, **kwargs) -> Dict[str, Any]:
+        success = True
+        if not html:
+            return None
+
+        soup = BeautifulSoup(html, 'html.parser')
+        body = soup.body
+        
+        
+        image_description_min_word_threshold = kwargs.get('image_description_min_word_threshold', IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD)
+
+        for tag in kwargs.get('excluded_tags', []) or []:
+            for el in body.select(tag):
+                el.decompose()
+        
+        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)
+
+        links = {'internal': [], 'external': []}
+        media = {'images': [], 'videos': [], 'audios': []}
+        internal_links_dict = {}
+        external_links_dict = {}
+
+        # Extract meaningful text for media files from closest parent
+        def find_closest_parent_with_useful_text(tag):
+                current_tag = tag
+                while current_tag:
+                    current_tag = current_tag.parent
+                    # Get the text content of the parent tag
+                    if current_tag:
+                        text_content = current_tag.get_text(separator=' ',strip=True)
+                        # Check if the text content has at least word_count_threshold
+                        if len(text_content.split()) >= image_description_min_word_threshold:
+                            return text_content
+                return None
+
+        def process_image(img, url, index, total_images):
+            #Check if an image has valid display and inside undesired html elements
+            def is_valid_image(img, parent, parent_classes):
+                style = img.get('style', '')
+                src = img.get('src', '')
+                classes_to_check = ['button', 'icon', 'logo']
+                tags_to_check = ['button', 'input']
+                return all([
+                    'display:none' not in style,
+                    src,
+                    not any(s in var for var in [src, img.get('alt', ''), *parent_classes] for s in classes_to_check),
+                    parent.name not in tags_to_check
+                ])
+
+            #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
+                def parse_dimension(dimension):
+                    if dimension:
+                        match = re.match(r"(\d+)(\D*)", dimension)
+                        if match:
+                            number = int(match.group(1))
+                            unit = match.group(2) or 'px'  # Default unit is 'px' if not specified
+                            return number, unit
+                    return None, None
+
+                # 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
+                    img_url = urljoin(base_url,img.get('src'))
+                    try:
+                        response = requests.head(img_url)
+                        if response.status_code == 200:
+                            return response.headers.get('Content-Length',None)
+                        else:
+                            print(f"Failed to retrieve file size for {img_url}")
+                            return None
+                    except InvalidSchema as e:
+                        return None
+                    finally:
+                        return
+
+                image_height = img.get('height')
+                height_value, height_unit = parse_dimension(image_height)
+                image_width =  img.get('width')
+                width_value, width_unit = parse_dimension(image_width)
+                image_size = 0 #int(fetch_image_file_size(img,base_url) or 0)
+                image_src = img.get('src','')
+                if "data:image/" in image_src:
+                    image_format = image_src.split(',')[0].split(';')[0].split('/')[1]
+                else:
+                    image_format = os.path.splitext(img.get('src',''))[1].lower()
+                # Remove . from format
+                image_format = image_format.strip('.').split('?')[0]
+                score = 0
+                if height_value:
+                    if height_unit == 'px' and height_value > 150:
+                        score += 1
+                    if height_unit in ['%','vh','vmin','vmax'] and height_value >30:
+                        score += 1
+                if width_value:
+                    if width_unit == 'px' and width_value > 150:
+                        score += 1
+                    if width_unit in ['%','vh','vmin','vmax'] and width_value >30:
+                        score += 1
+                if image_size > 10000:
+                    score += 1
+                if img.get('alt') != '':
+                    score+=1
+                if any(image_format==format for format in ['jpg','png','webp']):
+                    score+=1
+                if index/images_count<0.5:
+                    score+=1
+                return score
+
+            
+            
+            if not is_valid_image(img, img.parent, img.parent.get('class', [])):
+                return None
+            score = score_image_for_usefulness(img, url, index, total_images)
+            if score <= IMAGE_SCORE_THRESHOLD:
+                return None
+            return {
+                'src': img.get('src', ''),
+                'data-src': img.get('data-src', ''),
+                'alt': img.get('alt', ''),
+                'desc': find_closest_parent_with_useful_text(img),
+                'score': score,
+                'type': 'image'
+            }
+
+        def remove_unwanted_attributes(element, important_attrs, keep_data_attributes=False):
+            attrs_to_remove = []
+            for attr in element.attrs:
+                if attr not in important_attrs:
+                    if keep_data_attributes:
+                        if not attr.startswith('data-'):
+                            attrs_to_remove.append(attr)
+                    else:
+                        attrs_to_remove.append(attr)
+            
+            for attr in attrs_to_remove:
+                del element[attr]
+        
+        def process_element(element: element.PageElement) -> bool:
+            try:
+                if isinstance(element, NavigableString):
+                    if isinstance(element, Comment):
+                        element.extract()
+                    return False
+                
+                # if element.name == 'img':
+                #     process_image(element, url, 0, 1)
+                #     return True
+
+                if element.name in ['script', 'style', 'link', 'meta', 'noscript']:
+                    element.decompose()
+                    return False
+
+                keep_element = False
+                
+                exclude_social_media_domains = SOCIAL_MEDIA_DOMAINS + kwargs.get('exclude_social_media_domains', [])
+                exclude_social_media_domains = list(set(exclude_social_media_domains))
+
+                
+                try:
+                    if element.name == 'a' and element.get('href'):
+                        href = element.get('href', '').strip()
+                        if not href:  # Skip empty hrefs
+                            return False
+                            
+                        url_base = url.split('/')[2]
+                        
+                        # Normalize the URL
+                        try:
+                            normalized_href = normalize_url(href, url)
+                        except ValueError as e:
+                            # logging.warning(f"Invalid URL format: {href}, Error: {str(e)}")
+                            return False
+                            
+                        link_data = {
+                            'href': normalized_href,
+                            'text': element.get_text().strip(),
+                            'title': element.get('title', '').strip()
+                        }
+                        
+                        # Check for duplicates and add to appropriate dictionary
+                        is_external = is_external_url(normalized_href, url_base)
+                        if is_external:
+                            if normalized_href not in external_links_dict:
+                                external_links_dict[normalized_href] = link_data
+                        else:
+                            if normalized_href not in internal_links_dict:
+                                internal_links_dict[normalized_href] = link_data
+                                
+                        keep_element = True
+                        
+                        # Handle external link exclusions
+                        if is_external:
+                            if kwargs.get('exclude_external_links', False):
+                                element.decompose()
+                                return False
+                            elif kwargs.get('exclude_social_media_links', False):
+                                if any(domain in normalized_href.lower() for domain in exclude_social_media_domains):
+                                    element.decompose()
+                                    return False
+                            elif kwargs.get('exclude_domains', []):
+                                if any(domain in normalized_href.lower() for domain in kwargs.get('exclude_domains', [])):
+                                    element.decompose()
+                                    return False
+                                    
+                except Exception as e:
+                    raise Exception(f"Error processing links: {str(e)}")
+
+                try:
+                    if element.name == 'img':
+                        potential_sources = ['src', 'data-src', 'srcset' 'data-lazy-src', 'data-original']
+                        src = element.get('src', '')
+                        while not src and potential_sources:
+                            src = element.get(potential_sources.pop(0), '')
+                        if not src:
+                            element.decompose()
+                            return False
+                        
+                        # If it is srcset pick up the first image
+                        if 'srcset' in element.attrs:
+                            src = element.attrs['srcset'].split(',')[0].split(' ')[0]
+                            
+                        # Check flag if we should remove external images
+                        if kwargs.get('exclude_external_images', False):
+                            src_url_base = src.split('/')[2]
+                            url_base = url.split('/')[2]
+                            if url_base not in src_url_base:
+                                element.decompose()
+                                return False
+                            
+                        if not kwargs.get('exclude_external_images', False) and kwargs.get('exclude_social_media_links', False):
+                            src_url_base = src.split('/')[2]
+                            url_base = url.split('/')[2]
+                            if any(domain in src for domain in exclude_social_media_domains):
+                                element.decompose()
+                                return False
+                            
+                        # Handle exclude domains
+                        if kwargs.get('exclude_domains', []):
+                            if any(domain in src for domain in kwargs.get('exclude_domains', [])):
+                                element.decompose()
+                                return False
+                        
+                        return True  # Always keep image elements
+                except Exception as e:
+                    raise "Error processing images"
+                
+                
+                # Check if flag to remove all forms is set
+                if kwargs.get('remove_forms', False) and element.name == 'form':
+                    element.decompose()
+                    return False
+                
+                if element.name in ['video', 'audio']:
+                    media[f"{element.name}s"].append({
+                        'src': element.get('src'),
+                        'alt': element.get('alt'),
+                        'type': element.name,
+                        'description': find_closest_parent_with_useful_text(element)
+                    })
+                    source_tags = element.find_all('source')
+                    for source_tag in source_tags:
+                        media[f"{element.name}s"].append({
+                        'src': source_tag.get('src'),
+                        'alt': element.get('alt'),
+                        'type': element.name,
+                        'description': find_closest_parent_with_useful_text(element)
+                    })
+                    return True  # Always keep video and audio elements
+
+                if element.name in ONLY_TEXT_ELIGIBLE_TAGS:
+                    if kwargs.get('only_text', False):
+                        element.replace_with(element.get_text())
+
+                try:
+                    remove_unwanted_attributes(element, IMPORTANT_ATTRS, kwargs.get('keep_data_attributes', False))
+                except Exception as e:
+                    print('Error removing unwanted attributes:', str(e))
+                
+
+                # Process children
+                for child in list(element.children):
+                    if isinstance(child, NavigableString) and not isinstance(child, Comment):
+                        if len(child.strip()) > 0:
+                            keep_element = True
+                    else:
+                        if process_element(child):
+                            keep_element = True
+                    
+
+                # Check word count
+                if not keep_element:
+                    word_count = len(element.get_text(strip=True).split())
+                    keep_element = word_count >= word_count_threshold
+
+                if not keep_element:
+                    element.decompose()
+
+                return keep_element
+            except Exception as e:
+                print('Error processing element:', str(e))
+                return False
+
+        #process images by filtering and extracting contextual text from the page
+        # imgs = body.find_all('img')
+        # media['images'] = [
+        #     result for result in
+        #     (process_image(img, url, i, len(imgs)) for i, img in enumerate(imgs))
+        #     if result is not None
+        # ]
+        
+        process_element(body)
+        
+        # Update the links dictionary with unique links
+        links['internal'] = list(internal_links_dict.values())
+        links['external'] = list(external_links_dict.values())
+
+
+        # # Process images using ThreadPoolExecutor
+        imgs = body.find_all('img')
+        
+        with ThreadPoolExecutor() as executor:
+            image_results = list(executor.map(process_image, imgs, [url]*len(imgs), range(len(imgs)), [len(imgs)]*len(imgs)))
+        media['images'] = [result for result in image_results if result is not None]
+
+        def flatten_nested_elements(node):
+            if isinstance(node, NavigableString):
+                return node
+            if len(node.contents) == 1 and isinstance(node.contents[0], element.Tag) and node.contents[0].name == node.name:
+                return flatten_nested_elements(node.contents[0])
+            node.contents = [flatten_nested_elements(child) for child in node.contents]
+            return node
+
+        body = flatten_nested_elements(body)
+        base64_pattern = re.compile(r'data:image/[^;]+;base64,([^"]+)')
+        for img in imgs:
+            src = img.get('src', '')
+            if base64_pattern.match(src):
+                # Replace base64 data with empty string
+                img['src'] = base64_pattern.sub('', src)
+                
+        try:
+            str(body)
+        except Exception as e:
+            # Reset body to the original HTML
+            success = False
+            body = BeautifulSoup(html, 'html.parser')
+            
+            # Create a new div with a special ID
+            error_div = body.new_tag('div', id='crawl4ai_error_message')
+            error_div.string = '''
+            Crawl4AI Error: This page is not fully supported.
+            
+            Possible reasons:
+            1. The page may have restrictions that prevent crawling.
+            2. The page might not be fully loaded.
+            
+            Suggestions:
+            - Try calling the crawl function with these parameters:
+            magic=True,
+            - Set headless=False to visualize what's happening on the page.
+            
+            If the issue persists, please check the page's structure and any potential anti-crawling measures.
+            '''
+            
+            # Append the error div to the body
+            body.body.append(error_div)
+            
+            print(f"[LOG] 😧 Error: After processing the crawled HTML and removing irrelevant tags, nothing was left in the page. Check the markdown for further details.")
+
+
+        cleaned_html = str(body).replace('\n\n', '\n').replace('  ', ' ')
+
+        try:
+            h = CustomHTML2Text()
+            h.update_params(**kwargs.get('html2text', {}))            
+            markdown = h.handle(cleaned_html)
+        except Exception as e:
+            markdown = h.handle(sanitize_html(cleaned_html))
+        markdown = markdown.replace('    ```', '```')
+
+        try:
+            meta = extract_metadata(html, soup)
+        except Exception as e:
+            print('Error extracting metadata:', str(e))
+            meta = {}
+            
+        cleaner = ContentCleaningStrategy()
+        fit_html = cleaner.clean(cleaned_html)
+        fit_markdown = h.handle(fit_html)
+
+        cleaned_html = sanitize_html(cleaned_html)
+        return {
+            'markdown': markdown,
+            'fit_markdown': fit_markdown,
+            'fit_html': fit_html,
+            'cleaned_html': cleaned_html,
+            'success': success,
+            'media': media,
+            'links': links,
+            'metadata': meta
+        }

crawl4ai/crawler_strategy.py

@@ -258,6 +258,18 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                         lambda driver: driver.execute_script("return document.readyState") == "complete"
                     )
             
+            # Optionally, wait for some condition after executing the JS code : Contributed by (https://github.com/jonymusky)
+            wait_for = kwargs.get('wait_for', False)
+            if wait_for:
+                if callable(wait_for):
+                    print("[LOG] 🔄 Waiting for condition...")
+                    WebDriverWait(self.driver, 20).until(wait_for)
+                else:
+                    print("[LOG] 🔄 Waiting for condition...")
+                    WebDriverWait(self.driver, 20).until(
+                        EC.presence_of_element_located((By.CSS_SELECTOR, wait_for))
+                    ) 
+            
             if not can_not_be_done_headless:
                 html = sanitize_input_encode(self.driver.page_source)
             self.driver = self.execute_hook('before_return_html', self.driver, html)

crawl4ai/extraction_strategy.py

@@ -10,7 +10,7 @@ from functools import partial
 from .model_loader import *
 import math
 import numpy as np
-
+from lxml import etree
 
 class ExtractionStrategy(ABC):
     """
@@ -68,7 +68,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
         """
         super().__init__() 
         self.provider = provider
-        self.api_token = api_token or PROVIDER_MODELS.get(provider, None) or os.getenv("OPENAI_API_KEY")
+        self.api_token = api_token or PROVIDER_MODELS.get(provider, "no-token") or os.getenv("OPENAI_API_KEY")
         self.instruction = instruction
         self.extract_type = extraction_type
         self.schema = schema
@@ -80,6 +80,8 @@ class LLMExtractionStrategy(ExtractionStrategy):
         self.word_token_rate = kwargs.get("word_token_rate", WORD_TOKEN_RATE)
         self.apply_chunking = kwargs.get("apply_chunking", True)
         self.base_url = kwargs.get("base_url", None)
+        self.api_base = kwargs.get("api_base", kwargs.get("base_url", None))
+        self.extra_args = kwargs.get("extra_args", {})
         if not self.apply_chunking:
             self.chunk_token_threshold = 1e9
         
@@ -102,7 +104,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
             variable_values["REQUEST"] = self.instruction
             prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION
             
-        if self.extract_type == "schema":
+        if self.extract_type == "schema" and self.schema:
             variable_values["SCHEMA"] = json.dumps(self.schema, indent=2)
             prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION
 
@@ -111,7 +113,13 @@ class LLMExtractionStrategy(ExtractionStrategy):
                 "{" + variable + "}", variable_values[variable]
             )
         
-        response = perform_completion_with_backoff(self.provider, prompt_with_variables, self.api_token, base_url=self.base_url) # , json_response=self.extract_type == "schema")
+        response = perform_completion_with_backoff(
+            self.provider, 
+            prompt_with_variables, 
+            self.api_token, 
+            base_url=self.api_base or self.base_url,
+            extra_args = self.extra_args
+            ) # , json_response=self.extract_type == "schema")
         try:
             blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks']
             blocks = json.loads(blocks)
@@ -227,11 +235,12 @@ class CosineStrategy(ExtractionStrategy):
         """
         Initialize the strategy with clustering parameters.
 
-        :param semantic_filter: A keyword filter for document filtering.
-        :param word_count_threshold: Minimum number of words per cluster.
-        :param max_dist: The maximum cophenetic distance on the dendrogram to form clusters.
-        :param linkage_method: The linkage method for hierarchical clustering.
-        :param top_k: Number of top categories to extract.
+        Args:
+            semantic_filter (str): A keyword filter for document filtering.
+            word_count_threshold (int): Minimum number of words per cluster.
+            max_dist (float): The maximum cophenetic distance on the dendrogram to form clusters.
+            linkage_method (str): The linkage method for hierarchical clustering.
+            top_k (int): Number of top categories to extract.
         """
         super().__init__()
         
@@ -250,8 +259,8 @@ class CosineStrategy(ExtractionStrategy):
         self.get_embedding_method = "direct"
         
         self.device = get_device()
-        import torch
-        self.device = torch.device('cpu')
+        # import torch
+        # self.device = torch.device('cpu')
         
         self.default_batch_size = calculate_batch_size(self.device)
 
@@ -264,7 +273,7 @@ class CosineStrategy(ExtractionStrategy):
         #     self.get_embedding_method = "direct"
         # else:
 
-        self.tokenizer, self.model = load_bge_small_en_v1_5()
+        self.tokenizer, self.model = load_HF_embedding_model(model_name)
         self.model.to(self.device)
         self.model.eval()  
         
@@ -623,3 +632,240 @@ class ContentSummarizationStrategy(ExtractionStrategy):
         # Sort summaries by the original section index to maintain order
         summaries.sort(key=lambda x: x[0])
         return [summary for _, summary in summaries]
+  
+class JsonCssExtractionStrategy(ExtractionStrategy):
+    def __init__(self, schema: Dict[str, Any], **kwargs):
+        super().__init__(**kwargs)
+        self.schema = schema
+
+    def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]:
+        soup = BeautifulSoup(html, 'html.parser')
+        base_elements = soup.select(self.schema['baseSelector'])
+        
+        results = []
+        for element in base_elements:
+            item = self._extract_item(element, self.schema['fields'])
+            if item:
+                results.append(item)
+        
+        return results
+
+    
+
+    def _extract_field(self, element, field):
+        try:
+            if field['type'] == 'nested':
+                nested_element = element.select_one(field['selector'])
+                return self._extract_item(nested_element, field['fields']) if nested_element else {}
+            
+            if field['type'] == 'list':
+                elements = element.select(field['selector'])
+                return [self._extract_list_item(el, field['fields']) for el in elements]
+            
+            if field['type'] == 'nested_list':
+                elements = element.select(field['selector'])
+                return [self._extract_item(el, field['fields']) for el in elements]
+            
+            return self._extract_single_field(element, field)
+        except Exception as e:
+            if self.verbose:
+                print(f"Error extracting field {field['name']}: {str(e)}")
+            return field.get('default')
+
+    def _extract_list_item(self, element, fields):
+        item = {}
+        for field in fields:
+            value = self._extract_single_field(element, field)
+            if value is not None:
+                item[field['name']] = value
+        return item
+    
+    def _extract_single_field(self, element, field):
+        if 'selector' in field:
+            selected = element.select_one(field['selector'])
+            if not selected:
+                return field.get('default')
+        else:
+            selected = element
+
+        value = None
+        if field['type'] == 'text':
+            value = selected.get_text(strip=True)
+        elif field['type'] == 'attribute':
+            value = selected.get(field['attribute'])
+        elif field['type'] == 'html':
+            value = str(selected)
+        elif field['type'] == 'regex':
+            text = selected.get_text(strip=True)
+            match = re.search(field['pattern'], text)
+            value = match.group(1) if match else None
+
+        if 'transform' in field:
+            value = self._apply_transform(value, field['transform'])
+
+        return value if value is not None else field.get('default')
+
+    def _extract_item(self, element, fields):
+        item = {}
+        for field in fields:
+            if field['type'] == 'computed':
+                value = self._compute_field(item, field)
+            else:
+                value = self._extract_field(element, field)
+            if value is not None:
+                item[field['name']] = value
+        return item
+    
+    def _apply_transform(self, value, transform):
+        if transform == 'lowercase':
+            return value.lower()
+        elif transform == 'uppercase':
+            return value.upper()
+        elif transform == 'strip':
+            return value.strip()
+        return value
+
+    def _compute_field(self, item, field):
+        try:
+            if 'expression' in field:
+                return eval(field['expression'], {}, item)
+            elif 'function' in field:
+                return field['function'](item)
+        except Exception as e:
+            if self.verbose:
+                print(f"Error computing field {field['name']}: {str(e)}")
+            return field.get('default')
+
+    def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]:
+        combined_html = self.DEL.join(sections)
+        return self.extract(url, combined_html, **kwargs)
+    
+class JsonXPATHExtractionStrategy(ExtractionStrategy):
+    def __init__(self, schema: Dict[str, Any], **kwargs):
+        super().__init__(**kwargs)
+        self.schema = schema
+        self.use_cssselect = self._check_cssselect()
+
+    def _check_cssselect(self):
+        try:
+            import cssselect
+            return True
+        except ImportError:
+            print("Warning: cssselect is not installed. Falling back to XPath for all selectors.")
+            return False
+
+    def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]:
+        self.soup = BeautifulSoup(html, 'lxml')
+        self.tree = etree.HTML(str(self.soup))
+        
+        selector_type = 'xpath' if not self.use_cssselect else self.schema.get('selectorType', 'css')
+        base_selector = self.schema.get('baseXPath' if selector_type == 'xpath' else 'baseSelector')
+        base_elements = self._select_elements(base_selector, selector_type)
+        
+        results = []
+        for element in base_elements:
+            item = self._extract_item(element, self.schema['fields'])
+            if item:
+                results.append(item)
+        
+        return results
+
+    def _select_elements(self, selector, selector_type, element=None):
+        if selector_type == 'xpath' or not self.use_cssselect:
+            return self.tree.xpath(selector) if element is None else element.xpath(selector)
+        else:  # CSS
+            return self.tree.cssselect(selector) if element is None else element.cssselect(selector)
+
+    def _extract_field(self, element, field):
+        try:
+            selector_type = 'xpath' if not self.use_cssselect else field.get('selectorType', 'css')
+            selector = field.get('xpathSelector' if selector_type == 'xpath' else 'selector')
+            
+            if field['type'] == 'nested':
+                nested_element = self._select_elements(selector, selector_type, element)
+                return self._extract_item(nested_element[0], field['fields']) if nested_element else {}
+            
+            if field['type'] == 'list':
+                elements = self._select_elements(selector, selector_type, element)
+                return [self._extract_list_item(el, field['fields']) for el in elements]
+            
+            if field['type'] == 'nested_list':
+                elements = self._select_elements(selector, selector_type, element)
+                return [self._extract_item(el, field['fields']) for el in elements]
+            
+            return self._extract_single_field(element, field)
+        except Exception as e:
+            if self.verbose:
+                print(f"Error extracting field {field['name']}: {str(e)}")
+            return field.get('default')
+
+    def _extract_list_item(self, element, fields):
+        item = {}
+        for field in fields:
+            value = self._extract_single_field(element, field)
+            if value is not None:
+                item[field['name']] = value
+        return item
+    
+    def _extract_single_field(self, element, field):
+        selector_type = field.get('selectorType', 'css')
+        
+        if 'selector' in field:
+            selected = self._select_elements(field['selector'], selector_type, element)
+            if not selected:
+                return field.get('default')
+            selected = selected[0]
+        else:
+            selected = element
+
+        value = None
+        if field['type'] == 'text':
+            value = selected.text_content().strip() if hasattr(selected, 'text_content') else selected.text.strip()
+        elif field['type'] == 'attribute':
+            value = selected.get(field['attribute'])
+        elif field['type'] == 'html':
+            value = etree.tostring(selected, encoding='unicode')
+        elif field['type'] == 'regex':
+            text = selected.text_content().strip() if hasattr(selected, 'text_content') else selected.text.strip()
+            match = re.search(field['pattern'], text)
+            value = match.group(1) if match else None
+
+        if 'transform' in field:
+            value = self._apply_transform(value, field['transform'])
+
+        return value if value is not None else field.get('default')
+
+    def _extract_item(self, element, fields):
+        item = {}
+        for field in fields:
+            if field['type'] == 'computed':
+                value = self._compute_field(item, field)
+            else:
+                value = self._extract_field(element, field)
+            if value is not None:
+                item[field['name']] = value
+        return item
+    
+    def _apply_transform(self, value, transform):
+        if transform == 'lowercase':
+            return value.lower()
+        elif transform == 'uppercase':
+            return value.upper()
+        elif transform == 'strip':
+            return value.strip()
+        return value
+
+    def _compute_field(self, item, field):
+        try:
+            if 'expression' in field:
+                return eval(field['expression'], {}, item)
+            elif 'function' in field:
+                return field['function'](item)
+        except Exception as e:
+            if self.verbose:
+                print(f"Error computing field {field['name']}: {str(e)}")
+            return field.get('default')
+
+    def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]:
+        combined_html = self.DEL.join(sections)
+        return self.extract(url, combined_html, **kwargs)

crawl4ai/html2text/__main__.py

@@ -0,0 +1,3 @@
+from .cli import main
+
+main()

crawl4ai/html2text/_typing.py

@@ -0,0 +1,2 @@
+class OutCallback:
+    def __call__(self, s: str) -> None: ...

