Delete test.txt

.do/app.yaml

@@ -1,19 +0,0 @@
-alerts:
-- rule: DEPLOYMENT_FAILED
-- rule: DOMAIN_FAILED
-name: crawl4ai
-region: nyc
-services:
-- dockerfile_path: Dockerfile
-  github:
-    branch: 0.3.74
-    deploy_on_push: true
-    repo: unclecode/crawl4ai 
-  health_check:
-    http_path: /health
-  http_port: 11235
-  instance_count: 1
-  instance_size_slug: professional-xs
-  name: web
-  routes:
-  - path: /

.do/deploy.template.yaml

@@ -1,22 +0,0 @@
-spec:
-  name: crawl4ai
-  services:
-    - name: crawl4ai
-      git:
-        branch: 0.3.74
-        repo_clone_url: https://github.com/unclecode/crawl4ai.git
-      dockerfile_path: Dockerfile
-      http_port: 11235
-      instance_count: 1
-      instance_size_slug: professional-xs
-      health_check:
-        http_path: /health
-      envs:
-        - key: INSTALL_TYPE
-          value: "basic"
-        - key: PYTHON_VERSION  
-          value: "3.10"
-        - key: ENABLE_GPU
-          value: "false"
-      routes:
-        - path: /

.gitignore

@@ -199,7 +199,6 @@ test_env/
 **/.DS_Store
 
 todo.md
-todo_executor.md
 git_changes.py
 git_changes.md
 pypi_build.sh
@@ -209,9 +208,4 @@ git_issues.md
 .tests/
 .issues/
 .docs/
-.issues/
-.gitboss/
-todo_executor.md
-protect-all-except-feature.sh
-manage-collab.sh
-publish.sh
+.issues/

CHANGELOG.md

@@ -1,303 +1,6 @@
 # Changelog
 
-## [0.3.743] November 27, 2024
-
-Enhance features and documentation
-- Updated version to 0.3.743
-- Improved ManagedBrowser configuration with dynamic host/port
-- Implemented fast HTML formatting in web crawler
-- Enhanced markdown generation with a new generator class
-- Improved sanitization and utility functions
-- Added contributor details and pull request acknowledgments
-- Updated documentation for clearer usage scenarios
-- Adjusted tests to reflect class name changes
-
-### CONTRIBUTORS.md
-Added new contributors and pull request details.
-Updated community contributions and acknowledged pull requests.
-
-### crawl4ai/__version__.py
-Version update.
-Bumped version to 0.3.743.
-
-### crawl4ai/async_crawler_strategy.py
-Improved ManagedBrowser configuration.
-Enhanced browser initialization with configurable host and debugging port; improved hook execution.
-
-### crawl4ai/async_webcrawler.py
-Optimized HTML processing.
-Implemented 'fast_format_html' for optimized HTML formatting; applied it when 'prettiify' is enabled.
-
-### crawl4ai/content_scraping_strategy.py
-Enhanced markdown generation strategy.
-Updated to use DefaultMarkdownGenerator and improved markdown generation with filters option.
-
-### crawl4ai/markdown_generation_strategy.py
-Refactored markdown generation class.
-Renamed DefaultMarkdownGenerationStrategy to DefaultMarkdownGenerator; added content filter handling.
-
-### crawl4ai/utils.py
-Enhanced utility functions.
-Improved input sanitization and enhanced HTML formatting method.
-
-### docs/md_v2/advanced/hooks-auth.md
-Improved documentation for hooks.
-Updated code examples to include cookies in crawler strategy initialization.
-
-### tests/async/test_markdown_genertor.py
-Refactored tests to match class renaming.
-Updated tests to use renamed DefaultMarkdownGenerator class.
-
-## [0.3.74] November 17, 2024
-
-This changelog details the updates and changes introduced in Crawl4AI version 0.3.74. It's designed to inform developers about new features, modifications to existing components, removals, and other important information.
-
-### 1. File Download Processing
-
-- Users can now specify download folders using the `downloads_path` parameter in the `AsyncWebCrawler` constructor or the `arun` method. If not specified, downloads are saved to a "downloads" folder within the `.crawl4ai` directory.