crawl4ai/html2text/cli.py

@@ -0,0 +1,330 @@
+import argparse
+import sys
+
+from . import HTML2Text, __version__, config
+
+
+def main() -> None:
+    baseurl = ""
+
+    class bcolors:
+        HEADER = "\033[95m"
+        OKBLUE = "\033[94m"
+        OKGREEN = "\033[92m"
+        WARNING = "\033[93m"
+        FAIL = "\033[91m"
+        ENDC = "\033[0m"
+        BOLD = "\033[1m"
+        UNDERLINE = "\033[4m"
+
+    p = argparse.ArgumentParser()
+    p.add_argument(
+        "--default-image-alt",
+        dest="default_image_alt",
+        default=config.DEFAULT_IMAGE_ALT,
+        help="The default alt string for images with missing ones",
+    )
+    p.add_argument(
+        "--pad-tables",
+        dest="pad_tables",
+        action="store_true",
+        default=config.PAD_TABLES,
+        help="pad the cells to equal column width in tables",
+    )
+    p.add_argument(
+        "--no-wrap-links",
+        dest="wrap_links",
+        action="store_false",
+        default=config.WRAP_LINKS,
+        help="don't wrap links during conversion",
+    )
+    p.add_argument(
+        "--wrap-list-items",
+        dest="wrap_list_items",
+        action="store_true",
+        default=config.WRAP_LIST_ITEMS,
+        help="wrap list items during conversion",
+    )
+    p.add_argument(
+        "--wrap-tables",
+        dest="wrap_tables",
+        action="store_true",
+        default=config.WRAP_TABLES,
+        help="wrap tables",
+    )
+    p.add_argument(
+        "--ignore-emphasis",
+        dest="ignore_emphasis",
+        action="store_true",
+        default=config.IGNORE_EMPHASIS,
+        help="don't include any formatting for emphasis",
+    )
+    p.add_argument(
+        "--reference-links",
+        dest="inline_links",
+        action="store_false",
+        default=config.INLINE_LINKS,
+        help="use reference style links instead of inline links",
+    )
+    p.add_argument(
+        "--ignore-links",
+        dest="ignore_links",
+        action="store_true",
+        default=config.IGNORE_ANCHORS,
+        help="don't include any formatting for links",
+    )
+    p.add_argument(
+        "--ignore-mailto-links",
+        action="store_true",
+        dest="ignore_mailto_links",
+        default=config.IGNORE_MAILTO_LINKS,
+        help="don't include mailto: links",
+    )
+    p.add_argument(
+        "--protect-links",
+        dest="protect_links",
+        action="store_true",
+        default=config.PROTECT_LINKS,
+        help="protect links from line breaks surrounding them with angle brackets",
+    )
+    p.add_argument(
+        "--ignore-images",
+        dest="ignore_images",
+        action="store_true",
+        default=config.IGNORE_IMAGES,
+        help="don't include any formatting for images",
+    )
+    p.add_argument(
+        "--images-as-html",
+        dest="images_as_html",
+        action="store_true",
+        default=config.IMAGES_AS_HTML,
+        help=(
+            "Always write image tags as raw html; preserves `height`, `width` and "
+            "`alt` if possible."
+        ),
+    )
+    p.add_argument(
+        "--images-to-alt",
+        dest="images_to_alt",
+        action="store_true",
+        default=config.IMAGES_TO_ALT,
+        help="Discard image data, only keep alt text",
+    )
+    p.add_argument(
+        "--images-with-size",
+        dest="images_with_size",
+        action="store_true",
+        default=config.IMAGES_WITH_SIZE,
+        help=(
+            "Write image tags with height and width attrs as raw html to retain "
+            "dimensions"
+        ),
+    )
+    p.add_argument(
+        "-g",
+        "--google-doc",
+        action="store_true",
+        dest="google_doc",
+        default=False,
+        help="convert an html-exported Google Document",
+    )
+    p.add_argument(
+        "-d",
+        "--dash-unordered-list",
+        action="store_true",
+        dest="ul_style_dash",
+        default=False,
+        help="use a dash rather than a star for unordered list items",
+    )
+    p.add_argument(
+        "-e",
+        "--asterisk-emphasis",
+        action="store_true",
+        dest="em_style_asterisk",
+        default=False,
+        help="use an asterisk rather than an underscore for emphasized text",
+    )
+    p.add_argument(
+        "-b",
+        "--body-width",
+        dest="body_width",
+        type=int,
+        default=config.BODY_WIDTH,
+        help="number of characters per output line, 0 for no wrap",
+    )
+    p.add_argument(
+        "-i",
+        "--google-list-indent",
+        dest="list_indent",
+        type=int,
+        default=config.GOOGLE_LIST_INDENT,
+        help="number of pixels Google indents nested lists",
+    )
+    p.add_argument(
+        "-s",
+        "--hide-strikethrough",
+        action="store_true",
+        dest="hide_strikethrough",
+        default=False,
+        help="hide strike-through text. only relevant when -g is " "specified as well",
+    )
+    p.add_argument(
+        "--escape-all",
+        action="store_true",
+        dest="escape_snob",
+        default=False,
+        help=(
+            "Escape all special characters.  Output is less readable, but avoids "
+            "corner case formatting issues."
+        ),
+    )
+    p.add_argument(
+        "--bypass-tables",
+        action="store_true",
+        dest="bypass_tables",
+        default=config.BYPASS_TABLES,
+        help="Format tables in HTML rather than Markdown syntax.",
+    )
+    p.add_argument(
+        "--ignore-tables",
+        action="store_true",
+        dest="ignore_tables",
+        default=config.IGNORE_TABLES,
+        help="Ignore table-related tags (table, th, td, tr) " "while keeping rows.",
+    )
+    p.add_argument(
+        "--single-line-break",
+        action="store_true",
+        dest="single_line_break",
+        default=config.SINGLE_LINE_BREAK,
+        help=(
+            "Use a single line break after a block element rather than two line "
+            "breaks. NOTE: Requires --body-width=0"
+        ),
+    )
+    p.add_argument(
+        "--unicode-snob",
+        action="store_true",
+        dest="unicode_snob",
+        default=config.UNICODE_SNOB,
+        help="Use unicode throughout document",
+    )
+    p.add_argument(
+        "--no-automatic-links",
+        action="store_false",
+        dest="use_automatic_links",
+        default=config.USE_AUTOMATIC_LINKS,
+        help="Do not use automatic links wherever applicable",
+    )
+    p.add_argument(
+        "--no-skip-internal-links",
+        action="store_false",
+        dest="skip_internal_links",
+        default=config.SKIP_INTERNAL_LINKS,
+        help="Do not skip internal links",
+    )
+    p.add_argument(
+        "--links-after-para",
+        action="store_true",
+        dest="links_each_paragraph",
+        default=config.LINKS_EACH_PARAGRAPH,
+        help="Put links after each paragraph instead of document",
+    )
+    p.add_argument(
+        "--mark-code",
+        action="store_true",
+        dest="mark_code",
+        default=config.MARK_CODE,
+        help="Mark program code blocks with [code]...[/code]",
+    )
+    p.add_argument(
+        "--decode-errors",
+        dest="decode_errors",
+        default=config.DECODE_ERRORS,
+        help=(
+            "What to do in case of decode errors.'ignore', 'strict' and 'replace' are "
+            "acceptable values"
+        ),
+    )
+    p.add_argument(
+        "--open-quote",
+        dest="open_quote",
+        default=config.OPEN_QUOTE,
+        help="The character used to open quotes",
+    )
+    p.add_argument(
+        "--close-quote",
+        dest="close_quote",
+        default=config.CLOSE_QUOTE,
+        help="The character used to close quotes",
+    )
+    p.add_argument(
+        "--version", action="version", version=".".join(map(str, __version__))
+    )
+    p.add_argument("filename", nargs="?")
+    p.add_argument("encoding", nargs="?", default="utf-8")
+    p.add_argument(
+        "--include-sup-sub",
+        dest="include_sup_sub",
+        action="store_true",
+        default=config.INCLUDE_SUP_SUB,
+        help="Include the sup and sub tags",
+    )
+    args = p.parse_args()
+
+    if args.filename and args.filename != "-":
+        with open(args.filename, "rb") as fp:
+            data = fp.read()
+    else:
+        data = sys.stdin.buffer.read()
+
+    try:
+        html = data.decode(args.encoding, args.decode_errors)
+    except UnicodeDecodeError as err:
+        warning = bcolors.WARNING + "Warning:" + bcolors.ENDC
+        warning += " Use the " + bcolors.OKGREEN
+        warning += "--decode-errors=ignore" + bcolors.ENDC + " flag."
+        print(warning)
+        raise err
+
+    h = HTML2Text(baseurl=baseurl)
+    # handle options
+    if args.ul_style_dash:
+        h.ul_item_mark = "-"
+    if args.em_style_asterisk:
+        h.emphasis_mark = "*"
+        h.strong_mark = "__"
+
+    h.body_width = args.body_width
+    h.google_list_indent = args.list_indent
+    h.ignore_emphasis = args.ignore_emphasis
+    h.ignore_links = args.ignore_links
+    h.ignore_mailto_links = args.ignore_mailto_links
+    h.protect_links = args.protect_links
+    h.ignore_images = args.ignore_images
+    h.images_as_html = args.images_as_html
+    h.images_to_alt = args.images_to_alt
+    h.images_with_size = args.images_with_size
+    h.google_doc = args.google_doc
+    h.hide_strikethrough = args.hide_strikethrough
+    h.escape_snob = args.escape_snob
+    h.bypass_tables = args.bypass_tables
+    h.ignore_tables = args.ignore_tables
+    h.single_line_break = args.single_line_break
+    h.inline_links = args.inline_links
+    h.unicode_snob = args.unicode_snob
+    h.use_automatic_links = args.use_automatic_links
+    h.skip_internal_links = args.skip_internal_links
+    h.links_each_paragraph = args.links_each_paragraph
+    h.mark_code = args.mark_code
+    h.wrap_links = args.wrap_links
+    h.wrap_list_items = args.wrap_list_items
+    h.wrap_tables = args.wrap_tables
+    h.pad_tables = args.pad_tables
+    h.default_image_alt = args.default_image_alt
+    h.open_quote = args.open_quote
+    h.close_quote = args.close_quote
+    h.include_sup_sub = args.include_sup_sub
+
+    sys.stdout.write(h.handle(html))

crawl4ai/html2text/config.py

@@ -0,0 +1,172 @@
+import re
+
+# Use Unicode characters instead of their ascii pseudo-replacements
+UNICODE_SNOB = False
+
+# Marker to use for marking tables for padding post processing
+TABLE_MARKER_FOR_PAD = "special_marker_for_table_padding"
+# Escape all special characters.  Output is less readable, but avoids
+# corner case formatting issues.
+ESCAPE_SNOB = False
+ESCAPE_BACKSLASH = False
+ESCAPE_DOT = False
+ESCAPE_PLUS = False
+ESCAPE_DASH = False
+
+# Put the links after each paragraph instead of at the end.
+LINKS_EACH_PARAGRAPH = False
+
+# Wrap long lines at position. 0 for no wrapping.
+BODY_WIDTH = 78
+
+# Don't show internal links (href="#local-anchor") -- corresponding link
+# targets won't be visible in the plain text file anyway.
+SKIP_INTERNAL_LINKS = True
+
+# Use inline, rather than reference, formatting for images and links
+INLINE_LINKS = True
+
+# Protect links from line breaks surrounding them with angle brackets (in
+# addition to their square brackets)
+PROTECT_LINKS = False
+# WRAP_LINKS = True
+WRAP_LINKS = True
+
+# Wrap list items.
+WRAP_LIST_ITEMS = False
+
+# Wrap tables
+WRAP_TABLES = False
+
+# Number of pixels Google indents nested lists
+GOOGLE_LIST_INDENT = 36
+
+# Values Google and others may use to indicate bold text
+BOLD_TEXT_STYLE_VALUES = ("bold", "700", "800", "900")
+
+IGNORE_ANCHORS = False
+IGNORE_MAILTO_LINKS = False
+IGNORE_IMAGES = False
+IMAGES_AS_HTML = False
+IMAGES_TO_ALT = False
+IMAGES_WITH_SIZE = False
+IGNORE_EMPHASIS = False
+MARK_CODE = False
+DECODE_ERRORS = "strict"
+DEFAULT_IMAGE_ALT = ""
+PAD_TABLES = False
+
+# Convert links with same href and text to <href> format
+# if they are absolute links
+USE_AUTOMATIC_LINKS = True
+
+# For checking space-only lines on line 771
+RE_SPACE = re.compile(r"\s\+")
+
+RE_ORDERED_LIST_MATCHER = re.compile(r"\d+\.\s")
+RE_UNORDERED_LIST_MATCHER = re.compile(r"[-\*\+]\s")
+RE_MD_CHARS_MATCHER = re.compile(r"([\\\[\]\(\)])")
+RE_MD_CHARS_MATCHER_ALL = re.compile(r"([`\*_{}\[\]\(\)#!])")
+
+# to find links in the text
+RE_LINK = re.compile(r"(\[.*?\] ?\(.*?\))|(\[.*?\]:.*?)")
+
+# to find table separators
+RE_TABLE = re.compile(r" \| ")
+
+RE_MD_DOT_MATCHER = re.compile(
+    r"""
+    ^             # start of line
+    (\s*\d+)      # optional whitespace and a number
+    (\.)          # dot
+    (?=\s)        # lookahead assert whitespace
+    """,
+    re.MULTILINE | re.VERBOSE,
+)
+RE_MD_PLUS_MATCHER = re.compile(
+    r"""
+    ^
+    (\s*)
+    (\+)
+    (?=\s)
+    """,
+    flags=re.MULTILINE | re.VERBOSE,
+)
+RE_MD_DASH_MATCHER = re.compile(
+    r"""
+    ^
+    (\s*)
+    (-)
+    (?=\s|\-)     # followed by whitespace (bullet list, or spaced out hr)
+                  # or another dash (header or hr)
+    """,
+    flags=re.MULTILINE | re.VERBOSE,
+)
+RE_SLASH_CHARS = r"\`*_{}[]()#+-.!"
+RE_MD_BACKSLASH_MATCHER = re.compile(
+    r"""
+    (\\)          # match one slash
+    (?=[%s])      # followed by a char that requires escaping
+    """
+    % re.escape(RE_SLASH_CHARS),
+    flags=re.VERBOSE,
+)
+
+UNIFIABLE = {
+    "rsquo": "'",
+    "lsquo": "'",
+    "rdquo": '"',
+    "ldquo": '"',
+    "copy": "(C)",
+    "mdash": "--",
+    "nbsp": " ",
+    "rarr": "->",
+    "larr": "<-",
+    "middot": "*",
+    "ndash": "-",
+    "oelig": "oe",
+    "aelig": "ae",
+    "agrave": "a",
+    "aacute": "a",
+    "acirc": "a",
+    "atilde": "a",
+    "auml": "a",
+    "aring": "a",
+    "egrave": "e",
+    "eacute": "e",
+    "ecirc": "e",
+    "euml": "e",
+    "igrave": "i",
+    "iacute": "i",
+    "icirc": "i",
+    "iuml": "i",
+    "ograve": "o",
+    "oacute": "o",
+    "ocirc": "o",
+    "otilde": "o",
+    "ouml": "o",
+    "ugrave": "u",
+    "uacute": "u",
+    "ucirc": "u",
+    "uuml": "u",
+    "lrm": "",
+    "rlm": "",
+}
+
+# Format tables in HTML rather than Markdown syntax
+BYPASS_TABLES = False
+# Ignore table-related tags (table, th, td, tr) while keeping rows
+IGNORE_TABLES = False
+
+
+# Use a single line break after a block element rather than two line breaks.
+# NOTE: Requires body width setting to be 0.
+SINGLE_LINE_BREAK = False
+
+
+# Use double quotation marks when converting the <q> tag.
+OPEN_QUOTE = '"'
+CLOSE_QUOTE = '"'
+
+# Include the <sup> and <sub> tags
+INCLUDE_SUP_SUB = False

crawl4ai/html2text/elements.py

@@ -0,0 +1,18 @@
+from typing import Dict, Optional
+
+
+class AnchorElement:
+    __slots__ = ["attrs", "count", "outcount"]
+
+    def __init__(self, attrs: Dict[str, Optional[str]], count: int, outcount: int):
+        self.attrs = attrs
+        self.count = count
+        self.outcount = outcount
+
+
+class ListElement:
+    __slots__ = ["name", "num"]
+
+    def __init__(self, name: str, num: int):
+        self.name = name
+        self.num = num

crawl4ai/html2text/utils.py

@@ -0,0 +1,303 @@
+import html.entities
+from typing import Dict, List, Optional
+
+from . import config
+
+unifiable_n = {
+    html.entities.name2codepoint[k]: v
+    for k, v in config.UNIFIABLE.items()
+    if k != "nbsp"
+}
+
+
+def hn(tag: str) -> int:
+    if tag[0] == "h" and len(tag) == 2:
+        n = tag[1]
+        if "0" < n <= "9":
+            return int(n)
+    return 0
+
+
+def dumb_property_dict(style: str) -> Dict[str, str]:
+    """
+    :returns: A hash of css attributes
+    """
+    return {
+        x.strip().lower(): y.strip().lower()
+        for x, y in [z.split(":", 1) for z in style.split(";") if ":" in z]
+    }
+
+
+def dumb_css_parser(data: str) -> Dict[str, Dict[str, str]]:
+    """
+    :type data: str
+
+    :returns: A hash of css selectors, each of which contains a hash of
+    css attributes.
+    :rtype: dict
+    """
+    # remove @import sentences
+    data += ";"
+    importIndex = data.find("@import")
+    while importIndex != -1:
+        data = data[0:importIndex] + data[data.find(";", importIndex) + 1 :]
+        importIndex = data.find("@import")
+
+    # parse the css. reverted from dictionary comprehension in order to
+    # support older pythons
+    pairs = [x.split("{") for x in data.split("}") if "{" in x.strip()]
+    try:
+        elements = {a.strip(): dumb_property_dict(b) for a, b in pairs}
+    except ValueError:
+        elements = {}  # not that important
+
+    return elements
+
+
+def element_style(
+    attrs: Dict[str, Optional[str]],
+    style_def: Dict[str, Dict[str, str]],
+    parent_style: Dict[str, str],
+) -> Dict[str, str]:
+    """
+    :type attrs: dict
+    :type style_def: dict
+    :type style_def: dict
+
+    :returns: A hash of the 'final' style attributes of the element
+    :rtype: dict
+    """
+    style = parent_style.copy()
+    if "class" in attrs:
+        assert attrs["class"] is not None
+        for css_class in attrs["class"].split():
+            css_style = style_def.get("." + css_class, {})
+            style.update(css_style)
+    if "style" in attrs:
+        assert attrs["style"] is not None
+        immediate_style = dumb_property_dict(attrs["style"])
+        style.update(immediate_style)
+
+    return style
+
+
+def google_list_style(style: Dict[str, str]) -> str:
+    """
+    Finds out whether this is an ordered or unordered list
+
+    :type style: dict
+
+    :rtype: str
+    """
+    if "list-style-type" in style:
+        list_style = style["list-style-type"]
+        if list_style in ["disc", "circle", "square", "none"]:
+            return "ul"
+
+    return "ol"
+
+
+def google_has_height(style: Dict[str, str]) -> bool:
+    """
+    Check if the style of the element has the 'height' attribute
+    explicitly defined
+
+    :type style: dict
+
+    :rtype: bool
+    """
+    return "height" in style
+
+
+def google_text_emphasis(style: Dict[str, str]) -> List[str]:
+    """
+    :type style: dict
+
+    :returns: A list of all emphasis modifiers of the element
+    :rtype: list
+    """
+    emphasis = []
+    if "text-decoration" in style:
+        emphasis.append(style["text-decoration"])
+    if "font-style" in style:
+        emphasis.append(style["font-style"])
+    if "font-weight" in style:
+        emphasis.append(style["font-weight"])
+
+    return emphasis
+
+
+def google_fixed_width_font(style: Dict[str, str]) -> bool:
+    """
+    Check if the css of the current element defines a fixed width font
+
+    :type style: dict
+
+    :rtype: bool
+    """
+    font_family = ""
+    if "font-family" in style:
+        font_family = style["font-family"]
+    return "courier new" == font_family or "consolas" == font_family
+
+
+def list_numbering_start(attrs: Dict[str, Optional[str]]) -> int:
+    """
+    Extract numbering from list element attributes
+
+    :type attrs: dict
+
+    :rtype: int or None
+    """
+    if "start" in attrs:
+        assert attrs["start"] is not None
+        try:
+            return int(attrs["start"]) - 1
+        except ValueError:
+            pass
+
+    return 0
+
+
+def skipwrap(
+    para: str, wrap_links: bool, wrap_list_items: bool, wrap_tables: bool
+) -> bool:
+    # If it appears to contain a link
+    # don't wrap
+    if not wrap_links and config.RE_LINK.search(para):
+        return True
+    # If the text begins with four spaces or one tab, it's a code block;
+    # don't wrap
+    if para[0:4] == "    " or para[0] == "\t":
+        return True
+
+    # If the text begins with only two "--", possibly preceded by
+    # whitespace, that's an emdash; so wrap.
+    stripped = para.lstrip()
+    if stripped[0:2] == "--" and len(stripped) > 2 and stripped[2] != "-":
+        return False
+
+    # I'm not sure what this is for; I thought it was to detect lists,
+    # but there's a <br>-inside-<span> case in one of the tests that
+    # also depends upon it.
+    if stripped[0:1] in ("-", "*") and not stripped[0:2] == "**":
+        return not wrap_list_items
+
+    # If text contains a pipe character it is likely a table
+    if not wrap_tables and config.RE_TABLE.search(para):
+        return True
+
+    # If the text begins with a single -, *, or +, followed by a space,
+    # or an integer, followed by a ., followed by a space (in either
+    # case optionally proceeded by whitespace), it's a list; don't wrap.
+    return bool(
+        config.RE_ORDERED_LIST_MATCHER.match(stripped)
+        or config.RE_UNORDERED_LIST_MATCHER.match(stripped)
+    )
+
+
+def escape_md(text: str) -> str:
+    """
+    Escapes markdown-sensitive characters within other markdown
+    constructs.
+    """
+    return config.RE_MD_CHARS_MATCHER.sub(r"\\\1", text)
+
+
+def escape_md_section(
+    text: str,
+    escape_backslash: bool = True,
+    snob: bool = False,
+    escape_dot: bool = True,
+    escape_plus: bool = True,
+    escape_dash: bool = True
+) -> str:
+    """
+    Escapes markdown-sensitive characters across whole document sections.
+    Each escaping operation can be controlled individually.
+    """
+    if escape_backslash:
+        text = config.RE_MD_BACKSLASH_MATCHER.sub(r"\\\1", text)
+
+    if snob:
+        text = config.RE_MD_CHARS_MATCHER_ALL.sub(r"\\\1", text)
+
+    if escape_dot:
+        text = config.RE_MD_DOT_MATCHER.sub(r"\1\\\2", text)
+
+    if escape_plus:
+        text = config.RE_MD_PLUS_MATCHER.sub(r"\1\\\2", text)
+
+    if escape_dash:
+        text = config.RE_MD_DASH_MATCHER.sub(r"\1\\\2", text)
+
+    return text
+
+def reformat_table(lines: List[str], right_margin: int) -> List[str]:
+    """
+    Given the lines of a table
+    padds the cells and returns the new lines
+    """
+    # find the maximum width of the columns
+    max_width = [len(x.rstrip()) + right_margin for x in lines[0].split("|")]
+    max_cols = len(max_width)
+    for line in lines:
+        cols = [x.rstrip() for x in line.split("|")]
+        num_cols = len(cols)
+
+        # don't drop any data if colspan attributes result in unequal lengths
+        if num_cols < max_cols:
+            cols += [""] * (max_cols - num_cols)
+        elif max_cols < num_cols:
+            max_width += [len(x) + right_margin for x in cols[-(num_cols - max_cols) :]]
+            max_cols = num_cols
+
+        max_width = [
+            max(len(x) + right_margin, old_len) for x, old_len in zip(cols, max_width)
+        ]
+
+    # reformat
+    new_lines = []
+    for line in lines:
+        cols = [x.rstrip() for x in line.split("|")]
+        if set(line.strip()) == set("-|"):
+            filler = "-"
+            new_cols = [
+                x.rstrip() + (filler * (M - len(x.rstrip())))
+                for x, M in zip(cols, max_width)
+            ]
+            new_lines.append("|-" + "|".join(new_cols) + "|")
+        else:
+            filler = " "
+            new_cols = [
+                x.rstrip() + (filler * (M - len(x.rstrip())))
+                for x, M in zip(cols, max_width)
+            ]
+            new_lines.append("| " + "|".join(new_cols) + "|")
+    return new_lines
+
+
+def pad_tables_in_text(text: str, right_margin: int = 1) -> str:
+    """
+    Provide padding for tables in the text
+    """
+    lines = text.split("\n")
+    table_buffer = []  # type: List[str]
+    table_started = False
+    new_lines = []
+    for line in lines:
+        # Toggle table started
+        if config.TABLE_MARKER_FOR_PAD in line:
+            table_started = not table_started
+            if not table_started:
+                table = reformat_table(table_buffer, right_margin)
+                new_lines.extend(table)
+                table_buffer = []
+                new_lines.append("")
+            continue
+        # Process lines
+        if table_started:
+            table_buffer.append(line)
+        else:
+            new_lines.append(line)
+    return "\n".join(new_lines)

crawl4ai/model_loader.py

@@ -72,56 +72,22 @@ def load_bert_base_uncased():
     return tokenizer, model
 
 @lru_cache()
-def load_bge_small_en_v1_5():
+def load_HF_embedding_model(model_name="BAAI/bge-small-en-v1.5") -> tuple:
+    """Load the Hugging Face model for embedding.
+    
+    Args:
+        model_name (str, optional): The model name to load. Defaults to "BAAI/bge-small-en-v1.5".
+        
+    Returns:
+        tuple: The tokenizer and model.
+    """
     from transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel
-    tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5', resume_download=None)
-    model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5', resume_download=None)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, resume_download=None)
+    model = AutoModel.from_pretrained(model_name, resume_download=None)
     model.eval()
     model, device = set_model_device(model)
     return tokenizer, model
 
-@lru_cache()
-def load_onnx_all_MiniLM_l6_v2():
-    from crawl4ai.onnx_embedding import DefaultEmbeddingModel
-
-    model_path = "models/onnx.tar.gz"
-    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/onnx.tar.gz"
-    __location__ = os.path.realpath(
-        os.path.join(os.getcwd(), os.path.dirname(__file__)))
-    download_path = os.path.join(__location__, model_path)
-    onnx_dir = os.path.join(__location__, "models/onnx")
-    
-    # Create the models directory if it does not exist
-    os.makedirs(os.path.dirname(download_path), exist_ok=True)
-
-    # Download the tar.gz file if it does not exist
-    if not os.path.exists(download_path):
-        def download_with_progress(url, filename):
-            def reporthook(block_num, block_size, total_size):
-                downloaded = block_num * block_size
-                percentage = 100 * downloaded / total_size
-                if downloaded < total_size:
-                    print(f"\rDownloading: {percentage:.2f}% ({downloaded / (1024 * 1024):.2f} MB of {total_size / (1024 * 1024):.2f} MB)", end='')
-                else:
-                    print("\rDownload complete!")
-
-            urllib.request.urlretrieve(url, filename, reporthook)
-
-        download_with_progress(model_url, download_path)
-
-    # Extract the tar.gz file if the onnx directory does not exist
-    if not os.path.exists(onnx_dir):
-        with tarfile.open(download_path, "r:gz") as tar:
-            tar.extractall(path=os.path.join(__location__, "models"))
-        
-        # remove the tar.gz file
-        os.remove(download_path)
-    
-    
-    
-    model = DefaultEmbeddingModel()
-    return model
-
 @lru_cache()
 def load_text_classifier():
     from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -188,7 +154,6 @@ def load_nltk_punkt():
         nltk.download('punkt')
     return nltk.data.find('tokenizers/punkt')
 
-
 @lru_cache()
 def load_spacy_model():
     import spacy

crawl4ai/models.py

@@ -14,6 +14,11 @@ class CrawlResult(BaseModel):
     links: Dict[str, List[Dict]] = {}
     screenshot: Optional[str] = None
     markdown: Optional[str] = None
+    fit_markdown: Optional[str] = None
+    fit_html: Optional[str] = None
     extracted_content: Optional[str] = None
     metadata: Optional[dict] = None
-    error_message: Optional[str] = None
+    error_message: Optional[str] = None
+    session_id: Optional[str] = None
+    response_headers: Optional[dict] = None
+    status_code: Optional[int] = None

crawl4ai/onnx_embedding.py

@@ -1,50 +0,0 @@
-# A dependency-light way to run the onnx model
-
-
-import numpy as np
-from typing import List
-import os
-
-__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
-
-def normalize(v):
-    norm = np.linalg.norm(v, axis=1)
-    norm[norm == 0] = 1e-12
-    return v / norm[:, np.newaxis]
-
-# Sampel implementation of the default sentence-transformers model using ONNX
-class DefaultEmbeddingModel():
-
-    def __init__(self):
-        from tokenizers import Tokenizer
-        import onnxruntime as ort
-        # max_seq_length = 256, for some reason sentence-transformers uses 256 even though the HF config has a max length of 128
-        # https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/docs/_static/html/models_en_sentence_embeddings.html#LL480
-        self.tokenizer = Tokenizer.from_file(os.path.join(__location__, "models/onnx/tokenizer.json"))
-        self.tokenizer.enable_truncation(max_length=256)
-        self.tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256)
-        self.model = ort.InferenceSession(os.path.join(__location__,"models/onnx/model.onnx"))
-        
-
-    def __call__(self, documents: List[str], batch_size: int = 32):
-        all_embeddings = []
-        for i in range(0, len(documents), batch_size):
-            batch = documents[i:i + batch_size]
-            encoded = [self.tokenizer.encode(d) for d in batch]
-            input_ids = np.array([e.ids for e in encoded])
-            attention_mask = np.array([e.attention_mask for e in encoded])
-            onnx_input = {
-                "input_ids": np.array(input_ids, dtype=np.int64),
-                "attention_mask": np.array(attention_mask, dtype=np.int64),
-                "token_type_ids": np.array([np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64),
-            }
-            model_output = self.model.run(None, onnx_input)
-            last_hidden_state = model_output[0]
-            # Perform mean pooling with attention weighting
-            input_mask_expanded = np.broadcast_to(np.expand_dims(attention_mask, -1), last_hidden_state.shape)
-            embeddings = np.sum(last_hidden_state * input_mask_expanded, 1) / np.clip(input_mask_expanded.sum(1), a_min=1e-9, a_max=None)
-            embeddings = normalize(embeddings).astype(np.float32)
-            all_embeddings.append(embeddings)
-        return np.concatenate(all_embeddings)
-

crawl4ai/prompts.py

@@ -1,4 +1,4 @@
-PROMPT_EXTRACT_BLOCKS = """YHere is the URL of the webpage:
+PROMPT_EXTRACT_BLOCKS = """Here is the URL of the webpage:
 <url>{URL}</url>
 
 And here is the cleaned HTML content of that webpage:
@@ -79,7 +79,7 @@ To generate the JSON objects:
 2. For each block:
    a. Assign it an index based on its order in the content.
    b. Analyze the content and generate ONE semantic tag that describe what the block is about.
-   c. Extract the text content, EXACTLY SAME AS GIVE DATA, clean it up if needed, and store it as a list of strings in the "content" field.
+   c. Extract the text content, EXACTLY SAME AS THE GIVE DATA, clean it up if needed, and store it as a list of strings in the "content" field.
 
 3. Ensure that the order of the JSON objects matches the order of the blocks as they appear in the original HTML content.
 

crawl4ai/utils.py

@@ -1,12 +1,12 @@
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from bs4 import BeautifulSoup, Comment, element, Tag, NavigableString
-import html2text
 import json
 import html
 import re
 import os
-from html2text import HTML2Text
+import platform
+from .html2text import HTML2Text
 from .prompts import PROMPT_EXTRACT_BLOCKS
 from .config import *
 from pathlib import Path
@@ -18,6 +18,46 @@ from requests.exceptions import InvalidSchema
 class InvalidCSSSelectorError(Exception):
     pass
 
+def calculate_semaphore_count():
+    cpu_count = os.cpu_count()
+    memory_gb = get_system_memory() / (1024 ** 3)  # Convert to GB
+    base_count = max(1, cpu_count // 2)
+    memory_based_cap = int(memory_gb / 2)  # Assume 2GB per instance
+    return min(base_count, memory_based_cap)
+
+def get_system_memory():
+    system = platform.system()
+    if system == "Linux":
+        with open('/proc/meminfo', 'r') as mem:
+            for line in mem:
+                if line.startswith('MemTotal:'):
+                    return int(line.split()[1]) * 1024  # Convert KB to bytes
+    elif system == "Darwin":  # macOS
+        import subprocess
+        output = subprocess.check_output(['sysctl', '-n', 'hw.memsize']).decode('utf-8')
+        return int(output.strip())
+    elif system == "Windows":
+        import ctypes
+        kernel32 = ctypes.windll.kernel32
+        c_ulonglong = ctypes.c_ulonglong
+        class MEMORYSTATUSEX(ctypes.Structure):
+            _fields_ = [
+                ('dwLength', ctypes.c_ulong),
+                ('dwMemoryLoad', ctypes.c_ulong),
+                ('ullTotalPhys', c_ulonglong),
+                ('ullAvailPhys', c_ulonglong),
+                ('ullTotalPageFile', c_ulonglong),
+                ('ullAvailPageFile', c_ulonglong),
+                ('ullTotalVirtual', c_ulonglong),
+                ('ullAvailVirtual', c_ulonglong),
+                ('ullAvailExtendedVirtual', c_ulonglong),
+            ]
+        memoryStatus = MEMORYSTATUSEX()
+        memoryStatus.dwLength = ctypes.sizeof(MEMORYSTATUSEX)
+        kernel32.GlobalMemoryStatusEx(ctypes.byref(memoryStatus))
+        return memoryStatus.ullTotalPhys
+    else:
+        raise OSError("Unsupported operating system")
 
 def get_home_folder():
     home_folder = os.path.join(Path.home(), ".crawl4ai")
@@ -90,7 +130,7 @@ def split_and_parse_json_objects(json_string):
     return parsed_objects, unparsed_segments
 
 def sanitize_html(html):
-    # Replace all weird and special characters with an empty string
+    # Replace all unwanted and special characters with an empty string
     sanitized_html = html
     # sanitized_html = re.sub(r'[^\w\s.,;:!?=\[\]{}()<>\/\\\-"]', '', html)
 
@@ -141,9 +181,22 @@ def escape_json_string(s):
 class CustomHTML2Text(HTML2Text):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.ignore_links = True
         self.inside_pre = False
         self.inside_code = False
+        
+        self.skip_internal_links = False
+        self.single_line_break = False
+        self.mark_code = False
+        self.include_sup_sub = False
+        self.body_width = 0
+        self.ignore_mailto_links = True
+        self.ignore_links = False
+        self.escape_backslash = False
+        self.escape_dot = False
+        self.escape_plus = False
+        self.escape_dash = False
+        self.escape_snob = False
+
 
     def handle_tag(self, tag, attrs, start):
         if tag == 'pre':
@@ -153,6 +206,10 @@ class CustomHTML2Text(HTML2Text):
             else:
                 self.o('\n```')
                 self.inside_pre = False
+        elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
+            pass
+
+
         # elif tag == 'code' and not self.inside_pre:
         #     if start:
         #         if not self.inside_pre:
@@ -260,7 +317,7 @@ def get_content_of_website(url, html, word_count_threshold = MIN_WORD_THRESHOLD,
             if tag.name != 'img':
                 tag.attrs = {}
 
-        # Extract all img tgas inti [{src: '', alt: ''}]
+        # Extract all img tgas int0 [{src: '', alt: ''}]
         media = {
             'images': [],
             'videos': [],
@@ -298,7 +355,7 @@ def get_content_of_website(url, html, word_count_threshold = MIN_WORD_THRESHOLD,
                 img.decompose()
 
 
-        # Create a function that replace content of all"pre" tage with its inner text
+        # Create a function that replace content of all"pre" tag with its inner text
         def replace_pre_tags_with_text(node):
             for child in node.find_all('pre'):
                 # set child inner html to its text
@@ -441,6 +498,10 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
     
     image_description_min_word_threshold = kwargs.get('image_description_min_word_threshold', IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD)
 
+    for tag in kwargs.get('excluded_tags', []) or []:
+        for el in body.select(tag):
+            el.decompose()
+        
     if css_selector:
         selected_elements = body.select(css_selector)
         if not selected_elements:
@@ -452,102 +513,102 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
     links = {'internal': [], 'external': []}
     media = {'images': [], 'videos': [], 'audios': []}
 
+    # Extract meaningful text for media files from closest parent
+    def find_closest_parent_with_useful_text(tag):
+            current_tag = tag
+            while current_tag:
+                current_tag = current_tag.parent
+                # Get the text content from the parent tag
+                if current_tag:
+                    text_content = current_tag.get_text(separator=' ',strip=True)
+                    # Check if the text content has at least word_count_threshold
+                    if len(text_content.split()) >= image_description_min_word_threshold:
+                        return text_content
+            return None
+
     def process_image(img, url, index, total_images):
-            #Check if an image has valid display and inside undesired html elements
-            def is_valid_image(img, parent, parent_classes):
-                style = img.get('style', '')
-                src = img.get('src', '')
-                classes_to_check = ['button', 'icon', 'logo']
-                tags_to_check = ['button', 'input']
-                return all([
-                    'display:none' not in style,
-                    src,
-                    not any(s in var for var in [src, img.get('alt', ''), *parent_classes] for s in classes_to_check),
-                    parent.name not in tags_to_check
-                ])
+        #Check if an image has valid display and inside undesired html elements
+        def is_valid_image(img, parent, parent_classes):
+            style = img.get('style', '')
+            src = img.get('src', '')
+            classes_to_check = ['button', 'icon', 'logo']
+            tags_to_check = ['button', 'input']
+            return all([
+                'display:none' not in style,
+                src,
+                not any(s in var for var in [src, img.get('alt', ''), *parent_classes] for s in classes_to_check),
+                parent.name not in tags_to_check
+            ])
 
-            #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
-                def parse_dimension(dimension):
-                    if dimension:
-                        match = re.match(r"(\d+)(\D*)", dimension)
-                        if match:
-                            number = int(match.group(1))
-                            unit = match.group(2) or 'px'  # Default unit is 'px' if not specified
-                            return number, unit
-                    return None, None
+        #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
+            def parse_dimension(dimension):
+                if dimension:
+                    match = re.match(r"(\d+)(\D*)", dimension)
+                    if match:
+                        number = int(match.group(1))
+                        unit = match.group(2) or 'px'  # Default unit is 'px' if not specified
+                        return number, unit
+                return None, None
 
-                # 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
-                    img_url = urljoin(base_url,img.get('src'))
-                    try:
-                        response = requests.head(img_url)
-                        if response.status_code == 200:
-                            return response.headers.get('Content-Length',None)
-                        else:
-                            print(f"Failed to retrieve file size for {img_url}")
-                            return None
-                    except InvalidSchema as e:
+            # 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
+                img_url = urljoin(base_url,img.get('src'))
+                try:
+                    response = requests.head(img_url)
+                    if response.status_code == 200:
+                        return response.headers.get('Content-Length',None)
+                    else:
+                        print(f"Failed to retrieve file size for {img_url}")
                         return None
-                    finally:
-                        return
+                except InvalidSchema as e:
+                    return None
+                finally:
+                    return
 
-                image_height = img.get('height')
-                height_value, height_unit = parse_dimension(image_height)
-                image_width =  img.get('width')
-                width_value, width_unit = parse_dimension(image_width)
-                image_size = 0 #int(fetch_image_file_size(img,base_url) or 0)
-                image_format = os.path.splitext(img.get('src',''))[1].lower()
-                # Remove . from format
-                image_format = image_format.strip('.')
-                score = 0
-                if height_value:
-                    if height_unit == 'px' and height_value > 150:
-                        score += 1
-                    if height_unit in ['%','vh','vmin','vmax'] and height_value >30:
-                        score += 1
-                if width_value:
-                    if width_unit == 'px' and width_value > 150:
-                        score += 1
-                    if width_unit in ['%','vh','vmin','vmax'] and width_value >30:
-                        score += 1
-                if image_size > 10000:
+            image_height = img.get('height')
+            height_value, height_unit = parse_dimension(image_height)
+            image_width =  img.get('width')
+            width_value, width_unit = parse_dimension(image_width)
+            image_size = 0 #int(fetch_image_file_size(img,base_url) or 0)
+            image_format = os.path.splitext(img.get('src',''))[1].lower()
+            # Remove . from format
+            image_format = image_format.strip('.')
+            score = 0
+            if height_value:
+                if height_unit == 'px' and height_value > 150:
                     score += 1
-                if img.get('alt') != '':
-                    score+=1
-                if any(image_format==format for format in ['jpg','png','webp']):
-                    score+=1
-                if index/images_count<0.5:
-                    score+=1
-                return score
+                if height_unit in ['%','vh','vmin','vmax'] and height_value >30:
+                    score += 1
+            if width_value:
+                if width_unit == 'px' and width_value > 150:
+                    score += 1
+                if width_unit in ['%','vh','vmin','vmax'] and width_value >30:
+                    score += 1
+            if image_size > 10000:
+                score += 1
+            if img.get('alt') != '':
+                score+=1
+            if any(image_format==format for format in ['jpg','png','webp']):
+                score+=1
+            if index/images_count<0.5:
+                score+=1
+            return score
 
-            # Extract meaningful text for images from closest parent
-            def find_closest_parent_with_useful_text(tag):
-                current_tag = tag
-                while current_tag:
-                    current_tag = current_tag.parent
-                    # Get the text content of the parent tag
-                    if current_tag:
-                        text_content = current_tag.get_text(separator=' ',strip=True)
-                        # Check if the text content has at least word_count_threshold
-                        if len(text_content.split()) >= image_description_min_word_threshold:
-                            return text_content
-                return None
-
-            if not is_valid_image(img, img.parent, img.parent.get('class', [])):
-                return None
-            score = score_image_for_usefulness(img, url, index, total_images)
-            if score <= IMAGE_SCORE_THRESHOLD:
-                return None
-            return {
-                'src': img.get('src', ''),
-                'alt': img.get('alt', ''),
-                'desc': find_closest_parent_with_useful_text(img),
-                'score': score,
-                'type': 'image'
-            }
+        if not is_valid_image(img, img.parent, img.parent.get('class', [])):
+            return None
+        score = score_image_for_usefulness(img, url, index, total_images)
+        if score <= IMAGE_SCORE_THRESHOLD:
+            return None
+        return {
+            'src': img.get('src', '').replace('\\"', '"').strip(),
+            'alt': img.get('alt', ''),
+            'desc': find_closest_parent_with_useful_text(img),
+            'score': score,
+            'type': 'image'
+        }
 
     def process_element(element: element.PageElement) -> bool:
         try:
@@ -579,7 +640,16 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
                 media[f"{element.name}s"].append({
                     'src': element.get('src'),
                     'alt': element.get('alt'),
-                    'type': element.name
+                    'type': element.name,
+                    'description': find_closest_parent_with_useful_text(element)
+                })
+                source_tags = element.find_all('source')
+                for source_tag in source_tags:
+                    media[f"{element.name}s"].append({
+                    'src': source_tag.get('src'),
+                    'alt': element.get('alt'),
+                    'type': element.name,
+                    'description': find_closest_parent_with_useful_text(element)
                 })
                 return True  # Always keep video and audio elements
 
@@ -638,8 +708,8 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
     for img in imgs:
         src = img.get('src', '')
         if base64_pattern.match(src):
-            # Replace base64 data with empty string
             img['src'] = base64_pattern.sub('', src)
+
     cleaned_html = str(body).replace('\n\n', '\n').replace('  ', ' ')
     cleaned_html = sanitize_html(cleaned_html)
 
@@ -721,7 +791,14 @@ def extract_xml_data(tags, string):
     return data
     
 # Function to perform the completion with exponential backoff
-def perform_completion_with_backoff(provider, prompt_with_variables, api_token, json_response = False, base_url=None):
+def perform_completion_with_backoff(
+    provider, 
+    prompt_with_variables, 
+    api_token, 
+    json_response = False, 
+    base_url=None,
+    **kwargs
+    ):
     from litellm import completion 
     from litellm.exceptions import RateLimitError
     max_attempts = 3
@@ -730,6 +807,9 @@ def perform_completion_with_backoff(provider, prompt_with_variables, api_token,
     extra_args = {}
     if json_response:
         extra_args["response_format"] = { "type": "json_object" }
+        
+    if kwargs.get("extra_args"):
+        extra_args.update(kwargs["extra_args"])
     
     for attempt in range(max_attempts):
         try:
@@ -840,7 +920,6 @@ def extract_blocks_batch(batch_data, provider = "groq/llama3-70b-8192", api_toke
     
     return sum(all_blocks, [])
 
-
 def merge_chunks_based_on_token_threshold(chunks, token_threshold):
     """
     Merges small chunks into larger ones based on the total token threshold.
@@ -886,7 +965,6 @@ def process_sections(url: str, sections: list, provider: str, api_token: str, ba
     
     return extracted_content
 
-
 def wrap_text(draw, text, font, max_width):
     # Wrap the text to fit within the specified width
     lines = []
@@ -898,9 +976,57 @@ def wrap_text(draw, text, font, max_width):
         lines.append(line)
     return '\n'.join(lines)
 
-
 def format_html(html_string):
     soup = BeautifulSoup(html_string, 'html.parser')
     return soup.prettify()
 
+def normalize_url(href, base_url):
+    """Normalize URLs to ensure consistent format"""
+    # Extract protocol and domain from base URL
+    try:
+        base_parts = base_url.split('/')
+        protocol = base_parts[0]
+        domain = base_parts[2]
+    except IndexError:
+        raise ValueError(f"Invalid base URL format: {base_url}")
+    
+    # Handle special protocols
+    special_protocols = {'mailto:', 'tel:', 'ftp:', 'file:', 'data:', 'javascript:'}
+    if any(href.lower().startswith(proto) for proto in special_protocols):
+        return href.strip()
+        
+    # Handle anchor links
+    if href.startswith('#'):
+        return f"{base_url}{href}"
+        
+    # Handle protocol-relative URLs
+    if href.startswith('//'):
+        return f"{protocol}{href}"
+        
+    # Handle root-relative URLs
+    if href.startswith('/'):
+        return f"{protocol}//{domain}{href}"
+        
+    # Handle relative URLs
+    if not href.startswith(('http://', 'https://')):
+        # Remove leading './' if present
+        href = href.lstrip('./')
+        return f"{protocol}//{domain}/{href}"
+        
+    return href.strip()
 
+def is_external_url(url, base_domain):
+    """Determine if a URL is external"""
+    special_protocols = {'mailto:', 'tel:', 'ftp:', 'file:', 'data:', 'javascript:'}
+    if any(url.lower().startswith(proto) for proto in special_protocols):
+        return True
+        
+    try:
+        # Handle URLs with protocol
+        if url.startswith(('http://', 'https://')):
+            url_domain = url.split('/')[2]
+            return base_domain.lower() not in url_domain.lower()
+    except IndexError:
+        return False
+        
+    return False

crawl4ai/web_crawler.py

@@ -12,45 +12,28 @@ from typing import List
 from concurrent.futures import ThreadPoolExecutor
 from .config import *
 import warnings
+import json
 warnings.filterwarnings("ignore", message='Field "model_name" has conflict with protected namespace "model_".')
 
 
 class WebCrawler:
-    def __init__(
-        self,
-        # db_path: str = None,
-        crawler_strategy: CrawlerStrategy = None,
-        always_by_pass_cache: bool = False,
-        verbose: bool = False,
-        proxy: str = None,
-    ):
-        # self.db_path = db_path
-        self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose, proxy=proxy)
+    def __init__(self, crawler_strategy: CrawlerStrategy = None, always_by_pass_cache: bool = False, verbose: bool = False):
+        self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose)
         self.always_by_pass_cache = always_by_pass_cache
-
-        # Create the .crawl4ai folder in the user's home directory if it doesn't exist
         self.crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
         os.makedirs(self.crawl4ai_folder, exist_ok=True)
         os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
-
-        # If db_path is not provided, use the default path
-        # if not db_path:
-            # self.db_path = f"{self.crawl4ai_folder}/crawl4ai.db"
-        
-        # flush_db()
         init_db()
-        
         self.ready = False
         
     def warmup(self):
         print("[LOG] 🌤️  Warming up the WebCrawler")
-        result = self.run(
+        self.run(
             url='https://google.com/',
             word_count_threshold=5,
-            extraction_strategy= NoExtractionStrategy(),
+            extraction_strategy=NoExtractionStrategy(),
             bypass_cache=False,
-            verbose = False,
-            # warmup=True
+            verbose=False
         )
         self.ready = True
         print("[LOG] 🌞 WebCrawler is ready to crawl")
@@ -140,12 +123,8 @@ class WebCrawler:
                 if not isinstance(chunking_strategy, ChunkingStrategy):
                     raise ValueError("Unsupported chunking strategy")
                 
-                # if word_count_threshold < MIN_WORD_THRESHOLD:
-                #     word_count_threshold = MIN_WORD_THRESHOLD
-                    
-                word_count_threshold = max(word_count_threshold, 0)
+                word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
 
-                # Check cache first
                 cached = None
                 screenshot_data = None
                 extracted_content = None
@@ -170,7 +149,7 @@ class WebCrawler:
                     html = sanitize_input_encode(self.crawler_strategy.crawl(url, **kwargs))
                     t2 = time.time()
                     if verbose:
-                        print(f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1} seconds")
+                        print(f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds")
                     if screenshot:
                         screenshot_data = self.crawler_strategy.take_screenshot()
 
@@ -201,13 +180,10 @@ class WebCrawler:
             t = time.time()
             # Extract content from HTML
             try:
-                # t1 = time.time()
-                # result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
-                # print(f"[LOG] 🚀 Crawling done for {url}, success: True, time taken: {time.time() - t1} seconds")
                 t1 = time.time()
                 result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
                 if verbose:
-                    print(f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1} seconds")
+                    print(f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds")
                 
                 if result is None:
                     raise ValueError(f"Failed to extract content from the website: {url}")
@@ -226,10 +202,10 @@ class WebCrawler:
 
                 sections = chunking_strategy.chunk(markdown)
                 extracted_content = extraction_strategy.run(url, sections)
-                extracted_content = json.dumps(extracted_content, indent=4, default=str)
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
 
                 if verbose:
-                    print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds.")
+                    print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds.")
                 
             screenshot = None if not screenshot else screenshot
             

docker-compose.yml

@@ -1,10 +0,0 @@
-version: '3.8'
-
-services:
-  web:
-    build: .
-    command: uvicorn main:app --host 0.0.0.0 --port 80 --workers $(nproc)
-    ports:
-      - "80:80"
-    environment:
-      - PYTHONUNBUFFERED=1

docs/details/extraction.md

@@ -0,0 +1,157 @@
+### Extraction Strategies
+
+#### 1. LLMExtractionStrategy
+```python
+LLMExtractionStrategy(
+    # Core Parameters
+    provider: str = DEFAULT_PROVIDER,  # LLM provider (e.g., "openai/gpt-4", "huggingface/...", "ollama/...")
+    api_token: Optional[str] = None,  # API token for the provider
+    instruction: str = None,  # Custom instruction for extraction
+    schema: Dict = None,  # Pydantic model schema for structured extraction
+    extraction_type: str = "block",  # Type of extraction: "block" or "schema"
+    
+    # Chunking Parameters
+    chunk_token_threshold: int = CHUNK_TOKEN_THRESHOLD,  # Maximum tokens per chunk
+    overlap_rate: float = OVERLAP_RATE,  # Overlap between chunks
+    word_token_rate: float = WORD_TOKEN_RATE,  # Conversion rate from words to tokens
+    apply_chunking: bool = True,  # Whether to apply text chunking
+    
+    # API Configuration
+    base_url: str = None,  # Base URL for API calls
+    api_base: str = None,  # Alternative base URL
+    extra_args: Dict = {},  # Additional provider-specific arguments
+    
+    verbose: bool = False  # Enable verbose logging
+)
+```
+
+Usage Example:
+```python
+class NewsArticle(BaseModel):
+    title: str
+    content: str
+
+strategy = LLMExtractionStrategy(
+    provider="ollama/nemotron",
+    api_token="your-token",
+    schema=NewsArticle.schema(),
+    instruction="Extract news article content with title and main text"
+)
+
+result = await crawler.arun(url="https://example.com", extraction_strategy=strategy)
+```
+
+#### 2. JsonCssExtractionStrategy
+```python
+JsonCssExtractionStrategy(
+    schema: Dict[str, Any],  # Schema defining extraction rules
+    verbose: bool = False  # Enable verbose logging
+)
+
+# Schema Structure
+schema = {
+    "name": str,  # Name of the extraction schema
+    "baseSelector": str,  # CSS selector for base elements
+    "fields": [
+        {
+            "name": str,  # Field name
+            "selector": str,  # CSS selector
+            "type": str,  # Field type: "text", "attribute", "html", "regex", "nested", "list", "nested_list"
+            "attribute": str,  # For type="attribute"
+            "pattern": str,  # For type="regex"
+            "transform": str,  # Optional: "lowercase", "uppercase", "strip"
+            "default": Any,  # Default value if extraction fails
+            "fields": List[Dict],  # For nested/list types
+        }
+    ]
+}
+```
+
+Usage Example:
+```python
+schema = {
+    "name": "News Articles",
+    "baseSelector": "article.news-item",
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h1",
+            "type": "text",
+            "transform": "strip"
+        },
+        {
+            "name": "date",
+            "selector": ".date",
+            "type": "attribute",
+            "attribute": "datetime"
+        }
+    ]
+}
+
+strategy = JsonCssExtractionStrategy(schema)
+result = await crawler.arun(url="https://example.com", extraction_strategy=strategy)
+```
+
+#### 3. CosineStrategy
+```python
+CosineStrategy(
+    # Content Filtering
+    semantic_filter: str = None,  # Keyword filter for document filtering
+    word_count_threshold: int = 10,  # Minimum words per cluster
+    sim_threshold: float = 0.3,  # Similarity threshold for filtering
+    
+    # Clustering Parameters
+    max_dist: float = 0.2,  # Maximum distance for clustering
+    linkage_method: str = 'ward',  # Clustering linkage method
+    top_k: int = 3,  # Number of top categories to extract
+    
+    # Model Configuration
+    model_name: str = 'sentence-transformers/all-MiniLM-L6-v2',  # Embedding model
+    
+    verbose: bool = False  # Enable verbose logging
+)
+```
+
+### Chunking Strategies
+
+#### 1. RegexChunking
+```python
+RegexChunking(
+    patterns: List[str] = None  # List of regex patterns for splitting text
+    # Default pattern: [r'\n\n']
+)
+```
+
+Usage Example:
+```python
+chunker = RegexChunking(patterns=[r'\n\n', r'\.\s+'])  # Split on double newlines and sentences
+chunks = chunker.chunk(text)
+```
+
+#### 2. SlidingWindowChunking
+```python
+SlidingWindowChunking(
+    window_size: int = 100,  # Size of the window in words
+    step: int = 50,  # Number of words to slide the window
+)
+```
+
+Usage Example:
+```python
+chunker = SlidingWindowChunking(window_size=200, step=100)
+chunks = chunker.chunk(text)  # Creates overlapping chunks of 200 words, moving 100 words at a time
+```
+
+#### 3. OverlappingWindowChunking
+```python
+OverlappingWindowChunking(
+    window_size: int = 1000,  # Size of each chunk in words
+    overlap: int = 100  # Number of words to overlap between chunks
+)
+```
+
+Usage Example:
+```python
+chunker = OverlappingWindowChunking(window_size=500, overlap=50)
+chunks = chunker.chunk(text)  # Creates 500-word chunks with 50-word overlap
+```

docs/details/feature_lists.md

@@ -0,0 +1,175 @@
+# Features
+
+## Current Features
+1. Async-first architecture for high-performance web crawling
+2. Built-in anti-bot detection bypass ("magic mode")
+3. Multiple browser engine support (Chromium, Firefox, WebKit)
+4. Smart session management with automatic cleanup
+5. Automatic content cleaning and relevance scoring
+6. Built-in markdown generation with formatting preservation
+7. Intelligent image scoring and filtering
+8. Automatic popup and overlay removal
+9. Smart wait conditions (CSS/JavaScript based)
+10. Multi-provider LLM integration (OpenAI, HuggingFace, Ollama)
+11. Schema-based structured data extraction
+12. Automated iframe content processing
+13. Intelligent link categorization (internal/external)
+14. Multiple chunking strategies for large content
+15. Real-time HTML cleaning and sanitization
+16. Automatic screenshot capabilities
+17. Social media link filtering
+18. Semantic similarity-based content clustering
+19. Human behavior simulation for anti-bot bypass
+20. Proxy support with authentication
+21. Automatic resource cleanup
+22. Custom CSS selector-based extraction
+23. Automatic content relevance scoring ("fit" content)
+24. Recursive website crawling capabilities
+25. Flexible hook system for customization
+26. Built-in caching system
+27. Domain-based content filtering
+28. Dynamic content handling with JavaScript execution
+29. Automatic media content extraction and classification
+30. Metadata extraction and processing
+31. Customizable HTML to Markdown conversion
+32. Token-aware content chunking for LLM processing
+33. Automatic response header and status code handling
+34. Browser fingerprint customization
+35. Multiple extraction strategies (LLM, CSS, Cosine, XPATH)
+36. Automatic error image generation for failed screenshots
+37. Smart content overlap handling for large texts
+38. Built-in rate limiting for batch processing
+39. Automatic cookie handling
+40. Browser Console logging and debugging capabilities
+
+## Feature Techs
+• Browser Management
+  - Asynchronous browser control
+  - Multi-browser support (Chromium, Firefox, WebKit)
+  - Headless mode support
+  - Browser cleanup and resource management
+  - Custom browser arguments and configuration
+  - Context management with `__aenter__` and `__aexit__`
+
+• Session Handling
+  - Session management with TTL (Time To Live)
+  - Session reuse capabilities
+  - Session cleanup for expired sessions
+  - Session-based context preservation
+
+• Stealth Features
+  - Playwright stealth configuration
+  - Navigator properties override
+  - WebDriver detection evasion
+  - Chrome app simulation
+  - Plugin simulation
+  - Language preferences simulation
+  - Hardware concurrency simulation
+  - Media codecs simulation
+
+• Network Features
+  - Proxy support with authentication
+  - Custom headers management
+  - Cookie handling
+  - Response header capture
+  - Status code tracking
+  - Network idle detection
+
+• Page Interaction
+  - Smart wait functionality for multiple conditions
+  - CSS selector-based waiting
+  - JavaScript condition waiting
+  - Custom JavaScript execution
+  - User interaction simulation (mouse/keyboard)
+  - Page scrolling
+  - Timeout management
+  - Load state monitoring
+
+• Content Processing
+  - HTML content extraction
+  - Iframe processing and content extraction
+  - Delayed content retrieval
+  - Content caching
+  - Cache file management
+  - HTML cleaning and processing
+
+• Image Handling
+  - Screenshot capabilities (full page)
+  - Base64 encoding of screenshots
+  - Image dimension updating
+  - Image filtering (size/visibility)
+  - Error image generation
+  - Natural width/height preservation
+
+• Overlay Management
+  - Popup removal
+  - Cookie notice removal
+  - Newsletter dialog removal
+  - Modal removal
+  - Fixed position element removal
+  - Z-index based overlay detection
+  - Visibility checking
+
+• Hook System
+  - Browser creation hooks
+  - User agent update hooks
+  - Execution start hooks
+  - Navigation hooks (before/after goto)
+  - HTML retrieval hooks
+  - HTML return hooks
+
+• Error Handling
+  - Browser error catching
+  - Network error handling
+  - Timeout handling
+  - Screenshot error recovery
+  - Invalid selector handling
+  - General exception management
+
+• Performance Features
+  - Concurrent URL processing
+  - Semaphore-based rate limiting
+  - Async gathering of results
+  - Resource cleanup
+  - Memory management
+
+• Debug Features
+  - Console logging
+  - Page error logging
+  - Verbose mode
+  - Error message generation
+  - Warning system
+
+• Security Features
+  - Certificate error handling
+  - Sandbox configuration
+  - GPU handling
+  - CSP (Content Security Policy) compliant waiting
+
+• Configuration
+  - User agent customization
+  - Viewport configuration
+  - Timeout configuration
+  - Browser type selection
+  - Proxy configuration
+  - Header configuration
+
+• Data Models
+  - Pydantic model for responses
+  - Type hints throughout code
+  - Structured response format
+  - Optional response fields
+
+• File System Integration
+  - Cache directory management
+  - File path handling
+  - Cache metadata storage
+  - File read/write operations
+
+• Metadata Handling
+  - Response headers capture
+  - Status code tracking
+  - Cache metadata
+  - Session tracking
+  - Timestamp management
+

docs/details/features.md

@@ -0,0 +1,150 @@
+### 1. Basic Web Crawling
+```python
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(url="https://example.com")
+    print(result.markdown)  # Get clean markdown content
+    print(result.html)      # Get raw HTML
+    print(result.cleaned_html)  # Get cleaned HTML
+```
+
+### 2. Browser Control Options
+- Multiple Browser Support
+```python
+# Choose between different browser engines
+crawler = AsyncWebCrawler(browser_type="firefox")  # or "chromium", "webkit"
+crawler = AsyncWebCrawler(headless=False)  # For visible browser
+```
+
+- Proxy Configuration
+```python
+crawler = AsyncWebCrawler(proxy="http://proxy.example.com:8080")
+# Or with authentication
+crawler = AsyncWebCrawler(proxy_config={
+    "server": "http://proxy.example.com:8080",
+    "username": "user",
+    "password": "pass"
+})
+```
+
+### 3. Content Selection & Filtering
+- CSS Selector Support
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    css_selector=".main-content"  # Extract specific content
+)
+```
+
+- Content Filtering Options
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    word_count_threshold=10,  # Minimum words per block
+    excluded_tags=['form', 'header'],  # Tags to exclude
+    exclude_external_links=True,  # Remove external links
+    exclude_social_media_links=True,  # Remove social media links
+    exclude_external_images=True  # Remove external images
+)
+```
+
+### 4. Dynamic Content Handling
+- JavaScript Execution
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    js_code="window.scrollTo(0, document.body.scrollHeight)"  # Execute custom JS
+)
+```
+
+- Wait Conditions
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    wait_for="css:.my-element",  # Wait for element
+    wait_for="js:() => document.readyState === 'complete'"  # Wait for condition
+)
+```
+
+### 5. Anti-Bot Protection Handling
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    simulate_user=True,  # Simulate human behavior
+    override_navigator=True,  # Mask automation signals
+    magic=True  # Enable all anti-detection features
+)
+```
+
+### 6. Session Management
+```python
+session_id = "my_session"
+result1 = await crawler.arun(url="https://example.com/page1", session_id=session_id)
+result2 = await crawler.arun(url="https://example.com/page2", session_id=session_id)
+await crawler.crawler_strategy.kill_session(session_id)
+```
+
+### 7. Media Handling
+- Screenshot Capture
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    screenshot=True
+)
+base64_screenshot = result.screenshot
+```
+
+- Media Extraction
+```python
+result = await crawler.arun(url="https://example.com")
+print(result.media['images'])  # List of images
+print(result.media['videos'])  # List of videos
+print(result.media['audios'])  # List of audio files
+```
+
+### 8. Structured Data Extraction
+- CSS-based Extraction
+```python
+schema = {
+    "name": "News Articles",
+    "baseSelector": "article",
+    "fields": [
+        {"name": "title", "selector": "h1", "type": "text"},
+        {"name": "date", "selector": ".date", "type": "text"}
+    ]
+}
+extraction_strategy = JsonCssExtractionStrategy(schema)
+result = await crawler.arun(
+    url="https://example.com",
+    extraction_strategy=extraction_strategy
+)
+structured_data = json.loads(result.extracted_content)
+```
+
+- LLM-based Extraction (Multiple Providers)
+```python
+class NewsArticle(BaseModel):
+    title: str
+    summary: str
+
+strategy = LLMExtractionStrategy(
+    provider="ollama/nemotron",  # or "huggingface/...", "ollama/..."
+    api_token="your-token",
+    schema=NewsArticle.schema(),
+    instruction="Extract news article details..."
+)
+result = await crawler.arun(
+    url="https://example.com",
+    extraction_strategy=strategy
+)
+```
+
+### 9. Content Cleaning & Processing
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    remove_overlay_elements=True,  # Remove popups/modals
+    process_iframes=True,  # Process iframe content
+)
+print(result.fit_markdown)  # Get most relevant content
+print(result.fit_html)     # Get cleaned HTML
+```

docs/details/features_details.md

@@ -0,0 +1,457 @@
+I'll expand the outline with detailed descriptions and examples based on all the provided files. I'll start with the first few sections:
+
+### 1. Basic Web Crawling
+Basic web crawling provides the foundation for extracting content from websites. The library supports both simple single-page crawling and recursive website crawling.
+
+```python
+# Simple page crawling
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(url="https://example.com")
+    print(result.html)        # Raw HTML
+    print(result.markdown)    # Cleaned markdown
+    print(result.cleaned_html)  # Cleaned HTML
+
+# Recursive website crawling
+class SimpleWebsiteScraper:
+    def __init__(self, crawler: AsyncWebCrawler):
+        self.crawler = crawler
+
+    async def scrape(self, start_url: str, max_depth: int):
+        results = await self.scrape_recursive(start_url, max_depth)
+        return results
+
+# Usage
+async with AsyncWebCrawler() as crawler:
+    scraper = SimpleWebsiteScraper(crawler)
+    results = await scraper.scrape("https://example.com", depth=2)
+```
+
+### 2. Browser Control Options
+The library provides extensive control over browser behavior, allowing customization of browser type, headless mode, and proxy settings.
+
+```python
+# Browser Type Selection
+async with AsyncWebCrawler(
+    browser_type="firefox",  # Options: "chromium", "firefox", "webkit"
+    headless=False,         # For visible browser
+    verbose=True           # Enable logging
+) as crawler:
+    result = await crawler.arun(url="https://example.com")
+
+# Proxy Configuration
+async with AsyncWebCrawler(
+    proxy_config={
+        "server": "http://proxy.example.com:8080",
+        "username": "user",
+        "password": "pass"
+    },
+    headers={
+        "User-Agent": "Custom User Agent",
+        "Accept-Language": "en-US,en;q=0.9"
+    }
+) as crawler:
+    result = await crawler.arun(url="https://example.com")
+```
+
+### 3. Content Selection & Filtering
+The library offers multiple ways to select and filter content, from CSS selectors to word count thresholds.
+
+```python
+# CSS Selector and Content Filtering
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        css_selector="article.main-content",  # Extract specific content
+        word_count_threshold=10,              # Minimum words per block
+        excluded_tags=['form', 'header'],     # Tags to exclude
+        exclude_external_links=True,          # Remove external links
+        exclude_social_media_links=True,      # Remove social media links
+        exclude_domains=["pinterest.com", "facebook.com"]  # Exclude specific domains
+    )
+
+# Custom HTML to Text Options
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        html2text={
+            "escape_dot": False,
+            "links_each_paragraph": True,
+            "protect_links": True
+        }
+    )
+```
+
+### 4. Dynamic Content Handling
+The library provides sophisticated handling of dynamic content with JavaScript execution and wait conditions.
+
+```python
+# JavaScript Execution and Wait Conditions
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        js_code=[
+            "window.scrollTo(0, document.body.scrollHeight);",
+            "document.querySelector('.load-more').click();"
+        ],
+        wait_for="css:.dynamic-content",  # Wait for element
+        delay_before_return_html=2.0      # Wait after JS execution
+    )
+
+# Smart Wait Conditions
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        wait_for="""() => {
+            return document.querySelectorAll('.item').length > 10;
+        }""",
+        page_timeout=60000  # 60 seconds timeout
+    )
+```
+
+### 5. Advanced Link Analysis
+The library provides comprehensive link analysis capabilities, distinguishing between internal and external links, with options for filtering and processing.
+
+```python
+# Basic Link Analysis
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(url="https://example.com")
+    
+    # Access internal and external links
+    for internal_link in result.links['internal']:
+        print(f"Internal: {internal_link['href']} - {internal_link['text']}")
+    
+    for external_link in result.links['external']:
+        print(f"External: {external_link['href']} - {external_link['text']}")
+
+# Advanced Link Filtering
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        exclude_external_links=True,          # Remove all external links
+        exclude_social_media_links=True,      # Remove social media links
+        exclude_social_media_domains=[                # Custom social media domains
+            "facebook.com", "twitter.com", "instagram.com"
+        ],
+        exclude_domains=["pinterest.com"]     # Specific domains to exclude
+    )
+```
+
+### 6. Anti-Bot Protection Handling
+The library includes sophisticated anti-detection mechanisms to handle websites with bot protection.
+
+```python
+# Basic Anti-Detection
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        simulate_user=True,        # Simulate human behavior
+        override_navigator=True    # Override navigator properties
+    )
+
+# Advanced Anti-Detection with Magic Mode
+async with AsyncWebCrawler(headless=False) as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        magic=True,               # Enable all anti-detection features
+        remove_overlay_elements=True,  # Remove popups/modals automatically
+        # Custom navigator properties
+        js_code="""
+        Object.defineProperty(navigator, 'webdriver', {
+            get: () => undefined
+        });
+        """
+    )
+```
+
+### 7. Session Management
+Session management allows maintaining state across multiple requests and handling cookies.
+
+```python
+# Basic Session Management
+async with AsyncWebCrawler() as crawler:
+    session_id = "my_session"
+    
+    # Login
+    login_result = await crawler.arun(
+        url="https://example.com/login",
+        session_id=session_id,
+        js_code="document.querySelector('form').submit();"
+    )
+    
+    # Use same session for subsequent requests
+    protected_result = await crawler.arun(
+        url="https://example.com/protected",
+        session_id=session_id
+    )
+    
+    # Clean up session
+    await crawler.crawler_strategy.kill_session(session_id)
+
+# Advanced Session with Custom Cookies
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        session_id="custom_session",
+        cookies=[{
+            "name": "sessionId",
+            "value": "abc123",
+            "domain": "example.com"
+        }]
+    )
+```
+
+### 8. Screenshot and Media Handling
+The library provides comprehensive media handling capabilities, including screenshots and media content extraction.
+
+```python
+# Screenshot Capture
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        screenshot=True,
+        screenshot_wait_for=2.0  # Wait before taking screenshot
+    )
+    
+    # Save screenshot
+    if result.screenshot:
+        with open("screenshot.png", "wb") as f:
+            f.write(base64.b64decode(result.screenshot))
+
+# Media Extraction
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(url="https://example.com")
+    
+    # Process images with metadata
+    for image in result.media['images']:
+        print(f"Image: {image['src']}")
+        print(f"Alt text: {image['alt']}")
+        print(f"Context: {image['desc']}")
+        print(f"Relevance score: {image['score']}")
+    
+    # Process videos and audio
+    for video in result.media['videos']:
+        print(f"Video: {video['src']}")
+    for audio in result.media['audios']:
+        print(f"Audio: {audio['src']}")
+```
+
+### 9. Structured Data Extraction & Chunking
+The library supports multiple strategies for structured data extraction and content chunking.
+
+```python
+# LLM-based Extraction
+class NewsArticle(BaseModel):
+    title: str
+    content: str
+    author: str
+
+extraction_strategy = LLMExtractionStrategy(
+    provider='openai/gpt-4',
+    api_token="your-token",
+    schema=NewsArticle.schema(),
+    instruction="Extract news article details",
+    chunk_token_threshold=1000,
+    overlap_rate=0.1
+)
+
+# CSS-based Extraction
+schema = {
+    "name": "Product Listing",
+    "baseSelector": ".product-card",
+    "fields": [
+        {
+            "name": "title",
+            "selector": "h2",
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": ".price",
+            "type": "text",
+            "transform": "strip"
+        }
+    ]
+}
+
+css_strategy = JsonCssExtractionStrategy(schema)
+
+# Text Chunking
+from crawl4ai.chunking_strategy import OverlappingWindowChunking
+
+chunking_strategy = OverlappingWindowChunking(
+    window_size=1000,
+    overlap=100
+)
+
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        extraction_strategy=extraction_strategy,
+        chunking_strategy=chunking_strategy
+    )
+```
+
+
+### 10. Content Cleaning & Processing
+The library provides extensive content cleaning and processing capabilities, ensuring high-quality output in various formats.
+
+```python
+# Basic Content Cleaning
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        remove_overlay_elements=True,  # Remove popups/modals
+        process_iframes=True,          # Process iframe content
+        word_count_threshold=10        # Minimum words per block
+    )
+    
+    print(result.cleaned_html)    # Clean HTML
+    print(result.fit_html)        # Most relevant HTML content
+    print(result.fit_markdown)    # Most relevant markdown content
+
+# Advanced Content Processing
+async with AsyncWebCrawler() as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        excluded_tags=['form', 'header', 'footer', 'nav'],
+        html2text={
+            "escape_dot": False,
+            "body_width": 0,
+            "protect_links": True,
+            "unicode_snob": True,
+            "ignore_links": False,
+            "ignore_images": False,
+            "ignore_emphasis": False,
+            "bypass_tables": False,
+            "ignore_tables": False
+        }
+    )
+```
+
+### Advanced Usage Patterns
+
+#### 1. Combining Multiple Features
+```python
+async with AsyncWebCrawler(
+    browser_type="chromium",
+    headless=False,
+    verbose=True
+) as crawler:
+    result = await crawler.arun(
+        url="https://example.com",
+        # Anti-bot measures
+        magic=True,
+        simulate_user=True,
+        
+        # Content selection
+        css_selector="article.main",
+        word_count_threshold=10,
+        
+        # Dynamic content handling
+        js_code="window.scrollTo(0, document.body.scrollHeight);",
+        wait_for="css:.dynamic-content",
+        
+        # Content filtering
+        exclude_external_links=True,
+        exclude_social_media_links=True,
+        
+        # Media handling
+        screenshot=True,
+        process_iframes=True,
+        
+        # Content cleaning
+        remove_overlay_elements=True
+    )
+```
+
+#### 2. Custom Extraction Pipeline
+```python
+# Define custom schemas and strategies
+class Article(BaseModel):
+    title: str
+    content: str
+    date: str
+
+# CSS extraction for initial content
+css_schema = {
+    "name": "Article Extraction",
+    "baseSelector": "article",
+    "fields": [
+        {"name": "title", "selector": "h1", "type": "text"},
+        {"name": "content", "selector": ".content", "type": "html"},
+        {"name": "date", "selector": ".date", "type": "text"}
+    ]
+}
+
+# LLM processing for semantic analysis
+llm_strategy = LLMExtractionStrategy(
+    provider="ollama/nemotron",
+    api_token="your-token",
+    schema=Article.schema(),
+    instruction="Extract and clean article content"
+)
+
+# Chunking strategy for large content
+chunking = OverlappingWindowChunking(window_size=1000, overlap=100)
+
+async with AsyncWebCrawler() as crawler:
+    # First pass: Extract structure
+    css_result = await crawler.arun(
+        url="https://example.com",
+        extraction_strategy=JsonCssExtractionStrategy(css_schema)
+    )
+    
+    # Second pass: Semantic processing
+    llm_result = await crawler.arun(
+        url="https://example.com",
+        extraction_strategy=llm_strategy,
+        chunking_strategy=chunking
+    )
+```
+
+#### 3. Website Crawling with Custom Processing
+```python
+class CustomWebsiteCrawler:
+    def __init__(self, crawler: AsyncWebCrawler):
+        self.crawler = crawler
+        self.results = {}
+
+    async def process_page(self, url: str) -> Dict:
+        result = await self.crawler.arun(
+            url=url,
+            magic=True,
+            word_count_threshold=10,
+            exclude_external_links=True,
+            process_iframes=True,
+            remove_overlay_elements=True
+        )
+        
+        # Process internal links
+        internal_links = [
+            link['href'] for link in result.links['internal']
+            if self._is_valid_link(link['href'])
+        ]
+        
+        # Extract media
+        media_urls = [img['src'] for img in result.media['images']]
+        
+        return {
+            'content': result.markdown,
+            'links': internal_links,
+            'media': media_urls,
+            'metadata': result.metadata
+        }
+
+    async def crawl_website(self, start_url: str, max_depth: int = 2):
+        visited = set()
+        queue = [(start_url, 0)]
+        
+        while queue:
+            url, depth = queue.pop(0)
+            if depth > max_depth or url in visited:
+                continue
+                
+            visited.add(url)
+            self.results[url] = await self.process_page(url)
+```
+

docs/details/input_output.md

@@ -0,0 +1,282 @@
+### AsyncWebCrawler Constructor Parameters
+```python
+AsyncWebCrawler(
+    # Core Browser Settings
+    browser_type: str = "chromium",  # Options: "chromium", "firefox", "webkit"
+    headless: bool = True,  # Whether to run browser in headless mode
+    verbose: bool = False,  # Enable verbose logging
+    
+    # Cache Settings
+    always_by_pass_cache: bool = False,  # Always bypass cache regardless of run settings
+    base_directory: str = str(Path.home()),  # Base directory for cache storage
+    
+    # Network Settings
+    proxy: str = None,  # Simple proxy URL (e.g., "http://proxy.example.com:8080")
+    proxy_config: Dict = None,  # Advanced proxy settings with auth: {"server": str, "username": str, "password": str}
+    
+    # Browser Behavior
+    sleep_on_close: bool = False,  # Wait before closing browser
+    
+    # Other Settings passed to AsyncPlaywrightCrawlerStrategy
+    user_agent: str = None,  # Custom user agent string
+    headers: Dict[str, str] = {},  # Custom HTTP headers
+    js_code: Union[str, List[str]] = None,  # Default JavaScript to execute
+)
+```
+
+### arun() Method Parameters
+```python
+arun(
+    # Core Parameters
+    url: str,  # Required: URL to crawl
+    
+    # Content Selection
+    css_selector: str = None,  # CSS selector to extract specific content
+    word_count_threshold: int = MIN_WORD_THRESHOLD,  # Minimum words for content blocks
+    
+    # Cache Control
+    bypass_cache: bool = False,  # Bypass cache for this request
+    
+    # Session Management
+    session_id: str = None,  # Session identifier for persistent browsing
+    
+    # Screenshot Options
+    screenshot: bool = False,  # Take page screenshot
+    screenshot_wait_for: float = None,  # Wait time before screenshot
+    
+    # Content Processing
+    process_iframes: bool = False,  # Process iframe content
+    remove_overlay_elements: bool = False,  # Remove popups/modals
+    
+    # Anti-Bot/Detection
+    simulate_user: bool = False,  # Simulate human-like behavior
+    override_navigator: bool = False,  # Override navigator properties
+    magic: bool = False,  # Enable all anti-detection features
+    
+    # Content Filtering
+    excluded_tags: List[str] = None,  # HTML tags to exclude
+    exclude_external_links: bool = False,  # Remove external links
+    exclude_social_media_links: bool = False,  # Remove social media links
+    exclude_external_images: bool = False,  # Remove external images
+    exclude_social_media_domains: List[str] = None,  # Additional social media domains to exclude
+    remove_forms: bool = False,  # Remove all form elements
+    
+    # JavaScript Handling
+    js_code: Union[str, List[str]] = None,  # JavaScript to execute
+    js_only: bool = False,  # Only execute JavaScript without reloading page
+    wait_for: str = None,  # Wait condition (CSS selector or JS function)
+    
+    # Page Loading
+    page_timeout: int = 60000,  # Page load timeout in milliseconds
+    delay_before_return_html: float = None,  # Wait before returning HTML
+    
+    # Debug Options
+    log_console: bool = False,  # Log browser console messages
+    
+    # Content Format Control
+    only_text: bool = False,  # Extract only text content
+    keep_data_attributes: bool = False,  # Keep data-* attributes in HTML
+    
+    # Markdown Options
+    include_links_on_markdown: bool = False,  # Include links in markdown output
+    html2text: Dict = {},  # HTML to text conversion options
+    
+    # Extraction Strategy
+    extraction_strategy: ExtractionStrategy = None,  # Strategy for structured data extraction
+    
+    # Advanced Browser Control
+    user_agent: str = None,  # Override user agent for this request
+)
+```
+
+### Extraction Strategy Parameters
+```python
+# JsonCssExtractionStrategy
+{
+    "name": str,  # Name of extraction schema
+    "baseSelector": str,  # Base CSS selector
+    "fields": [
+        {
+            "name": str,  # Field name
+            "selector": str,  # CSS selector
+            "type": str,  # Data type ("text", etc.)
+            "transform": str = None  # Optional transformation
+        }
+    ]
+}
+
+# LLMExtractionStrategy
+{
+    "provider": str,  # LLM provider (e.g., "openai/gpt-4", "huggingface/...", "ollama/...")
+    "api_token": str,  # API token
+    "schema": dict,  # Pydantic model schema
+    "extraction_type": str,  # Type of extraction ("schema", etc.)
+    "instruction": str,  # Extraction instruction
+    "extra_args": dict = None,  # Additional provider-specific arguments
+    "extra_headers": dict = None  # Additional HTTP headers
+}
+```
+
+### HTML to Text Conversion Options (html2text parameter)
+```python
+{
+    "escape_dot": bool = True,  # Escape dots in text
+    # Other html2text library options
+}
+```
+
+
+### CrawlResult Fields
+
+```python
+class CrawlResult(BaseModel):
+    # Basic Information
+    url: str  # The crawled URL
+    # Example: "https://example.com"
+    
+    success: bool  # Whether the crawl was successful
+    # Example: True/False
+    
+    status_code: Optional[int]  # HTTP status code
+    # Example: 200, 404, 500
+    
+    # Content Fields
+    html: str  # Raw HTML content
+    # Example: "<html><body>...</body></html>"
+    
+    cleaned_html: Optional[str]  # HTML after cleaning and processing
+    # Example: "<article><p>Clean content...</p></article>"
+    
+    fit_html: Optional[str]  # Most relevant HTML content after content cleaning strategy
+    # Example: "<div><p>Most relevant content...</p></div>"
+    
+    markdown: Optional[str]  # HTML converted to markdown
+    # Example: "# Title\n\nContent paragraph..."
+    
+    fit_markdown: Optional[str]  # Most relevant content in markdown
+    # Example: "# Main Article\n\nKey content..."
+    
+    # Media Content
+    media: Dict[str, List[Dict]] = {}  # Extracted media information
+    # Example: {
+    #     "images": [
+    #         {
+    #             "src": "https://example.com/image.jpg",
+    #             "alt": "Image description",
+    #             "desc": "Contextual description",
+    #             "score": 5,  # Relevance score
+    #             "type": "image"
+    #         }
+    #     ],
+    #     "videos": [
+    #         {
+    #             "src": "https://example.com/video.mp4",
+    #             "alt": "Video title",
+    #             "type": "video",
+    #             "description": "Video context"
+    #         }
+    #     ],
+    #     "audios": [
+    #         {
+    #             "src": "https://example.com/audio.mp3",
+    #             "alt": "Audio title",
+    #             "type": "audio",
+    #             "description": "Audio context"
+    #         }
+    #     ]
+    # }
+    
+    # Link Information
+    links: Dict[str, List[Dict]] = {}  # Extracted links
+    # Example: {
+    #     "internal": [
+    #         {
+    #             "href": "https://example.com/page",
+    #             "text": "Link text",
+    #             "title": "Link title"
+    #         }
+    #     ],
+    #     "external": [
+    #         {
+    #             "href": "https://external.com",
+    #             "text": "External link text",
+    #             "title": "External link title"
+    #         }
+    #     ]
+    # }
+    
+    # Extraction Results
+    extracted_content: Optional[str]  # Content from extraction strategy
+    # Example for JsonCssExtractionStrategy:
+    # '[{"title": "Article 1", "date": "2024-03-20"}, ...]'
+    # Example for LLMExtractionStrategy:
+    # '{"entities": [...], "relationships": [...]}'
+    
+    # Additional Information
+    metadata: Optional[dict] = None  # Page metadata
+    # Example: {
+    #     "title": "Page Title",
+    #     "description": "Meta description",
+    #     "keywords": ["keyword1", "keyword2"],
+    #     "author": "Author Name",
+    #     "published_date": "2024-03-20"
+    # }
+    
+    screenshot: Optional[str] = None  # Base64 encoded screenshot
+    # Example: "iVBORw0KGgoAAAANSUhEUgAA..."
+    
+    error_message: Optional[str] = None  # Error message if crawl failed
+    # Example: "Failed to load page: timeout"
+    
+    session_id: Optional[str] = None  # Session identifier
+    # Example: "session_123456"
+    
+    response_headers: Optional[dict] = None  # HTTP response headers
+    # Example: {
+    #     "content-type": "text/html",
+    #     "server": "nginx/1.18.0",
+    #     "date": "Wed, 20 Mar 2024 12:00:00 GMT"
+    # }
+```
+
+### Common Usage Patterns:
+
+1. Basic Content Extraction:
+```python
+result = await crawler.arun(url="https://example.com")
+print(result.markdown)  # Clean, readable content
+print(result.cleaned_html)  # Cleaned HTML
+```
+
+2. Media Analysis:
+```python
+result = await crawler.arun(url="https://example.com")
+for image in result.media["images"]:
+    if image["score"] > 3:  # High-relevance images
+        print(f"High-quality image: {image['src']}")
+```
+
+3. Link Analysis:
+```python
+result = await crawler.arun(url="https://example.com")
+internal_links = [link["href"] for link in result.links["internal"]]
+external_links = [link["href"] for link in result.links["external"]]
+```
+
+4. Structured Data Extraction:
+```python
+result = await crawler.arun(
+    url="https://example.com",
+    extraction_strategy=my_strategy
+)
+structured_data = json.loads(result.extracted_content)
+```
+
+5. Error Handling:
+```python
+result = await crawler.arun(url="https://example.com")
+if not result.success:
+    print(f"Crawl failed: {result.error_message}")
+    print(f"Status code: {result.status_code}")
+```
+

docs/details/realworld_examples.md

@@ -0,0 +1,67 @@
+1. **E-commerce Product Monitor**
+   - Scraping product details from multiple e-commerce sites
+   - Price tracking with structured data extraction
+   - Handling dynamic content and anti-bot measures
+   - Features: JsonCssExtraction, session management, anti-bot
+
+2. **News Aggregator & Summarizer**
+   - Crawling news websites
+   - Content extraction and summarization
+   - Topic classification
+   - Features: LLMExtraction, CosineStrategy, content cleaning
+
+3. **Academic Paper Research Assistant**
+   - Crawling research papers from academic sites
+   - Extracting citations and references
+   - Building knowledge graphs
+   - Features: structured extraction, link analysis, chunking
+
+4. **Social Media Content Analyzer**
+   - Handling JavaScript-heavy sites
+   - Dynamic content loading
+   - Sentiment analysis integration
+   - Features: dynamic content handling, session management
+
+5. **Real Estate Market Analyzer**
+   - Scraping property listings
+   - Processing image galleries
+   - Geolocation data extraction
+   - Features: media handling, structured data extraction
+
+6. **Documentation Site Generator**
+   - Recursive website crawling
+   - Markdown generation
+   - Link validation
+   - Features: website crawling, content cleaning
+
+7. **Job Board Aggregator**
+   - Handling pagination
+   - Structured job data extraction
+   - Filtering and categorization
+   - Features: session management, JsonCssExtraction
+
+8. **Recipe Database Builder**
+   - Schema-based extraction
+   - Image processing
+   - Ingredient parsing
+   - Features: structured extraction, media handling
+
+9. **Travel Blog Content Analyzer**
+   - Location extraction
+   - Image and map processing
+   - Content categorization
+   - Features: CosineStrategy, media handling
+
+10. **Technical Documentation Scraper**
+    - API documentation extraction
+    - Code snippet processing
+    - Version tracking
+    - Features: content cleaning, structured extraction
+
+Each example will include:
+- Problem description
+- Technical requirements
+- Complete implementation
+- Error handling
+- Output processing
+- Performance considerations

docs/examples/async_webcrawler_multiple_urls_example.py

@@ -0,0 +1,48 @@
+# File: async_webcrawler_multiple_urls_example.py
+import os, sys
+# append 2 parent directories to sys.path to import crawl4ai
+parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(parent_dir)
+
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    # Initialize the AsyncWebCrawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # List of URLs to crawl
+        urls = [
+            "https://example.com",
+            "https://python.org",
+            "https://github.com",
+            "https://stackoverflow.com",
+            "https://news.ycombinator.com"
+        ]
+
+        # Set up crawling parameters
+        word_count_threshold = 100
+
+        # Run the crawling process for multiple URLs
+        results = await crawler.arun_many(
+            urls=urls,
+            word_count_threshold=word_count_threshold,
+            bypass_cache=True,
+            verbose=True
+        )
+
+        # Process the results
+        for result in results:
+            if result.success:
+                print(f"Successfully crawled: {result.url}")
+                print(f"Title: {result.metadata.get('title', 'N/A')}")
+                print(f"Word count: {len(result.markdown.split())}")
+                print(f"Number of links: {len(result.links.get('internal', [])) + len(result.links.get('external', []))}")
+                print(f"Number of images: {len(result.media.get('images', []))}")
+                print("---")
+            else:
+                print(f"Failed to crawl: {result.url}")
+                print(f"Error: {result.error_message}")
+                print("---")
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/crawlai_vs_firecrawl.py

@@ -0,0 +1,67 @@
+import os, time
+# append the path to the root of the project
+import sys
+import asyncio
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
+from firecrawl import FirecrawlApp
+from crawl4ai import AsyncWebCrawler
+__data__ = os.path.join(os.path.dirname(__file__), '..', '..') + '/.data'
+
+async def compare():
+    app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])
+
+    # Tet Firecrawl with a simple crawl
+    start = time.time()
+    scrape_status = app.scrape_url(
+    'https://www.nbcnews.com/business',
+    params={'formats': ['markdown', 'html']}
+    )
+    end = time.time()
+    print(f"Time taken: {end - start} seconds")
+    print(len(scrape_status['markdown']))
+    # save the markdown content with provider name
+    with open(f"{__data__}/firecrawl_simple.md", "w") as f:
+        f.write(scrape_status['markdown'])
+    # Count how many "cldnry.s-nbcnews.com" are in the markdown
+    print(scrape_status['markdown'].count("cldnry.s-nbcnews.com"))
+    
+
+
+    async with AsyncWebCrawler() as crawler:
+        start = time.time()
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            # js_code=["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"],
+            word_count_threshold=0,
+            bypass_cache=True, 
+            verbose=False
+        )
+        end = time.time()
+        print(f"Time taken: {end - start} seconds")
+        print(len(result.markdown))
+        # save the markdown content with provider name  
+        with open(f"{__data__}/crawl4ai_simple.md", "w") as f:
+            f.write(result.markdown)
+        # count how many "cldnry.s-nbcnews.com" are in the markdown
+        print(result.markdown.count("cldnry.s-nbcnews.com"))
+
+        start = time.time()
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"],
+            word_count_threshold=0,
+            bypass_cache=True, 
+            verbose=False
+        )
+        end = time.time()
+        print(f"Time taken: {end - start} seconds")
+        print(len(result.markdown))
+        # save the markdown content with provider name
+        with open(f"{__data__}/crawl4ai_js.md", "w") as f:
+            f.write(result.markdown)
+        # count how many "cldnry.s-nbcnews.com" are in the markdown
+        print(result.markdown.count("cldnry.s-nbcnews.com"))
+        
+if __name__ == "__main__":
+    asyncio.run(compare())
+    

docs/examples/language_support_example.py

@@ -0,0 +1,45 @@
+import asyncio
+from crawl4ai import AsyncWebCrawler, AsyncPlaywrightCrawlerStrategy
+
+async def main():
+    # Example 1: Setting language when creating the crawler
+    crawler1 = AsyncWebCrawler(
+        crawler_strategy=AsyncPlaywrightCrawlerStrategy(
+            headers={"Accept-Language": "fr-FR,fr;q=0.9,en-US;q=0.8,en;q=0.7"}
+        )
+    )
+    result1 = await crawler1.arun("https://www.example.com")
+    print("Example 1 result:", result1.extracted_content[:100])  # Print first 100 characters
+
+    # Example 2: Setting language before crawling
+    crawler2 = AsyncWebCrawler()
+    crawler2.crawler_strategy.headers["Accept-Language"] = "es-ES,es;q=0.9,en-US;q=0.8,en;q=0.7"
+    result2 = await crawler2.arun("https://www.example.com")
+    print("Example 2 result:", result2.extracted_content[:100])
+
+    # Example 3: Setting language when calling arun method
+    crawler3 = AsyncWebCrawler()
+    result3 = await crawler3.arun(
+        "https://www.example.com",
+        headers={"Accept-Language": "de-DE,de;q=0.9,en-US;q=0.8,en;q=0.7"}
+    )
+    print("Example 3 result:", result3.extracted_content[:100])
+
+    # Example 4: Crawling multiple pages with different languages
+    urls = [
+        ("https://www.example.com", "fr-FR,fr;q=0.9"),
+        ("https://www.example.org", "es-ES,es;q=0.9"),
+        ("https://www.example.net", "de-DE,de;q=0.9"),
+    ]
+    
+    crawler4 = AsyncWebCrawler()
+    results = await asyncio.gather(*[
+        crawler4.arun(url, headers={"Accept-Language": lang})
+        for url, lang in urls
+    ])
+    
+    for url, result in zip([u for u, _ in urls], results):
+        print(f"Result for {url}:", result.extracted_content[:100])
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/quickstart.ipynb

@@ -0,0 +1,735 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6yLvrXn7yZQI"
+      },
+      "source": [
+        "# Crawl4AI: Advanced Web Crawling and Data Extraction\n",
+        "\n",
+        "Welcome to this interactive notebook showcasing Crawl4AI, an advanced asynchronous web crawling and data extraction library.\n",
+        "\n",
+        "- GitHub Repository: [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)\n",
+        "- Twitter: [@unclecode](https://twitter.com/unclecode)\n",
+        "- Website: [https://crawl4ai.com](https://crawl4ai.com)\n",
+        "\n",
+        "Let's explore the powerful features of Crawl4AI!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KIn_9nxFyZQK"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "First, let's install Crawl4AI from GitHub:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mSnaxLf3zMog"
+      },
+      "outputs": [],
+      "source": [
+        "!sudo apt-get update && sudo apt-get install -y libwoff1 libopus0 libwebp6 libwebpdemux2 libenchant1c2a libgudev-1.0-0 libsecret-1-0 libhyphen0 libgdk-pixbuf2.0-0 libegl1 libnotify4 libxslt1.1 libevent-2.1-7 libgles2 libvpx6 libxcomposite1 libatk1.0-0 libatk-bridge2.0-0 libepoxy0 libgtk-3-0 libharfbuzz-icu0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xlXqaRtayZQK"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install crawl4ai\n",
+        "!pip install nest-asyncio\n",
+        "!playwright install"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qKCE7TI7yZQL"
+      },
+      "source": [
+        "Now, let's import the necessary libraries:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "I67tr7aAyZQL"
+      },
+      "outputs": [],
+      "source": [
+        "import asyncio\n",
+        "import nest_asyncio\n",
+        "from crawl4ai import AsyncWebCrawler\n",
+        "from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy\n",
+        "import json\n",
+        "import time\n",
+        "from pydantic import BaseModel, Field\n",
+        "\n",
+        "nest_asyncio.apply()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h7yR_Rt_yZQM"
+      },
+      "source": [
+        "## Basic Usage\n",
+        "\n",
+        "Let's start with a simple crawl example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yBh6hf4WyZQM",
+        "outputId": "0f83af5c-abba-4175-ed95-70b7512e6bcc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LOG] 🌤️  Warming up the AsyncWebCrawler\n",
+            "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+            "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.05 seconds\n",
+            "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.05 seconds.\n",
+            "18102\n"
+          ]
+        }
+      ],
+      "source": [
+        "async def simple_crawl():\n",
+        "    async with AsyncWebCrawler(verbose=True) as crawler:\n",
+        "        result = await crawler.arun(url=\"https://www.nbcnews.com/business\")\n",
+        "        print(len(result.markdown))\n",
+        "await simple_crawl()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9rtkgHI28uI4"
+      },
+      "source": [
+        "💡 By default, **Crawl4AI** caches the result of every URL, so the next time you call it, you’ll get an instant result. But if you want to bypass the cache, just set `bypass_cache=True`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MzZ0zlJ9yZQM"
+      },
+      "source": [
+        "## Advanced Features\n",
+        "\n",
+        "### Executing JavaScript and Using CSS Selectors"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gHStF86xyZQM",
+        "outputId": "34d0fb6d-4dec-4677-f76e-85a1f082829b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LOG] 🌤️  Warming up the AsyncWebCrawler\n",
+            "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+            "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
+            "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 6.06 seconds\n",
+            "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.10 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+            "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.11 seconds.\n",
+            "41135\n"
+          ]
+        }
+      ],
+      "source": [
+        "async def js_and_css():\n",
+        "    async with AsyncWebCrawler(verbose=True) as crawler:\n",
+        "        js_code = [\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"]\n",
+        "        result = await crawler.arun(\n",
+        "            url=\"https://www.nbcnews.com/business\",\n",
+        "            js_code=js_code,\n",
+        "            # css_selector=\"YOUR_CSS_SELECTOR_HERE\",\n",
+        "            bypass_cache=True\n",
+        "        )\n",
+        "        print(len(result.markdown))\n",
+        "\n",
+        "await js_and_css()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cqE_W4coyZQM"
+      },
+      "source": [
+        "### Using a Proxy\n",
+        "\n",
+        "Note: You'll need to replace the proxy URL with a working proxy for this example to run successfully."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QjAyiAGqyZQM"
+      },
+      "outputs": [],
+      "source": [
+        "async def use_proxy():\n",
+        "    async with AsyncWebCrawler(verbose=True, proxy=\"http://your-proxy-url:port\") as crawler:\n",
+        "        result = await crawler.arun(\n",
+        "            url=\"https://www.nbcnews.com/business\",\n",
+        "            bypass_cache=True\n",
+        "        )\n",
+        "        print(result.markdown[:500])  # Print first 500 characters\n",
+        "\n",
+        "# Uncomment the following line to run the proxy example\n",
+        "# await use_proxy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XTZ88lbayZQN"
+      },
+      "source": [
+        "### Extracting Structured Data with OpenAI\n",
+        "\n",
+        "Note: You'll need to set your OpenAI API key as an environment variable for this example to work."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fIOlDayYyZQN",
+        "outputId": "cb8359cc-dee0-4762-9698-5dfdcee055b8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LOG] 🌤️  Warming up the AsyncWebCrawler\n",
+            "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+            "[LOG] 🕸️ Crawling https://openai.com/api/pricing/ using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://openai.com/api/pricing/ successfully!\n",
+            "[LOG] 🚀 Crawling done for https://openai.com/api/pricing/, success: True, time taken: 3.77 seconds\n",
+            "[LOG] 🚀 Content extracted for https://openai.com/api/pricing/, success: True, time taken: 0.21 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://openai.com/api/pricing/, Strategy: AsyncWebCrawler\n",
+            "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 0\n",
+            "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 1\n",
+            "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 2\n",
+            "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 3\n",
+            "[LOG] Extracted 4 blocks from URL: https://openai.com/api/pricing/ block index: 3\n",
+            "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 4\n",
+            "[LOG] Extracted 5 blocks from URL: https://openai.com/api/pricing/ block index: 0\n",
+            "[LOG] Extracted 1 blocks from URL: https://openai.com/api/pricing/ block index: 4\n",
+            "[LOG] Extracted 8 blocks from URL: https://openai.com/api/pricing/ block index: 1\n",
+            "[LOG] Extracted 12 blocks from URL: https://openai.com/api/pricing/ block index: 2\n",
+            "[LOG] 🚀 Extraction done for https://openai.com/api/pricing/, time taken: 8.55 seconds.\n",
+            "5029\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
+        "\n",
+        "class OpenAIModelFee(BaseModel):\n",
+        "    model_name: str = Field(..., description=\"Name of the OpenAI model.\")\n",
+        "    input_fee: str = Field(..., description=\"Fee for input token for the OpenAI model.\")\n",
+        "    output_fee: str = Field(..., description=\"Fee for output token for the OpenAI model.\")\n",
+        "\n",
+        "async def extract_openai_fees():\n",
+        "    async with AsyncWebCrawler(verbose=True) as crawler:\n",
+        "        result = await crawler.arun(\n",
+        "            url='https://openai.com/api/pricing/',\n",
+        "            word_count_threshold=1,\n",
+        "            extraction_strategy=LLMExtractionStrategy(\n",
+        "                provider=\"openai/gpt-4o\", api_token=os.getenv('OPENAI_API_KEY'),\n",
+        "                schema=OpenAIModelFee.schema(),\n",
+        "                extraction_type=\"schema\",\n",
+        "                instruction=\"\"\"From the crawled content, extract all mentioned model names along with their fees for input and output tokens.\n",
+        "                Do not miss any models in the entire content. One extracted model JSON format should look like this:\n",
+        "                {\"model_name\": \"GPT-4\", \"input_fee\": \"US$10.00 / 1M tokens\", \"output_fee\": \"US$30.00 / 1M tokens\"}.\"\"\"\n",
+        "            ),\n",
+        "            bypass_cache=True,\n",
+        "        )\n",
+        "        print(len(result.extracted_content))\n",
+        "\n",
+        "# Uncomment the following line to run the OpenAI extraction example\n",
+        "await extract_openai_fees()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BypA5YxEyZQN"
+      },
+      "source": [
+        "### Advanced Multi-Page Crawling with JavaScript Execution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tfkcVQ0b7mw-"
+      },
+      "source": [
+        "## Advanced Multi-Page Crawling with JavaScript Execution\n",
+        "\n",
+        "This example demonstrates Crawl4AI's ability to handle complex crawling scenarios, specifically extracting commits from multiple pages of a GitHub repository. The challenge here is that clicking the \"Next\" button doesn't load a new page, but instead uses asynchronous JavaScript to update the content. This is a common hurdle in modern web crawling.\n",
+        "\n",
+        "To overcome this, we use Crawl4AI's custom JavaScript execution to simulate clicking the \"Next\" button, and implement a custom hook to detect when new data has loaded. Our strategy involves comparing the first commit's text before and after \"clicking\" Next, waiting until it changes to confirm new data has rendered. This showcases Crawl4AI's flexibility in handling dynamic content and its ability to implement custom logic for even the most challenging crawling tasks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qUBKGpn3yZQN",
+        "outputId": "3e555b6a-ed33-42f4-cce9-499a923fbe17"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LOG] 🌤️  Warming up the AsyncWebCrawler\n",
+            "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+            "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+            "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 5.16 seconds\n",
+            "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.28 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+            "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.28 seconds.\n",
+            "Page 1: Found 35 commits\n",
+            "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+            "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.78 seconds\n",
+            "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.90 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+            "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.90 seconds.\n",
+            "Page 2: Found 35 commits\n",
+            "[LOG] 🕸️ Crawling https://github.com/microsoft/TypeScript/commits/main using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://github.com/microsoft/TypeScript/commits/main successfully!\n",
+            "[LOG] 🚀 Crawling done for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 2.00 seconds\n",
+            "[LOG] 🚀 Content extracted for https://github.com/microsoft/TypeScript/commits/main, success: True, time taken: 0.74 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://github.com/microsoft/TypeScript/commits/main, Strategy: AsyncWebCrawler\n",
+            "[LOG] 🚀 Extraction done for https://github.com/microsoft/TypeScript/commits/main, time taken: 0.75 seconds.\n",
+            "Page 3: Found 35 commits\n",
+            "Successfully crawled 105 commits across 3 pages\n"
+          ]
+        }
+      ],
+      "source": [
+        "import re\n",
+        "from bs4 import BeautifulSoup\n",
+        "\n",
+        "async def crawl_typescript_commits():\n",
+        "    first_commit = \"\"\n",
+        "    async def on_execution_started(page):\n",
+        "        nonlocal first_commit\n",
+        "        try:\n",
+        "            while True:\n",
+        "                await page.wait_for_selector('li.Box-sc-g0xbh4-0 h4')\n",
+        "                commit = await page.query_selector('li.Box-sc-g0xbh4-0 h4')\n",
+        "                commit = await commit.evaluate('(element) => element.textContent')\n",
+        "                commit = re.sub(r'\\s+', '', commit)\n",
+        "                if commit and commit != first_commit:\n",
+        "                    first_commit = commit\n",
+        "                    break\n",
+        "                await asyncio.sleep(0.5)\n",
+        "        except Exception as e:\n",
+        "            print(f\"Warning: New content didn't appear after JavaScript execution: {e}\")\n",
+        "\n",
+        "    async with AsyncWebCrawler(verbose=True) as crawler:\n",
+        "        crawler.crawler_strategy.set_hook('on_execution_started', on_execution_started)\n",
+        "\n",
+        "        url = \"https://github.com/microsoft/TypeScript/commits/main\"\n",
+        "        session_id = \"typescript_commits_session\"\n",
+        "        all_commits = []\n",
+        "\n",
+        "        js_next_page = \"\"\"\n",
+        "        const button = document.querySelector('a[data-testid=\"pagination-next-button\"]');\n",
+        "        if (button) button.click();\n",
+        "        \"\"\"\n",
+        "\n",
+        "        for page in range(3):  # Crawl 3 pages\n",
+        "            result = await crawler.arun(\n",
+        "                url=url,\n",
+        "                session_id=session_id,\n",
+        "                css_selector=\"li.Box-sc-g0xbh4-0\",\n",
+        "                js=js_next_page if page > 0 else None,\n",
+        "                bypass_cache=True,\n",
+        "                js_only=page > 0\n",
+        "            )\n",
+        "\n",
+        "            assert result.success, f\"Failed to crawl page {page + 1}\"\n",
+        "\n",
+        "            soup = BeautifulSoup(result.cleaned_html, 'html.parser')\n",
+        "            commits = soup.select(\"li\")\n",
+        "            all_commits.extend(commits)\n",
+        "\n",
+        "            print(f\"Page {page + 1}: Found {len(commits)} commits\")\n",
+        "\n",
+        "        await crawler.crawler_strategy.kill_session(session_id)\n",
+        "        print(f\"Successfully crawled {len(all_commits)} commits across 3 pages\")\n",
+        "\n",
+        "await crawl_typescript_commits()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EJRnYsp6yZQN"
+      },
+      "source": [
+        "### Using JsonCssExtractionStrategy for Fast Structured Output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ZMqIzB_8SYp"
+      },
+      "source": [
+        "The JsonCssExtractionStrategy is a powerful feature of Crawl4AI that allows for precise, structured data extraction from web pages. Here's how it works:\n",
+        "\n",
+        "1. You define a schema that describes the pattern of data you're interested in extracting.\n",
+        "2. The schema includes a base selector that identifies repeating elements on the page.\n",
+        "3. Within the schema, you define fields, each with its own selector and type.\n",
+        "4. These field selectors are applied within the context of each base selector element.\n",
+        "5. The strategy supports nested structures, lists within lists, and various data types.\n",
+        "6. You can even include computed fields for more complex data manipulation.\n",
+        "\n",
+        "This approach allows for highly flexible and precise data extraction, transforming semi-structured web content into clean, structured JSON data. It's particularly useful for extracting consistent data patterns from pages like product listings, news articles, or search results.\n",
+        "\n",
+        "For more details and advanced usage, check out the full documentation on the Crawl4AI website."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "trCMR2T9yZQN",
+        "outputId": "718d36f4-cccf-40f4-8d8c-c3ba73524d16"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[LOG] 🌤️  Warming up the AsyncWebCrawler\n",
+            "[LOG] 🌞 AsyncWebCrawler is ready to crawl\n",
+            "[LOG] 🕸️ Crawling https://www.nbcnews.com/business using AsyncPlaywrightCrawlerStrategy...\n",
+            "[LOG] ✅ Crawled https://www.nbcnews.com/business successfully!\n",
+            "[LOG] 🚀 Crawling done for https://www.nbcnews.com/business, success: True, time taken: 7.00 seconds\n",
+            "[LOG] 🚀 Content extracted for https://www.nbcnews.com/business, success: True, time taken: 0.32 seconds\n",
+            "[LOG] 🔥 Extracting semantic blocks for https://www.nbcnews.com/business, Strategy: AsyncWebCrawler\n",
+            "[LOG] 🚀 Extraction done for https://www.nbcnews.com/business, time taken: 0.48 seconds.\n",
+            "Successfully extracted 11 news teasers\n",
+            "{\n",
+            "  \"category\": \"Business News\",\n",
+            "  \"headline\": \"NBC ripped up its Olympics playbook for 2024 \\u2014 so far, the new strategy paid off\",\n",
+            "  \"summary\": \"The Olympics have long been key to NBCUniversal. Paris marked the 18th Olympic Games broadcast by NBC in the U.S.\",\n",
+            "  \"time\": \"13h ago\",\n",
+            "  \"image\": {\n",
+            "    \"src\": \"https://media-cldnry.s-nbcnews.com/image/upload/t_focal-200x100,f_auto,q_auto:best/rockcms/2024-09/240903-nbc-olympics-ch-1344-c7a486.jpg\",\n",
+            "    \"alt\": \"Mike Tirico.\"\n",
+            "  },\n",
+            "  \"link\": \"https://www.nbcnews.com/business\"\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "async def extract_news_teasers():\n",
+        "    schema = {\n",
+        "        \"name\": \"News Teaser Extractor\",\n",
+        "        \"baseSelector\": \".wide-tease-item__wrapper\",\n",
+        "        \"fields\": [\n",
+        "            {\n",
+        "                \"name\": \"category\",\n",
+        "                \"selector\": \".unibrow span[data-testid='unibrow-text']\",\n",
+        "                \"type\": \"text\",\n",
+        "            },\n",
+        "            {\n",
+        "                \"name\": \"headline\",\n",
+        "                \"selector\": \".wide-tease-item__headline\",\n",
+        "                \"type\": \"text\",\n",
+        "            },\n",
+        "            {\n",
+        "                \"name\": \"summary\",\n",
+        "                \"selector\": \".wide-tease-item__description\",\n",
+        "                \"type\": \"text\",\n",
+        "            },\n",
+        "            {\n",
+        "                \"name\": \"time\",\n",
+        "                \"selector\": \"[data-testid='wide-tease-date']\",\n",
+        "                \"type\": \"text\",\n",
+        "            },\n",
+        "            {\n",
+        "                \"name\": \"image\",\n",
+        "                \"type\": \"nested\",\n",
+        "                \"selector\": \"picture.teasePicture img\",\n",
+        "                \"fields\": [\n",
+        "                    {\"name\": \"src\", \"type\": \"attribute\", \"attribute\": \"src\"},\n",
+        "                    {\"name\": \"alt\", \"type\": \"attribute\", \"attribute\": \"alt\"},\n",
+        "                ],\n",
+        "            },\n",
+        "            {\n",
+        "                \"name\": \"link\",\n",
+        "                \"selector\": \"a[href]\",\n",
+        "                \"type\": \"attribute\",\n",
+        "                \"attribute\": \"href\",\n",
+        "            },\n",
+        "        ],\n",
+        "    }\n",
+        "\n",
+        "    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)\n",
+        "\n",
+        "    async with AsyncWebCrawler(verbose=True) as crawler:\n",
+        "        result = await crawler.arun(\n",
+        "            url=\"https://www.nbcnews.com/business\",\n",
+        "            extraction_strategy=extraction_strategy,\n",
+        "            bypass_cache=True,\n",
+        "        )\n",
+        "\n",
+        "        assert result.success, \"Failed to crawl the page\"\n",
+        "\n",
+        "        news_teasers = json.loads(result.extracted_content)\n",
+        "        print(f\"Successfully extracted {len(news_teasers)} news teasers\")\n",
+        "        print(json.dumps(news_teasers[0], indent=2))\n",
+        "\n",
+        "await extract_news_teasers()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FnyVhJaByZQN"
+      },
+      "source": [
+        "## Speed Comparison\n",
+        "\n",
+        "Let's compare the speed of Crawl4AI with Firecrawl, a paid service. Note that we can't run Firecrawl in this Colab environment, so we'll simulate its performance based on previously recorded data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "agDD186f3wig"
+      },
+      "source": [
+        "💡 **Note on Speed Comparison:**\n",
+        "\n",
+        "The speed test conducted here is running on Google Colab, where the internet speed and performance can vary and may not reflect optimal conditions. When we call Firecrawl's API, we're seeing its best performance, while Crawl4AI's performance is limited by Colab's network speed.\n",
+        "\n",
+        "For a more accurate comparison, it's recommended to run these tests on your own servers or computers with a stable and fast internet connection. Despite these limitations, Crawl4AI still demonstrates faster performance in this environment.\n",
+        "\n",
+        "If you run these tests locally, you may observe an even more significant speed advantage for Crawl4AI compared to other services."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F7KwHv8G1LbY"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install firecrawl"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "91813zILyZQN",
+        "outputId": "663223db-ab89-4976-b233-05ceca62b19b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Firecrawl (simulated):\n",
+            "Time taken: 4.38 seconds\n",
+            "Content length: 41967 characters\n",
+            "Images found: 49\n",
+            "\n",
+            "Crawl4AI (simple crawl):\n",
+            "Time taken: 4.22 seconds\n",
+            "Content length: 18221 characters\n",
+            "Images found: 49\n",
+            "\n",
+            "Crawl4AI (with JavaScript execution):\n",
+            "Time taken: 9.13 seconds\n",
+            "Content length: 34243 characters\n",
+            "Images found: 89\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "os.environ['FIRECRAWL_API_KEY'] = userdata.get('FIRECRAWL_API_KEY')\n",
+        "import time\n",
+        "from firecrawl import FirecrawlApp\n",
+        "\n",
+        "async def speed_comparison():\n",
+        "    # Simulated Firecrawl performance\n",
+        "    app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])\n",
+        "    start = time.time()\n",
+        "    scrape_status = app.scrape_url(\n",
+        "    'https://www.nbcnews.com/business',\n",
+        "    params={'formats': ['markdown', 'html']}\n",
+        "    )\n",
+        "    end = time.time()\n",
+        "    print(\"Firecrawl (simulated):\")\n",
+        "    print(f\"Time taken: {end - start:.2f} seconds\")\n",
+        "    print(f\"Content length: {len(scrape_status['markdown'])} characters\")\n",
+        "    print(f\"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}\")\n",
+        "    print()\n",
+        "\n",
+        "    async with AsyncWebCrawler() as crawler:\n",
+        "        # Crawl4AI simple crawl\n",
+        "        start = time.time()\n",
+        "        result = await crawler.arun(\n",
+        "            url=\"https://www.nbcnews.com/business\",\n",
+        "            word_count_threshold=0,\n",
+        "            bypass_cache=True,\n",
+        "            verbose=False\n",
+        "        )\n",
+        "        end = time.time()\n",
+        "        print(\"Crawl4AI (simple crawl):\")\n",
+        "        print(f\"Time taken: {end - start:.2f} seconds\")\n",
+        "        print(f\"Content length: {len(result.markdown)} characters\")\n",
+        "        print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
+        "        print()\n",
+        "\n",
+        "        # Crawl4AI with JavaScript execution\n",
+        "        start = time.time()\n",
+        "        result = await crawler.arun(\n",
+        "            url=\"https://www.nbcnews.com/business\",\n",
+        "            js_code=[\"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();\"],\n",
+        "            word_count_threshold=0,\n",
+        "            bypass_cache=True,\n",
+        "            verbose=False\n",
+        "        )\n",
+        "        end = time.time()\n",
+        "        print(\"Crawl4AI (with JavaScript execution):\")\n",
+        "        print(f\"Time taken: {end - start:.2f} seconds\")\n",
+        "        print(f\"Content length: {len(result.markdown)} characters\")\n",
+        "        print(f\"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}\")\n",
+        "\n",
+        "await speed_comparison()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OBFFYVJIyZQN"
+      },
+      "source": [
+        "If you run on a local machine with a proper internet speed:\n",
+        "- Simple crawl: Crawl4AI is typically over 3-4 times faster than Firecrawl.\n",
+        "- With JavaScript execution: Even when executing JavaScript to load more content (potentially doubling the number of images found), Crawl4AI is still faster than Firecrawl's simple crawl.\n",
+        "\n",
+        "Please note that actual performance may vary depending on network conditions and the specific content being crawled."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A6_1RK1_yZQO"
+      },
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "In this notebook, we've explored the powerful features of Crawl4AI, including:\n",
+        "\n",
+        "1. Basic crawling\n",
+        "2. JavaScript execution and CSS selector usage\n",
+        "3. Proxy support\n",
+        "4. Structured data extraction with OpenAI\n",
+        "5. Advanced multi-page crawling with JavaScript execution\n",
+        "6. Fast structured output using JsonCssExtractionStrategy\n",
+        "7. Speed comparison with other services\n",
+        "\n",
+        "Crawl4AI offers a fast, flexible, and powerful solution for web crawling and data extraction tasks. Its asynchronous architecture and advanced features make it suitable for a wide range of applications, from simple web scraping to complex, multi-page data extraction scenarios.\n",
+        "\n",
+        "For more information and advanced usage, please visit the [Crawl4AI documentation](https://crawl4ai.com/mkdocs/).\n",
+        "\n",
+        "Happy crawling!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/examples/quickstart_async.py

@@ -0,0 +1,541 @@
+import os, sys
+# append parent directory to system path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))); os.environ['FIRECRAWL_API_KEY'] = "fc-84b370ccfad44beabc686b38f1769692";
+
+import asyncio
+# import nest_asyncio
+# nest_asyncio.apply()
+
+import time
+import json
+import os
+import re
+from typing import Dict, List
+from bs4 import BeautifulSoup
+from pydantic import BaseModel, Field
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import (
+    JsonCssExtractionStrategy,
+    LLMExtractionStrategy,
+)
+
+__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+
+print("Crawl4AI: Advanced Web Crawling and Data Extraction")
+print("GitHub Repository: https://github.com/unclecode/crawl4ai")
+print("Twitter: @unclecode")
+print("Website: https://crawl4ai.com")
+
+
+async def simple_crawl():
+    print("\n--- Basic Usage ---")
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(url="https://www.nbcnews.com/business")
+        print(result.markdown[:500])  # Print first 500 characters
+
+async def simple_example_with_running_js_code():
+    print("\n--- Executing JavaScript and Using CSS Selectors ---")
+    # New code to handle the wait_for parameter
+    wait_for = """() => {
+        return Array.from(document.querySelectorAll('article.tease-card')).length > 10;
+    }"""
+
+    # wait_for can be also just a css selector
+    # wait_for = "article.tease-card:nth-child(10)"
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        js_code = [
+            "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+        ]
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            # wait_for=wait_for,
+            bypass_cache=True,
+        )
+        print(result.markdown[:500])  # Print first 500 characters
+
+async def simple_example_with_css_selector():
+    print("\n--- Using CSS Selectors ---")
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            css_selector=".wide-tease-item__description",
+            bypass_cache=True,
+        )
+        print(result.markdown[:500])  # Print first 500 characters
+
+async def use_proxy():
+    print("\n--- Using a Proxy ---")
+    print(
+        "Note: Replace 'http://your-proxy-url:port' with a working proxy to run this example."
+    )
+    # Uncomment and modify the following lines to use a proxy
+    # async with AsyncWebCrawler(verbose=True, proxy="http://your-proxy-url:port") as crawler:
+    #     result = await crawler.arun(
+    #         url="https://www.nbcnews.com/business",
+    #         bypass_cache=True
+    #     )
+    #     print(result.markdown[:500])  # Print first 500 characters
+
+async def capture_and_save_screenshot(url: str, output_path: str):
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url=url,
+            screenshot=True,
+            bypass_cache=True
+        )
+        
+        if result.success and result.screenshot:
+            import base64
+            
+            # Decode the base64 screenshot data
+            screenshot_data = base64.b64decode(result.screenshot)
+            
+            # Save the screenshot as a JPEG file
+            with open(output_path, 'wb') as f:
+                f.write(screenshot_data)
+            
+            print(f"Screenshot saved successfully to {output_path}")
+        else:
+            print("Failed to capture screenshot")
+
+class OpenAIModelFee(BaseModel):
+    model_name: str = Field(..., description="Name of the OpenAI model.")
+    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+    output_fee: str = Field(
+        ..., description="Fee for output token for the OpenAI model."
+    )
+
+async def extract_structured_data_using_llm(provider: str, api_token: str = None, extra_headers: Dict[str, str] = None):
+    print(f"\n--- Extracting Structured Data with {provider} ---")
+    
+    if api_token is None and provider != "ollama":
+        print(f"API token is required for {provider}. Skipping this example.")
+        return
+
+    extra_args = {}
+    if extra_headers:
+        extra_args["extra_headers"] = extra_headers
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://openai.com/api/pricing/",
+            word_count_threshold=1,
+            extraction_strategy=LLMExtractionStrategy(
+                provider=provider,
+                api_token=api_token,
+                schema=OpenAIModelFee.schema(),
+                extraction_type="schema",
+                instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
+                Do not miss any models in the entire content. One extracted model JSON format should look like this: 
+                {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
+                extra_args=extra_args
+            ),
+            bypass_cache=True,
+        )
+        print(result.extracted_content)
+
+async def extract_structured_data_using_css_extractor():
+    print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
+    schema = {
+        "name": "Coinbase Crypto Prices",
+        "baseSelector": ".cds-tableRow-t45thuk",
+        "fields": [
+            {
+                "name": "crypto",
+                "selector": "td:nth-child(1) h2",
+                "type": "text",
+            },
+            {
+                "name": "symbol",
+                "selector": "td:nth-child(1) p",
+                "type": "text",
+            },
+            {
+                "name": "price",
+                "selector": "td:nth-child(2)",
+                "type": "text",
+            }
+        ],
+    }
+
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.coinbase.com/explore",
+            extraction_strategy=extraction_strategy,
+            bypass_cache=True,
+        )
+
+        assert result.success, "Failed to crawl the page"
+
+        news_teasers = json.loads(result.extracted_content)
+        print(f"Successfully extracted {len(news_teasers)} news teasers")
+        print(json.dumps(news_teasers[0], indent=2))
+
+# Advanced Session-Based Crawling with Dynamic Content 🔄
+async def crawl_dynamic_content_pages_method_1():
+    print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")
+    first_commit = ""
+
+    async def on_execution_started(page):
+        nonlocal first_commit
+        try:
+            while True:
+                await page.wait_for_selector("li.Box-sc-g0xbh4-0 h4")
+                commit = await page.query_selector("li.Box-sc-g0xbh4-0 h4")
+                commit = await commit.evaluate("(element) => element.textContent")
+                commit = re.sub(r"\s+", "", commit)
+                if commit and commit != first_commit:
+                    first_commit = commit
+                    break
+                await asyncio.sleep(0.5)
+        except Exception as e:
+            print(f"Warning: New content didn't appear after JavaScript execution: {e}")
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        crawler.crawler_strategy.set_hook("on_execution_started", on_execution_started)
+
+        url = "https://github.com/microsoft/TypeScript/commits/main"
+        session_id = "typescript_commits_session"
+        all_commits = []
+
+        js_next_page = """
+        const button = document.querySelector('a[data-testid="pagination-next-button"]');
+        if (button) button.click();
+        """
+
+        for page in range(3):  # Crawl 3 pages
+            result = await crawler.arun(
+                url=url,
+                session_id=session_id,
+                css_selector="li.Box-sc-g0xbh4-0",
+                js=js_next_page if page > 0 else None,
+                bypass_cache=True,
+                js_only=page > 0,
+                headless=False,
+            )
+
+            assert result.success, f"Failed to crawl page {page + 1}"
+
+            soup = BeautifulSoup(result.cleaned_html, "html.parser")
+            commits = soup.select("li")
+            all_commits.extend(commits)
+
+            print(f"Page {page + 1}: Found {len(commits)} commits")
+
+        await crawler.crawler_strategy.kill_session(session_id)
+        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
+
+async def crawl_dynamic_content_pages_method_2():
+    print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        url = "https://github.com/microsoft/TypeScript/commits/main"
+        session_id = "typescript_commits_session"
+        all_commits = []
+        last_commit = ""
+
+        js_next_page_and_wait = """
+        (async () => {
+            const getCurrentCommit = () => {
+                const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
+                return commits.length > 0 ? commits[0].textContent.trim() : null;
+            };
+
+            const initialCommit = getCurrentCommit();
+            const button = document.querySelector('a[data-testid="pagination-next-button"]');
+            if (button) button.click();
+
+            // Poll for changes
+            while (true) {
+                await new Promise(resolve => setTimeout(resolve, 100)); // Wait 100ms
+                const newCommit = getCurrentCommit();
+                if (newCommit && newCommit !== initialCommit) {
+                    break;
+                }
+            }
+        })();
+        """
+
+        schema = {
+            "name": "Commit Extractor",
+            "baseSelector": "li.Box-sc-g0xbh4-0",
+            "fields": [
+                {
+                    "name": "title",
+                    "selector": "h4.markdown-title",
+                    "type": "text",
+                    "transform": "strip",
+                },
+            ],
+        }
+        extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+        for page in range(3):  # Crawl 3 pages
+            result = await crawler.arun(
+                url=url,
+                session_id=session_id,
+                css_selector="li.Box-sc-g0xbh4-0",
+                extraction_strategy=extraction_strategy,
+                js_code=js_next_page_and_wait if page > 0 else None,
+                js_only=page > 0,
+                bypass_cache=True,
+                headless=False,
+            )
+
+            assert result.success, f"Failed to crawl page {page + 1}"
+
+            commits = json.loads(result.extracted_content)
+            all_commits.extend(commits)
+
+            print(f"Page {page + 1}: Found {len(commits)} commits")
+
+        await crawler.crawler_strategy.kill_session(session_id)
+        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
+
+async def crawl_dynamic_content_pages_method_3():
+    print("\n--- Advanced Multi-Page Crawling with JavaScript Execution using `wait_for` ---")
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        url = "https://github.com/microsoft/TypeScript/commits/main"
+        session_id = "typescript_commits_session"
+        all_commits = []
+
+        js_next_page = """
+        const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
+        if (commits.length > 0) {
+            window.firstCommit = commits[0].textContent.trim();
+        }
+        const button = document.querySelector('a[data-testid="pagination-next-button"]');
+        if (button) button.click();
+        """
+
+        wait_for = """() => {
+            const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
+            if (commits.length === 0) return false;
+            const firstCommit = commits[0].textContent.trim();
+            return firstCommit !== window.firstCommit;
+        }"""
+        
+        schema = {
+            "name": "Commit Extractor",
+            "baseSelector": "li.Box-sc-g0xbh4-0",
+            "fields": [
+                {
+                    "name": "title",
+                    "selector": "h4.markdown-title",
+                    "type": "text",
+                    "transform": "strip",
+                },
+            ],
+        }
+        extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+        for page in range(3):  # Crawl 3 pages
+            result = await crawler.arun(
+                url=url,
+                session_id=session_id,
+                css_selector="li.Box-sc-g0xbh4-0",
+                extraction_strategy=extraction_strategy,
+                js_code=js_next_page if page > 0 else None,
+                wait_for=wait_for if page > 0 else None,
+                js_only=page > 0,
+                bypass_cache=True,
+                headless=False,
+            )
+
+            assert result.success, f"Failed to crawl page {page + 1}"
+
+            commits = json.loads(result.extracted_content)
+            all_commits.extend(commits)
+
+            print(f"Page {page + 1}: Found {len(commits)} commits")
+
+        await crawler.crawler_strategy.kill_session(session_id)
+        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
+
+async def crawl_custom_browser_type():
+    # Use Firefox
+    start = time.time()
+    async with AsyncWebCrawler(browser_type="firefox", verbose=True, headless = True) as crawler:
+        result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+        print(result.markdown[:500])
+        print("Time taken: ", time.time() - start)
+
+    # Use WebKit
+    start = time.time()
+    async with AsyncWebCrawler(browser_type="webkit", verbose=True, headless = True) as crawler:
+        result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+        print(result.markdown[:500])
+        print("Time taken: ", time.time() - start)
+
+    # Use Chromium (default)
+    start = time.time()
+    async with AsyncWebCrawler(verbose=True, headless = True) as crawler:
+        result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
+        print(result.markdown[:500])
+        print("Time taken: ", time.time() - start)
+
+async def crawl_with_user_simultion():
+    async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
+        url = "YOUR-URL-HERE"
+        result = await crawler.arun(
+            url=url,
+            bypass_cache=True,
+            simulate_user = True,# Causes a series of random mouse movements and clicks to simulate user interaction
+            override_navigator = True # Overrides the navigator object to make it look like a real user
+        )
+        
+        print(result.markdown)    
+
+async def speed_comparison():
+    # print("\n--- Speed Comparison ---")
+    # print("Firecrawl (simulated):")
+    # print("Time taken: 7.02 seconds")
+    # print("Content length: 42074 characters")
+    # print("Images found: 49")
+    # print()
+    # Simulated Firecrawl performance
+    from firecrawl import FirecrawlApp
+    app = FirecrawlApp(api_key=os.environ['FIRECRAWL_API_KEY'])
+    start = time.time()
+    scrape_status = app.scrape_url(
+    'https://www.nbcnews.com/business',
+    params={'formats': ['markdown', 'html']}
+    )
+    end = time.time()
+    print("Firecrawl (simulated):")
+    print(f"Time taken: {end - start:.2f} seconds")
+    print(f"Content length: {len(scrape_status['markdown'])} characters")
+    print(f"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}")
+    print()    
+
+    async with AsyncWebCrawler() as crawler:
+        # Crawl4AI simple crawl
+        start = time.time()
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            word_count_threshold=0,
+            bypass_cache=True,
+            verbose=False,
+        )
+        end = time.time()
+        print("Crawl4AI (simple crawl):")
+        print(f"Time taken: {end - start:.2f} seconds")
+        print(f"Content length: {len(result.markdown)} characters")
+        print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
+        print()
+
+        # Crawl4AI with JavaScript execution
+        start = time.time()
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=[
+                "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+            ],
+            word_count_threshold=0,
+            bypass_cache=True,
+            verbose=False,
+        )
+        end = time.time()
+        print("Crawl4AI (with JavaScript execution):")
+        print(f"Time taken: {end - start:.2f} seconds")
+        print(f"Content length: {len(result.markdown)} characters")
+        print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
+
+    print("\nNote on Speed Comparison:")
+    print("The speed test conducted here may not reflect optimal conditions.")
+    print("When we call Firecrawl's API, we're seeing its best performance,")
+    print("while Crawl4AI's performance is limited by the local network speed.")
+    print("For a more accurate comparison, it's recommended to run these tests")
+    print("on servers with a stable and fast internet connection.")
+    print("Despite these limitations, Crawl4AI still demonstrates faster performance.")
+    print("If you run these tests in an environment with better network conditions,")
+    print("you may observe an even more significant speed advantage for Crawl4AI.")
+
+async def generate_knowledge_graph():
+    class Entity(BaseModel):
+        name: str
+        description: str
+        
+    class Relationship(BaseModel):
+        entity1: Entity
+        entity2: Entity
+        description: str
+        relation_type: str
+
+    class KnowledgeGraph(BaseModel):
+        entities: List[Entity]
+        relationships: List[Relationship]
+
+    extraction_strategy = LLMExtractionStrategy(
+            provider='openai/gpt-4o-mini', # Or any other provider, including Ollama and open source models
+            api_token=os.getenv('OPENAI_API_KEY'), # In case of Ollama just pass "no-token"
+            schema=KnowledgeGraph.model_json_schema(),
+            extraction_type="schema",
+            instruction="""Extract entities and relationships from the given text."""
+    )
+    async with AsyncWebCrawler() as crawler:
+        url = "https://paulgraham.com/love.html"
+        result = await crawler.arun(
+            url=url,
+            bypass_cache=True,
+            extraction_strategy=extraction_strategy,
+            # magic=True
+        )
+        # print(result.extracted_content)
+        with open(os.path.join(__location__, "kb.json"), "w") as f:
+            f.write(result.extracted_content)
+
+async def fit_markdown_remove_overlay():
+    async with AsyncWebCrawler(headless = False) as crawler:
+        url = "https://janineintheworld.com/places-to-visit-in-central-mexico"
+        result = await crawler.arun(
+            url=url,
+            bypass_cache=True,
+            word_count_threshold = 10,
+            remove_overlay_elements=True,
+            screenshot = True
+        )
+        # Save markdown to file
+        with open(os.path.join(__location__, "mexico_places.md"), "w") as f:
+            f.write(result.fit_markdown)
+
+    print("Done")
+
+
+async def main():
+    await simple_crawl()
+    await simple_example_with_running_js_code()
+    await simple_example_with_css_selector()
+    await use_proxy()
+    await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
+    await extract_structured_data_using_css_extractor()
+
+    # LLM extraction examples
+    await extract_structured_data_using_llm()
+    await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
+    await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+    await extract_structured_data_using_llm("ollama/llama3.2")    
+
+    # You always can pass custom headers to the extraction strategy
+    custom_headers = {
+        "Authorization": "Bearer your-custom-token",
+        "X-Custom-Header": "Some-Value"
+    }
+    await extract_structured_data_using_llm(extra_headers=custom_headers)
+    
+    # await crawl_dynamic_content_pages_method_1()
+    # await crawl_dynamic_content_pages_method_2()
+    await crawl_dynamic_content_pages_method_3()
+    
+    await crawl_custom_browser_type()
+    
+    await speed_comparison()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/examples/research_assistant.py

@@ -1,4 +1,4 @@
-# Make sur to install the required packageschainlit and groq
+# Make sure to install the required packageschainlit and groq
 import os, time
 from openai import AsyncOpenAI
 import chainlit as cl

docs/examples/sample_ecommerce.html

@@ -0,0 +1,106 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Sample E-commerce Page for JsonCssExtractionStrategy Testing</title>
+    <style>
+        body { font-family: Arial, sans-serif; line-height: 1.6; padding: 20px; }
+        .category { border: 1px solid #ddd; margin-bottom: 20px; padding: 10px; }
+        .product { border: 1px solid #eee; margin: 10px 0; padding: 10px; }
+        .product-details, .product-reviews, .related-products { margin-top: 10px; }
+        .review { background-color: #f9f9f9; margin: 5px 0; padding: 5px; }
+    </style>
+</head>
+<body>
+    <h1>Sample E-commerce Product Catalog</h1>
+    <div id="catalog"></div>
+
+    <script>
+        const categories = ['Electronics', 'Home & Kitchen', 'Books'];
+        const products = [
+            {
+                name: 'Smartphone X',
+                price: '$999',
+                brand: 'TechCorp',
+                model: 'X-2000',
+                features: ['5G capable', '6.5" OLED screen', '128GB storage'],
+                reviews: [
+                    { reviewer: 'John D.', rating: '4.5', text: 'Great phone, love the camera!' },
+                    { reviewer: 'Jane S.', rating: '5', text: 'Best smartphone I\'ve ever owned.' }
+                ],
+                related: [
+                    { name: 'Phone Case', price: '$29.99' },
+                    { name: 'Screen Protector', price: '$9.99' }
+                ]
+            },
+            {
+                name: 'Laptop Pro',
+                price: '$1499',
+                brand: 'TechMaster',
+                model: 'LT-3000',
+                features: ['Intel i7 processor', '16GB RAM', '512GB SSD'],
+                reviews: [
+                    { reviewer: 'Alice W.', rating: '4', text: 'Powerful machine, but a bit heavy.' },
+                    { reviewer: 'Bob M.', rating: '5', text: 'Perfect for my development work!' }
+                ],
+                related: [
+                    { name: 'Laptop Bag', price: '$49.99' },
+                    { name: 'Wireless Mouse', price: '$24.99' }
+                ]
+            }
+        ];
+
+        function createProductHTML(product) {
+            return `
+                <div class="product">
+                    <h3 class="product-name">${product.name}</h3>
+                    <p class="product-price">${product.price}</p>
+                    <div class="product-details">
+                        <span class="brand">${product.brand}</span>
+                        <span class="model">${product.model}</span>
+                    </div>
+                    <ul class="product-features">
+                        ${product.features.map(feature => `<li>${feature}</li>`).join('')}
+                    </ul>
+                    <div class="product-reviews">
+                        ${product.reviews.map(review => `
+                            <div class="review">
+                                <span class="reviewer">${review.reviewer}</span>
+                                <span class="rating">${review.rating}</span>
+                                <p class="review-text">${review.text}</p>
+                            </div>
+                        `).join('')}
+                    </div>
+                    <ul class="related-products">
+                        ${product.related.map(item => `
+                            <li>
+                                <span class="related-name">${item.name}</span>
+                                <span class="related-price">${item.price}</span>
+                            </li>
+                        `).join('')}
+                    </ul>
+                </div>
+            `;
+        }
+
+        function createCategoryHTML(category, products) {
+            return `
+                <div class="category">
+                    <h2 class="category-name">${category}</h2>
+                    ${products.map(createProductHTML).join('')}
+                </div>
+            `;
+        }
+
+        function populateCatalog() {
+            const catalog = document.getElementById('catalog');
+            categories.forEach(category => {
+                catalog.innerHTML += createCategoryHTML(category, products);
+            });
+        }
+
+        populateCatalog();
+    </script>
+</body>
+</html>

docs/examples/tmp/research_assistant_audio_not_completed.py

@@ -1,4 +1,4 @@
-# Make sur to install the required packageschainlit and groq
+# Make sure to install the required packageschainlit and groq
 import os, time
 from openai import AsyncOpenAI
 import chainlit as cl

docs/md_v1/api/core_classes_and_functions.md

@@ -0,0 +1,141 @@
+# Core Classes and Functions
+
+## Overview
+
+In this section, we will delve into the core classes and functions that make up the Crawl4AI library. This includes the `WebCrawler` class, various `CrawlerStrategy` classes, `ChunkingStrategy` classes, and `ExtractionStrategy` classes. Understanding these core components will help you leverage the full power of Crawl4AI for your web crawling and data extraction needs.
+
+## WebCrawler Class
+
+The `WebCrawler` class is the main class you'll interact with. It provides the interface for crawling web pages and extracting data.
+
+### Initialization
+
+```python
+from crawl4ai import WebCrawler
+
+# Create an instance of WebCrawler
+crawler = WebCrawler()
+```
+
+### Methods
+
+- **`warmup()`**: Prepares the crawler for use, such as loading necessary models.
+- **`run(url: str, **kwargs)`**: Runs the crawler on the specified URL with optional parameters for customization.
+
+```python
+crawler.warmup()
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(result)
+```
+
+## CrawlerStrategy Classes
+
+The `CrawlerStrategy` classes define how the web crawling is executed. The base class is `CrawlerStrategy`, which is extended by specific implementations like `LocalSeleniumCrawlerStrategy`.
+
+### CrawlerStrategy Base Class
+
+An abstract base class that defines the interface for different crawler strategies.
+
+```python
+from abc import ABC, abstractmethod
+
+class CrawlerStrategy(ABC):
+    @abstractmethod
+    def crawl(self, url: str, **kwargs) -> str:
+        pass
+    
+    @abstractmethod
+    def take_screenshot(self, save_path: str):
+        pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
+    
+    @abstractmethod
+    def set_hook(self, hook_type: str, hook: Callable):
+        pass
+```
+
+### LocalSeleniumCrawlerStrategy Class
+
+A concrete implementation of `CrawlerStrategy` that uses Selenium to crawl web pages.
+
+#### Initialization
+
+```python
+from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
+
+strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
+```
+
+#### Methods
+
+- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+```python
+result = strategy.crawl("https://www.example.com")
+strategy.take_screenshot("screenshot.png")
+strategy.update_user_agent("Mozilla/5.0")
+strategy.set_hook("before_get_url", lambda: print("About to get URL"))
+```
+
+## ChunkingStrategy Classes
+
+The `ChunkingStrategy` classes define how the text from a web page is divided into chunks. Here are a few examples:
+
+### RegexChunking Class
+
+Splits text using regular expressions.
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+chunker = RegexChunking(patterns=[r'\n\n'])
+chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
+```
+
+### NlpSentenceChunking Class
+
+Uses NLP to split text into sentences.
+
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+chunker = NlpSentenceChunking()
+chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
+```
+
+## ExtractionStrategy Classes
+
+The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks. Here are a few examples:
+
+### CosineStrategy Class
+
+Clusters text chunks based on cosine similarity.
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### LLMExtractionStrategy Class
+
+Uses a Language Model to extract meaningful blocks from HTML.
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+## Conclusion
+
+By understanding these core classes and functions, you can customize and extend Crawl4AI to suit your specific web crawling and data extraction needs. Happy crawling! 🕷️🤖
+

docs/md_v1/api/detailed_api_documentation.md

@@ -0,0 +1,338 @@
+# Detailed API Documentation
+
+## Overview
+
+This section provides comprehensive documentation for the Crawl4AI API, covering all classes, methods, and their parameters. This guide will help you understand how to utilize the API to its full potential, enabling efficient web crawling and data extraction.
+
+## WebCrawler Class
+
+The `WebCrawler` class is the primary interface for crawling web pages and extracting data.
+
+### Initialization
+
+```python
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+```
+
+### Methods
+
+#### `warmup()`
+
+Prepares the crawler for use, such as loading necessary models.
+
+```python
+crawler.warmup()
+```
+
+#### `run(url: str, **kwargs) -> CrawlResult`
+
+Crawls the specified URL and returns the result.
+
+- **Parameters:**
+  - `url` (str): The URL to crawl.
+  - `**kwargs`: Additional parameters for customization.
+
+- **Returns:**
+  - `CrawlResult`: An object containing the crawl result.
+
+- **Example:**
+
+```python
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(result)
+```
+
+### CrawlResult Class
+
+Represents the result of a crawl operation.
+
+- **Attributes:**
+  - `url` (str): The URL of the crawled page.
+  - `html` (str): The raw HTML of the page.
+  - `success` (bool): Whether the crawl was successful.
+  - `cleaned_html` (Optional[str]): The cleaned HTML.
+  - `media` (Dict[str, List[Dict]]): Media tags in the page (images, audio, video).
+  - `links` (Dict[str, List[Dict]]): Links in the page (external, internal).
+  - `screenshot` (Optional[str]): Base64 encoded screenshot.
+  - `markdown` (Optional[str]): Extracted content in Markdown format.
+  - `extracted_content` (Optional[str]): Extracted meaningful content.
+  - `metadata` (Optional[dict]): Metadata from the page.
+  - `error_message` (Optional[str]): Error message if any.
+
+## CrawlerStrategy Classes
+
+The `CrawlerStrategy` classes define how the web crawling is executed.
+
+### CrawlerStrategy Base Class
+
+An abstract base class for different crawler strategies.
+
+#### Methods
+
+- **`crawl(url: str, **kwargs) -> str`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+### LocalSeleniumCrawlerStrategy Class
+
+Uses Selenium to crawl web pages.
+
+#### Initialization
+
+```python
+from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
+
+strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
+```
+
+#### Methods
+
+- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+#### Example
+
+```python
+result = strategy.crawl("https://www.example.com")
+strategy.take_screenshot("screenshot.png")
+strategy.update_user_agent("Mozilla/5.0")
+strategy.set_hook("before_get_url", lambda: print("About to get URL"))
+```
+
+## ChunkingStrategy Classes
+
+The `ChunkingStrategy` classes define how the text from a web page is divided into chunks.
+
+### RegexChunking Class
+
+Splits text using regular expressions.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+chunker = RegexChunking(patterns=[r'\n\n'])
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into chunks.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
+```
+
+### NlpSentenceChunking Class
+
+Uses NLP to split text into sentences.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+chunker = NlpSentenceChunking()
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into sentences.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
+```
+
+### TopicSegmentationChunking Class
+
+Uses the TextTiling algorithm to segment text into topics.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import TopicSegmentationChunking
+
+chunker = TopicSegmentationChunking(num_keywords=3)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into topic-based segments.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
+```
+
+### FixedLengthWordChunking Class
+
+Splits text into chunks of fixed length based on the number of words.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import FixedLengthWordChunking
+
+chunker = FixedLengthWordChunking(chunk_size=100)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into fixed-length word chunks.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
+```
+
+### SlidingWindowChunking Class
+
+Uses a sliding window approach to chunk text.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import SlidingWindowChunking
+
+chunker = SlidingWindowChunking(window_size=100, step=50)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text using a sliding window approach.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
+```
+
+## ExtractionStrategy Classes
+
+The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks.
+
+### NoExtractionStrategy Class
+
+Returns the entire HTML content without any modification.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import NoExtractionStrategy
+
+extractor = NoExtractionStrategy()
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Returns the HTML content.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### LLMExtractionStrategy Class
+
+Uses a Language Model to extract meaningful blocks from HTML.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts meaningful content using the LLM.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### CosineStrategy Class
+
+Clusters text chunks based on cosine similarity.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts clusters of text based on cosine similarity.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### TopicExtractionStrategy Class
+
+Uses the TextTiling algorithm to segment HTML content into topics and extract keywords.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import TopicExtractionStrategy
+
+extractor = TopicExtractionStrategy(num_keywords=3)
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts topic-based segments and keywords.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+## Parameters
+
+Here are the common parameters used across various classes and methods:
+
+- **`url`** (str): The URL to crawl.
+- **`html`** (str): The HTML content of the page.
+- **`user_agent`** (str): The user agent for the HTTP requests.
+- **`patterns`** (list): A list of regular expression patterns for chunking.
+- **`num_keywords`** (int): Number of keywords for topic extraction.
+- **`chunk_size`** (int): Number of words in each chunk.
+- **`window_size`** (int): Number of words in the sliding window.
+- **`step`** (int): Step size for the sliding window.
+- **`semantic_filter`** (str): Keywords for filtering relevant documents.
+- **`word_count_threshold`** (int): Minimum number of words per cluster.
+- **`max_dist`** (float): Maximum cophenetic distance for clustering.
+- **`linkage_method`** (str): Linkage method for hierarchical clustering.
+- **`top_k`** (int): Number of top categories to extract.
+- **`provider`** (
+
+str): Provider for language model completions.
+- **`api_token`** (str): API token for the provider.
+- **`instruction`** (str): Instruction to guide the LLM extraction.
+
+## Conclusion
+
+This detailed API documentation provides a thorough understanding of the classes, methods, and parameters in the Crawl4AI library. With this knowledge, you can effectively use the API to perform advanced web crawling and data extraction tasks.

docs/md_v1/assets/highlight_init.js

@@ -0,0 +1,6 @@
+document.addEventListener('DOMContentLoaded', (event) => {
+    document.querySelectorAll('pre code').forEach((block) => {
+      hljs.highlightBlock(block);
+    });
+  });
+  

docs/md_v1/assets/styles.css

@@ -0,0 +1,153 @@
+@font-face {
+    font-family: "Monaco";
+    font-style: normal;
+    font-weight: normal;
+    src: local("Monaco"), url("Monaco.woff") format("woff");
+}
+
+:root {
+    --global-font-size: 16px;
+    --global-line-height: 1.5em;
+    --global-space: 10px;
+    --font-stack: Menlo, Monaco, Lucida Console, Liberation Mono, DejaVu Sans Mono, Bitstream Vera Sans Mono,
+        Courier New, monospace, serif;
+    --font-stack: dm, Monaco, Courier New, monospace, serif;
+    --mono-font-stack: Menlo, Monaco, Lucida Console, Liberation Mono, DejaVu Sans Mono, Bitstream Vera Sans Mono,
+        Courier New, monospace, serif;
+
+    --background-color: #151515; /* Dark background */
+    --font-color: #eaeaea; /* Light font color for contrast */
+    --invert-font-color: #151515; /* Dark color for inverted elements */
+    --primary-color: #1a95e0; /* Primary color can remain the same or be adjusted for better contrast */
+    --secondary-color: #727578; /* Secondary color for less important text */
+    --error-color: #ff5555; /* Bright color for errors */
+    --progress-bar-background: #444; /* Darker background for progress bar */
+    --progress-bar-fill: #1a95e0; /* Bright color for progress bar fill */
+    --code-bg-color: #1e1e1e; /* Darker background for code blocks */
+    --input-style: solid; /* Keeping input style solid */
+    --block-background-color: #202020; /* Darker background for block elements */
+    --global-font-color: #eaeaea; /* Light font color for global elements */
+
+    --background-color: #222225;
+
+    --background-color: #070708;
+    --page-width: 70em;
+    --font-color: #e8e9ed;
+    --invert-font-color: #222225;
+    --secondary-color: #a3abba;
+    --secondary-color: #d5cec0;
+    --tertiary-color: #a3abba;
+    --primary-color: #09b5a5; /* Updated to the brand color */
+    --primary-color: #50ffff; /* Updated to the brand color */
+    --error-color: #ff3c74;
+    --progress-bar-background: #3f3f44;
+    --progress-bar-fill: #09b5a5; /* Updated to the brand color */
+    --code-bg-color: #3f3f44;
+    --input-style: solid;
+    --display-h1-decoration: none;
+
+    --display-h1-decoration: none;
+}
+
+/* body {
+    background-color: var(--background-color);
+    color: var(--font-color);
+}
+
+a {
+    color: var(--primary-color);
+}
+
+a:hover {
+    background-color: var(--primary-color);
+    color: var(--invert-font-color);
+}
+
+blockquote::after {
+    color: #444; 
+}
+
+pre, code {
+    background-color: var(--code-bg-color);
+    color: var(--font-color);
+}
+
+.terminal-nav:first-child {
+    border-bottom: 1px dashed var(--secondary-color);
+} */
+
+.terminal-mkdocs-main-content {
+    line-height: var(--global-line-height);
+}
+
+strong,
+.highlight {
+    /* background: url(//s2.svgbox.net/pen-brushes.svg?ic=brush-1&color=50ffff); */
+    background-color: #50ffff33;
+}
+
+.terminal-card > header {
+    color: var(--font-color);
+    text-align: center;
+    background-color: var(--progress-bar-background);
+    padding: 0.3em 0.5em;
+}
+.btn.btn-sm {
+    color: var(--font-color);
+    padding: 0.2em 0.5em;
+    font-size: 0.8em;
+}
+
+.loading-message {
+    display: none;
+    margin-top: 20px;
+}
+
+.response-section {
+    display: none;
+    padding-top: 20px;
+}
+
+.tabs {
+    display: flex;
+    flex-direction: column;
+}
+.tab-list {
+    display: flex;
+    padding: 0;
+    margin: 0;
+    list-style-type: none;
+    border-bottom: 1px solid var(--font-color);
+}
+.tab-item {
+    cursor: pointer;
+    padding: 10px;
+    border: 1px solid var(--font-color);
+    margin-right: -1px;
+    border-bottom: none;
+}
+.tab-item:hover,
+.tab-item:focus,
+.tab-item:active {
+    background-color: var(--progress-bar-background);
+}
+.tab-content {
+    display: none;
+    border: 1px solid var(--font-color);
+    border-top: none;
+}
+.tab-content:first-of-type {
+    display: block;
+}
+
+.tab-content header {
+    padding: 0.5em;
+    display: flex; 
+    justify-content: end; 
+    align-items: center;
+    background-color: var(--progress-bar-background);
+}
+.tab-content pre {
+    margin: 0;
+    max-height: 300px; overflow: auto; border:none;
+}

docs/md_v1/changelog.md

@@ -0,0 +1,102 @@
+# Changelog
+
+## [v0.2.77] - 2024-08-04
+
+Significant improvements in text processing and performance:
+
+- 🚀 **Dependency reduction**: Removed dependency on spaCy model for text chunk labeling in cosine extraction strategy.
+- 🤖 **Transformer upgrade**: Implemented text sequence classification using a transformer model for labeling text chunks.
+- ⚡ **Performance enhancement**: Improved model loading speed due to removal of spaCy dependency.
+- 🔧 **Future-proofing**: Laid groundwork for potential complete removal of spaCy dependency in future versions.
+
+These changes address issue #68 and provide a foundation for faster, more efficient text processing in Crawl4AI.
+
+## [v0.2.76] - 2024-08-02
+
+Major improvements in functionality, performance, and cross-platform compatibility! 🚀
+
+- 🐳 **Docker enhancements**: Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
+- 🌐 **Official Docker Hub image**: Launched our first official image on Docker Hub for streamlined deployment.
+- 🔧 **Selenium upgrade**: Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
+- 🖼️ **Image description**: Implemented ability to generate textual descriptions for extracted images from web pages.
+- ⚡ **Performance boost**: Various improvements to enhance overall speed and performance.
+
+A big shoutout to our amazing community contributors:
+- [@aravindkarnam](https://github.com/aravindkarnam) for developing the textual description extraction feature.
+- [@FractalMind](https://github.com/FractalMind) for creating the first official Docker Hub image and fixing Dockerfile errors.
+- [@ketonkss4](https://github.com/ketonkss4) for identifying Selenium's new capabilities, helping us reduce dependencies.
+
+Your contributions are driving Crawl4AI forward! 🙌
+
+## [v0.2.75] - 2024-07-19
+
+Minor improvements for a more maintainable codebase:
+
+- 🔄 Fixed typos in `chunking_strategy.py` and `crawler_strategy.py` to improve code readability
+- 🔄 Removed `.test_pads/` directory from `.gitignore` to keep our repository clean and organized
+
+These changes may seem small, but they contribute to a more stable and sustainable codebase. By fixing typos and updating our `.gitignore` settings, we're ensuring that our code is easier to maintain and scale in the long run.
+
+
+## v0.2.74 - 2024-07-08
+A slew of exciting updates to improve the crawler's stability and robustness! 🎉
+
+- 💻 **UTF encoding fix**: Resolved the Windows \"charmap\" error by adding UTF encoding.
+- 🛡️ **Error handling**: Implemented MaxRetryError exception handling in LocalSeleniumCrawlerStrategy.
+- 🧹 **Input sanitization**: Improved input sanitization and handled encoding issues in LLMExtractionStrategy.
+- 🚮 **Database cleanup**: Removed existing database file and initialized a new one.
+
+## [v0.2.73] - 2024-07-03
+
+💡 In this release, we've bumped the version to v0.2.73 and refreshed our documentation to ensure you have the best experience with our project.
+
+* Supporting website need "with-head" mode to crawl the website with head.
+* Fixing the installation issues for setup.py and dockerfile.
+* Resolve multiple issues.
+
+## [v0.2.72] - 2024-06-30
+
+This release brings exciting updates and improvements to our project! 🎉
+
+* 📚 **Documentation Updates**: Our documentation has been revamped to reflect the latest changes and additions.
+* 🚀 **New Modes in setup.py**: We've added support for three new modes in setup.py: default, torch, and transformers. This enhances the project's flexibility and usability.
+* 🐳 **Docker File Updates**: The Docker file has been updated to ensure seamless compatibility with the new modes and improvements.
+* 🕷️ **Temporary Solution for Headless Crawling**: We've implemented a temporary solution to overcome issues with crawling websites in headless mode.
+
+These changes aim to improve the overall user experience, provide more flexibility, and enhance the project's performance. We're thrilled to share these updates with you and look forward to continuing to evolve and improve our project!
+
+## [0.2.71] - 2024-06-26
+
+**Improved Error Handling and Performance** 🚧
+
+* 🚫 Refactored `crawler_strategy.py` to handle exceptions and provide better error messages, making it more robust and reliable.
+* 💻 Optimized the `get_content_of_website_optimized` function in `utils.py` for improved performance, reducing potential bottlenecks.
+* 💻 Updated `utils.py` with the latest changes, ensuring consistency and accuracy.
+* 🚫 Migrated to `ChromeDriverManager` to resolve Chrome driver download issues, providing a smoother user experience.
+
+These changes focus on refining the existing codebase, resulting in a more stable, efficient, and user-friendly experience. With these improvements, you can expect fewer errors and better performance in the crawler strategy and utility functions.
+
+## [0.2.71] - 2024-06-25
+### Fixed
+- Speed up twice the extraction function.
+
+## [0.2.6] - 2024-06-22
+### Fixed
+- Fix issue #19: Update Dockerfile to ensure compatibility across multiple platforms.
+
+## [0.2.5] - 2024-06-18
+### Added
+- Added five important hooks to the crawler:
+  - on_driver_created: Called when the driver is ready for initializations.
+  - before_get_url: Called right before Selenium fetches the URL.
+  - after_get_url: Called after Selenium fetches the URL.
+  - before_return_html: Called when the data is parsed and ready.
+  - on_user_agent_updated: Called when the user changes the user_agent, causing the driver to reinitialize.
+- Added an example in `quickstart.py` in the example folder under the docs.
+- Enhancement issue #24: Replaced inline HTML tags (e.g., DEL, INS, SUB, ABBR) with textual format for better context handling in LLM.
+- Maintaining the semantic context of inline tags (e.g., abbreviation, DEL, INS) for improved LLM-friendliness.
+- Updated Dockerfile to ensure compatibility across multiple platforms (Hopefully!).
+
+## [0.2.4] - 2024-06-17
+### Fixed
+- Fix issue #22: Use MD5 hash for caching HTML files to handle long URLs

docs/md_v1/contact.md

@@ -0,0 +1,25 @@
+# Contact
+If you have any questions, suggestions, or feedback, please feel free to reach out to us:
+
+- GitHub: [unclecode](https://github.com/unclecode)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+
+## Contributing 🤝
+
+We welcome contributions from the open-source community to help improve Crawl4AI and make it even more valuable for AI enthusiasts and developers. To contribute, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch for your feature or bug fix.
+3. Make your changes and commit them with descriptive messages.
+4. Push your changes to your forked repository.
+5. Submit a pull request to the main repository.
+
+For more information on contributing, please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md).
+
+## License 📄
+
+Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
+
+Let's work together to make the web more accessible and useful for AI applications! 💪🌐🤖

docs/md_v1/demo.md

@@ -0,0 +1,231 @@
+# Interactive Demo for Crowler
+<div id="demo">
+    <form id="crawlForm" class="terminal-form">
+        <fieldset>
+            <legend>Enter URL and Options</legend>
+            <div class="form-group">
+                <label for="url">Enter URL:</label>
+                <input type="text" id="url" name="url" required>
+            </div>
+            <div class="form-group">
+                <label for="screenshot">Get Screenshot:</label>
+                <input type="checkbox" id="screenshot" name="screenshot">
+            </div>
+            <div class="form-group">
+                <button class="btn btn-default" type="submit">Submit</button>
+            </div>
+
+        </fieldset>
+    </form>
+
+    <div id="loading" class="loading-message">
+        <div class="terminal-alert terminal-alert-primary">Loading... Please wait.</div>
+    </div>
+
+    <section id="response" class="response-section">
+        <h2>Response</h2>
+        <div class="tabs">
+            <ul class="tab-list">
+                <li class="tab-item" onclick="showTab('markdown')">Markdown</li>
+                <li class="tab-item" onclick="showTab('cleanedHtml')">Cleaned HTML</li>
+                <li class="tab-item" onclick="showTab('media')">Media</li>
+                <li class="tab-item" onclick="showTab('extractedContent')">Extracted Content</li>
+                <li class="tab-item" onclick="showTab('screenshot')">Screenshot</li>
+                <li class="tab-item" onclick="showTab('pythonCode')">Python Code</li>
+            </ul>
+            <div class="tab-content" id="tab-markdown">
+                <header>
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('markdownContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('markdownContent', 'markdown.md')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="markdownContent" class="language-markdown hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-cleanedHtml" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('cleanedHtmlContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('cleanedHtmlContent', 'cleaned.html')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="cleanedHtmlContent" class="language-html hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-media" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('mediaContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('mediaContent', 'media.json')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="mediaContent" class="language-json hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-extractedContent" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('extractedContentContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('extractedContentContent', 'extracted_content.json')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="extractedContentContent" class="language-json hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-screenshot" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadImage('screenshotContent', 'screenshot.png')">Download</button>
+                    </div>
+                </header>
+                <pre><img id="screenshotContent" /></pre>
+            </div>
+
+            <div class="tab-content" id="tab-pythonCode" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('pythonCode')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('pythonCode', 'example.py')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="pythonCode" class="language-python hljs"></code></pre>
+            </div>
+        </div>
+    </section>
+
+    <div id="error" class="error-message" style="display: none; margin-top:1em;">
+        <div class="terminal-alert terminal-alert-error"></div>
+    </div>
+
+    <script>
+        function showTab(tabId) {
+            const tabs = document.querySelectorAll('.tab-content');
+            tabs.forEach(tab => tab.style.display = 'none');
+            document.getElementById(`tab-${tabId}`).style.display = 'block';
+        }
+
+        function redo(codeBlock, codeText){
+            codeBlock.classList.remove('hljs');
+            codeBlock.removeAttribute('data-highlighted');
+
+            // Set new code and re-highlight
+            codeBlock.textContent = codeText;
+            hljs.highlightBlock(codeBlock);
+        }
+
+        function copyToClipboard(elementId) {
+            const content = document.getElementById(elementId).textContent;
+            navigator.clipboard.writeText(content).then(() => {
+                alert('Copied to clipboard');
+            });
+        }
+
+        function downloadContent(elementId, filename) {
+            const content = document.getElementById(elementId).textContent;
+            const blob = new Blob([content], { type: 'text/plain' });
+            const url = window.URL.createObjectURL(blob);
+            const a = document.createElement('a');
+            a.style.display = 'none';
+            a.href = url;
+            a.download = filename;
+            document.body.appendChild(a);
+            a.click();
+            window.URL.revokeObjectURL(url);
+            document.body.removeChild(a);
+        }
+
+        function downloadImage(elementId, filename) {
+            const content = document.getElementById(elementId).src;
+            const a = document.createElement('a');
+            a.style.display = 'none';
+            a.href = content;
+            a.download = filename;
+            document.body.appendChild(a);
+            a.click();
+            document.body.removeChild(a);
+        }
+
+        document.getElementById('crawlForm').addEventListener('submit', function(event) {
+            event.preventDefault();
+            document.getElementById('loading').style.display = 'block';
+            document.getElementById('response').style.display = 'none';
+
+            const url = document.getElementById('url').value;
+            const screenshot = document.getElementById('screenshot').checked;
+            const data = {
+                urls: [url],
+                bypass_cache: false,
+                word_count_threshold: 5,
+                screenshot: screenshot
+            };
+
+            fetch('/crawl', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify(data)
+            })
+            .then(response => {
+                if (!response.ok) {
+                    if (response.status === 429) {
+                        return response.json().then(err => { 
+                            throw Object.assign(new Error('Rate limit exceeded'), { status: 429, details: err });
+                        });
+                    }
+                    throw new Error('Network response was not ok');
+                }
+                return response.json();
+            })
+            .then(data => {
+                data = data.results[0]; // Only one URL is requested
+                document.getElementById('loading').style.display = 'none';
+                document.getElementById('response').style.display = 'block';
+                redo(document.getElementById('markdownContent'), data.markdown);
+                redo(document.getElementById('cleanedHtmlContent'), data.cleaned_html);
+                redo(document.getElementById('mediaContent'), JSON.stringify(data.media, null, 2));
+                redo(document.getElementById('extractedContentContent'), data.extracted_content);
+                if (screenshot) {
+                    document.getElementById('screenshotContent').src = `data:image/png;base64,${data.screenshot}`;
+                }
+                const pythonCode = `
+from crawl4ai.web_crawler import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+    url='${url}',
+    screenshot=${screenshot}
+)
+print(result)
+                `;
+                redo(document.getElementById('pythonCode'), pythonCode);
+                document.getElementById('error').style.display = 'none';
+            })
+            .catch(error => {
+                document.getElementById('loading').style.display = 'none';
+                document.getElementById('error').style.display = 'block';
+                let errorMessage = 'An unexpected error occurred. Please try again later.';
+                
+                if (error.status === 429) {
+                    const details = error.details;
+                    if (details.retry_after) {
+                        errorMessage = `Rate limit exceeded. Please wait ${parseFloat(details.retry_after).toFixed(1)} seconds before trying again.`;
+                    } else if (details.reset_at) {
+                        const resetTime = new Date(details.reset_at);
+                        const waitTime = Math.ceil((resetTime - new Date()) / 1000);
+                        errorMessage = `Rate limit exceeded. Please try again after ${waitTime} seconds.`;
+                    } else {
+                        errorMessage = `Rate limit exceeded. Please try again later.`;
+                    }
+                } else if (error.message) {
+                    errorMessage = error.message;
+                }
+                
+                document.querySelector('#error .terminal-alert').textContent = errorMessage;
+            });
+        });
+    </script>
+</div>

docs/md_v1/examples/hooks_auth.md

@@ -0,0 +1,110 @@
+# Hooks & Auth for AsyncWebCrawler
+
+Crawl4AI's AsyncWebCrawler allows you to customize the behavior of the web crawler using hooks. Hooks are asynchronous functions that are called at specific points in the crawling process, allowing you to modify the crawler's behavior or perform additional actions. This example demonstrates how to use various hooks to customize the asynchronous crawling process.
+
+## Example: Using Crawler Hooks with AsyncWebCrawler
+
+Let's see how we can customize the AsyncWebCrawler using hooks! In this example, we'll:
+
+1. Configure the browser when it's created.
+2. Add custom headers before navigating to the URL.
+3. Log the current URL after navigation.
+4. Perform actions after JavaScript execution.
+5. Log the length of the HTML before returning it.
+
+### Hook Definitions
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+from playwright.async_api import Page, Browser
+
+async def on_browser_created(browser: Browser):
+    print("[HOOK] on_browser_created")
+    # Example customization: set browser viewport size
+    context = await browser.new_context(viewport={'width': 1920, 'height': 1080})
+    page = await context.new_page()
+    
+    # Example customization: logging in to a hypothetical website
+    await page.goto('https://example.com/login')
+    await page.fill('input[name="username"]', 'testuser')
+    await page.fill('input[name="password"]', 'password123')
+    await page.click('button[type="submit"]')
+    await page.wait_for_selector('#welcome')
+    
+    # Add a custom cookie
+    await context.add_cookies([{'name': 'test_cookie', 'value': 'cookie_value', 'url': 'https://example.com'}])
+    
+    await page.close()
+    await context.close()
+
+async def before_goto(page: Page):
+    print("[HOOK] before_goto")
+    # Example customization: add custom headers
+    await page.set_extra_http_headers({'X-Test-Header': 'test'})
+
+async def after_goto(page: Page):
+    print("[HOOK] after_goto")
+    # Example customization: log the URL
+    print(f"Current URL: {page.url}")
+
+async def on_execution_started(page: Page):
+    print("[HOOK] on_execution_started")
+    # Example customization: perform actions after JS execution
+    await page.evaluate("console.log('Custom JS executed')")
+
+async def before_return_html(page: Page, html: str):
+    print("[HOOK] before_return_html")
+    # Example customization: log the HTML length
+    print(f"HTML length: {len(html)}")
+    return page
+```
+
+### Using the Hooks with the AsyncWebCrawler
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+async def main():
+    print("\n🔗 Using Crawler Hooks: Let's see how we can customize the AsyncWebCrawler using hooks!")
+    
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(verbose=True)
+    crawler_strategy.set_hook('on_browser_created', on_browser_created)
+    crawler_strategy.set_hook('before_goto', before_goto)
+    crawler_strategy.set_hook('after_goto', after_goto)
+    crawler_strategy.set_hook('on_execution_started', on_execution_started)
+    crawler_strategy.set_hook('before_return_html', before_return_html)
+    
+    async with AsyncWebCrawler(verbose=True, crawler_strategy=crawler_strategy) as crawler:
+        result = await crawler.arun(
+            url="https://example.com",
+            js_code="window.scrollTo(0, document.body.scrollHeight);",
+            wait_for="footer"
+        )
+
+    print("📦 Crawler Hooks result:")
+    print(result)
+
+asyncio.run(main())
+```
+
+### Explanation
+
+- `on_browser_created`: This hook is called when the Playwright browser is created. It sets up the browser context, logs in to a website, and adds a custom cookie.
+- `before_goto`: This hook is called right before Playwright navigates to the URL. It adds custom HTTP headers.
+- `after_goto`: This hook is called after Playwright navigates to the URL. It logs the current URL.
+- `on_execution_started`: This hook is called after any custom JavaScript is executed. It performs additional JavaScript actions.
+- `before_return_html`: This hook is called before returning the HTML content. It logs the length of the HTML content.
+
+### Additional Ideas
+
+- **Handling authentication**: Use the `on_browser_created` hook to handle login processes or set authentication tokens.
+- **Dynamic header modification**: Modify headers based on the target URL or other conditions in the `before_goto` hook.
+- **Content verification**: Use the `after_goto` hook to verify that the expected content is present on the page.
+- **Custom JavaScript injection**: Inject and execute custom JavaScript using the `on_execution_started` hook.
+- **Content preprocessing**: Modify or analyze the HTML content in the `before_return_html` hook before it's returned.
+
+By using these hooks, you can customize the behavior of the AsyncWebCrawler to suit your specific needs, including handling authentication, modifying requests, and preprocessing content.

docs/md_v1/examples/index.md

@@ -0,0 +1,33 @@
+# Examples
+
+Welcome to the examples section of Crawl4AI documentation! In this section, you will find practical examples demonstrating how to use Crawl4AI for various web crawling and data extraction tasks. Each example is designed to showcase different features and capabilities of the library.
+
+## Examples Index
+
+### [LLM Extraction](llm_extraction.md)
+
+This example demonstrates how to use Crawl4AI to extract information using Large Language Models (LLMs). You will learn how to configure the `LLMExtractionStrategy` to get structured data from web pages.
+
+### [JSON CSS Extraction](json_css_extraction.md)
+
+This example demonstrates how to use Crawl4AI to extract structured data without using LLM, and just focusing on page structure. You will learn how to use the `JsonCssExtractionStrategy` to extract data using CSS selectors.
+
+### [JS Execution & CSS Filtering](js_execution_css_filtering.md)
+
+Learn how to execute custom JavaScript code and filter data using CSS selectors. This example shows how to perform complex web interactions and extract specific content from web pages.
+
+### [Hooks & Auth](hooks_auth.md)
+
+This example covers the use of custom hooks for authentication and other pre-crawling tasks. You will see how to set up hooks to modify headers, authenticate sessions, and perform other preparatory actions before crawling.
+
+### [Summarization](summarization.md)
+
+Discover how to use Crawl4AI to summarize web page content. This example demonstrates the summarization capabilities of the library, helping you extract concise information from lengthy web pages.
+
+### [Research Assistant](research_assistant.md)
+
+In this example, Crawl4AI is used as a research assistant to gather and organize information from multiple sources. You will learn how to use various extraction and chunking strategies to compile a comprehensive report.
+
+---
+
+Each example includes detailed explanations and code snippets to help you understand and implement the features in your projects. Click on the links to explore each example and start making the most of Crawl4AI!

docs/md_v1/examples/js_execution_css_filtering.md

@@ -0,0 +1,104 @@
+# JS Execution & CSS Filtering with AsyncWebCrawler
+
+In this example, we'll demonstrate how to use Crawl4AI's AsyncWebCrawler to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
+
+## Example: Extracting Structured Data Asynchronously
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.chunking_strategy import RegexChunking
+from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+
+async def main():
+    # Define the JavaScript code to click the "Load More" button
+    js_code = """
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+    if (loadMoreButton) {
+        loadMoreButton.click();
+        // Wait for new content to load
+        await new Promise(resolve => setTimeout(resolve, 2000));
+    }
+    """
+
+    # Define a wait_for function to ensure content is loaded
+    wait_for = """
+    () => {
+        const articles = document.querySelectorAll('article.tease-card');
+        return articles.length > 10;
+    }
+    """
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Run the crawler with keyword filtering and CSS selector
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            extraction_strategy=CosineStrategy(
+                semantic_filter="technology",
+            ),
+            chunking_strategy=RegexChunking(),
+        )
+
+    # Display the extracted result
+    print(result.extracted_content)
+
+# Run the async function
+asyncio.run(main())
+```
+
+### Explanation
+
+1. **Asynchronous Execution**: We use `AsyncWebCrawler` with async/await syntax for non-blocking execution.
+
+2. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button and waits for new content to load.
+
+3. **Wait Condition**: The `wait_for` function ensures that the page has loaded more than 10 articles before proceeding with the extraction.
+
+4. **CSS Selector**: The `css_selector="article.tease-card"` parameter ensures that only article cards are extracted from the web page.
+
+5. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
+
+6. **Chunking Strategy**: We use `RegexChunking()` to split the content into manageable chunks for processing.
+
+## Advanced Usage: Custom Session and Multiple Requests
+
+For more complex scenarios where you need to maintain state across multiple requests or execute additional JavaScript after the initial page load, you can use a custom session:
+
+```python
+async def advanced_crawl():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Initial crawl with custom session
+        result1 = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            session_id="business_session"
+        )
+
+        # Execute additional JavaScript in the same session
+        result2 = await crawler.crawler_strategy.execute_js(
+            session_id="business_session",
+            js_code="window.scrollTo(0, document.body.scrollHeight);",
+            wait_for_js="() => window.innerHeight + window.scrollY >= document.body.offsetHeight"
+        )
+
+        # Process results
+        print("Initial crawl result:", result1.extracted_content)
+        print("Additional JS execution result:", result2.html)
+
+asyncio.run(advanced_crawl())
+```
+
+This advanced example demonstrates how to:
+1. Use a custom session to maintain state across requests.
+2. Execute additional JavaScript after the initial page load.
+3. Wait for specific conditions using JavaScript functions.
+
+## Try It Yourself
+
+These examples demonstrate the power and flexibility of Crawl4AI's AsyncWebCrawler in handling complex web interactions and extracting meaningful data asynchronously. You can customize the JavaScript code, CSS selectors, extraction strategies, and waiting conditions to suit your specific requirements.

docs/md_v1/examples/json_css_extraction.md

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+# JSON CSS Extraction Strategy with AsyncWebCrawler
+
+The `JsonCssExtractionStrategy` is a powerful feature of Crawl4AI that allows you to extract structured data from web pages using CSS selectors. This method is particularly useful when you need to extract specific data points from a consistent HTML structure, such as tables or repeated elements. Here's how to use it with the AsyncWebCrawler.
+
+## Overview
+
+The `JsonCssExtractionStrategy` works by defining a schema that specifies:
+1. A base CSS selector for the repeating elements
+2. Fields to extract from each element, each with its own CSS selector
+
+This strategy is fast and efficient, as it doesn't rely on external services like LLMs for extraction.
+
+## Example: Extracting Cryptocurrency Prices from Coinbase
+
+Let's look at an example that extracts cryptocurrency prices from the Coinbase explore page.
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def extract_structured_data_using_css_extractor():
+    print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
+    
+    # Define the extraction schema
+    schema = {
+        "name": "Coinbase Crypto Prices",
+        "baseSelector": ".cds-tableRow-t45thuk",
+        "fields": [
+            {
+                "name": "crypto",
+                "selector": "td:nth-child(1) h2",
+                "type": "text",
+            },
+            {
+                "name": "symbol",
+                "selector": "td:nth-child(1) p",
+                "type": "text",
+            },
+            {
+                "name": "price",
+                "selector": "td:nth-child(2)",
+                "type": "text",
+            }
+        ],
+    }
+
+    # Create the extraction strategy
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    # Use the AsyncWebCrawler with the extraction strategy
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.coinbase.com/explore",
+            extraction_strategy=extraction_strategy,
+            bypass_cache=True,
+        )
+
+        assert result.success, "Failed to crawl the page"
+
+        # Parse the extracted content
+        crypto_prices = json.loads(result.extracted_content)
+        print(f"Successfully extracted {len(crypto_prices)} cryptocurrency prices")
+        print(json.dumps(crypto_prices[0], indent=2))
+
+    return crypto_prices
+
+# Run the async function
+asyncio.run(extract_structured_data_using_css_extractor())
+```
+
+## Explanation of the Schema
+
+The schema defines how to extract the data:
+
+- `name`: A descriptive name for the extraction task.
+- `baseSelector`: The CSS selector for the repeating elements (in this case, table rows).
+- `fields`: An array of fields to extract from each element:
+  - `name`: The name to give the extracted data.
+  - `selector`: The CSS selector to find the specific data within the base element.
+  - `type`: The type of data to extract (usually "text" for textual content).
+
+## Advantages of JsonCssExtractionStrategy
+
+1. **Speed**: CSS selectors are fast to execute, making this method efficient for large datasets.
+2. **Precision**: You can target exactly the elements you need.
+3. **Structured Output**: The result is already structured as JSON, ready for further processing.
+4. **No External Dependencies**: Unlike LLM-based strategies, this doesn't require any API calls to external services.
+
+## Tips for Using JsonCssExtractionStrategy
+
+1. **Inspect the Page**: Use browser developer tools to identify the correct CSS selectors.
+2. **Test Selectors**: Verify your selectors in the browser console before using them in the script.
+3. **Handle Dynamic Content**: If the page uses JavaScript to load content, you may need to combine this with JS execution (see the Advanced Usage section).
+4. **Error Handling**: Always check the `result.success` flag and handle potential failures.
+
+## Advanced Usage: Combining with JavaScript Execution
+
+For pages that load data dynamically, you can combine the `JsonCssExtractionStrategy` with JavaScript execution:
+
+```python
+async def extract_dynamic_structured_data():
+    schema = {
+        "name": "Dynamic Crypto Prices",
+        "baseSelector": ".crypto-row",
+        "fields": [
+            {"name": "name", "selector": ".crypto-name", "type": "text"},
+            {"name": "price", "selector": ".crypto-price", "type": "text"},
+        ]
+    }
+
+    js_code = """
+    window.scrollTo(0, document.body.scrollHeight);
+    await new Promise(resolve => setTimeout(resolve, 2000));  // Wait for 2 seconds
+    """
+
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://example.com/crypto-prices",
+            extraction_strategy=extraction_strategy,
+            js_code=js_code,
+            wait_for=".crypto-row:nth-child(20)",  # Wait for 20 rows to load
+            bypass_cache=True,
+        )
+
+        crypto_data = json.loads(result.extracted_content)
+        print(f"Extracted {len(crypto_data)} cryptocurrency entries")
+
+asyncio.run(extract_dynamic_structured_data())
+```
+
+This advanced example demonstrates how to:
+1. Execute JavaScript to trigger dynamic content loading.
+2. Wait for a specific condition (20 rows loaded) before extraction.
+3. Extract data from the dynamically loaded content.
+
+By mastering the `JsonCssExtractionStrategy`, you can efficiently extract structured data from a wide variety of web pages, making it a valuable tool in your web scraping toolkit.
+
+For more details on schema definitions and advanced extraction strategies, check out the[Advanced JsonCssExtraction](../full_details/advanced_jsoncss_extraction.md).

docs/md_v1/examples/llm_extraction.md

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+# LLM Extraction with AsyncWebCrawler
+
+Crawl4AI's AsyncWebCrawler allows you to use Language Models (LLMs) to extract structured data or relevant content from web pages asynchronously. Below are two examples demonstrating how to use `LLMExtractionStrategy` for different purposes with the AsyncWebCrawler.
+
+## Example 1: Extract Structured Data
+
+In this example, we use the `LLMExtractionStrategy` to extract structured data (model names and their fees) from the OpenAI pricing page.
+
+```python
+import os
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from pydantic import BaseModel, Field
+
+class OpenAIModelFee(BaseModel):
+    model_name: str = Field(..., description="Name of the OpenAI model.")
+    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+    output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
+
+async def extract_openai_fees():
+    url = 'https://openai.com/api/pricing/'
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url=url,
+            word_count_threshold=1,
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o",
+                api_token=os.getenv('OPENAI_API_KEY'),
+                schema=OpenAIModelFee.model_json_schema(),
+                extraction_type="schema",
+                instruction="From the crawled content, extract all mentioned model names along with their "
+                            "fees for input and output tokens. Make sure not to miss anything in the entire content. "
+                            'One extracted model JSON format should look like this: '
+                            '{ "model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens" }'
+            ),
+            bypass_cache=True,
+        )
+
+    model_fees = json.loads(result.extracted_content)
+    print(f"Number of models extracted: {len(model_fees)}")
+
+    with open(".data/openai_fees.json", "w", encoding="utf-8") as f:
+        json.dump(model_fees, f, indent=2)
+
+asyncio.run(extract_openai_fees())
+```
+
+## Example 2: Extract Relevant Content
+
+In this example, we instruct the LLM to extract only content related to technology from the NBC News business page.
+
+```python
+import os
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+async def extract_tech_content():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o",
+                api_token=os.getenv('OPENAI_API_KEY'),
+                instruction="Extract only content related to technology"
+            ),
+            bypass_cache=True,
+        )
+
+    tech_content = json.loads(result.extracted_content)
+    print(f"Number of tech-related items extracted: {len(tech_content)}")
+
+    with open(".data/tech_content.json", "w", encoding="utf-8") as f:
+        json.dump(tech_content, f, indent=2)
+
+asyncio.run(extract_tech_content())
+```
+
+## Advanced Usage: Combining JS Execution with LLM Extraction
+
+This example demonstrates how to combine JavaScript execution with LLM extraction to handle dynamic content:
+
+```python
+async def extract_dynamic_content():
+    js_code = """
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+    if (loadMoreButton) {
+        loadMoreButton.click();
+        await new Promise(resolve => setTimeout(resolve, 2000));
+    }
+    """
+
+    wait_for = """
+    () => {
+        const articles = document.querySelectorAll('article.tease-card');
+        return articles.length > 10;
+    }
+    """
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o",
+                api_token=os.getenv('OPENAI_API_KEY'),
+                instruction="Summarize each article, focusing on technology-related content"
+            ),
+            bypass_cache=True,
+        )
+
+    summaries = json.loads(result.extracted_content)
+    print(f"Number of summarized articles: {len(summaries)}")
+
+    with open(".data/tech_summaries.json", "w", encoding="utf-8") as f:
+        json.dump(summaries, f, indent=2)
+
+asyncio.run(extract_dynamic_content())
+```
+
+## Customizing LLM Provider
+
+Crawl4AI uses the `litellm` library under the hood, which allows you to use any LLM provider you want. Just pass the correct model name and API token:
+
+```python
+extraction_strategy=LLMExtractionStrategy(
+    provider="your_llm_provider/model_name",
+    api_token="your_api_token",
+    instruction="Your extraction instruction"
+)
+```
+
+This flexibility allows you to integrate with various LLM providers and tailor the extraction process to your specific needs.
+
+## Error Handling and Retries
+
+When working with external LLM APIs, it's important to handle potential errors and implement retry logic. Here's an example of how you might do this:
+
+```python
+import asyncio
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+class LLMExtractionError(Exception):
+    pass
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+async def extract_with_retry(crawler, url, extraction_strategy):
+    try:
+        result = await crawler.arun(url=url, extraction_strategy=extraction_strategy, bypass_cache=True)
+        return json.loads(result.extracted_content)
+    except Exception as e:
+        raise LLMExtractionError(f"Failed to extract content: {str(e)}")
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        try:
+            content = await extract_with_retry(
+                crawler,
+                "https://www.example.com",
+                LLMExtractionStrategy(
+                    provider="openai/gpt-4o",
+                    api_token=os.getenv('OPENAI_API_KEY'),
+                    instruction="Extract and summarize main points"
+                )
+            )
+            print("Extracted content:", content)
+        except LLMExtractionError as e:
+            print(f"Extraction failed after retries: {e}")
+
+asyncio.run(main())
+```
+
+This example uses the `tenacity` library to implement a retry mechanism with exponential backoff, which can help handle temporary failures or rate limiting from the LLM API.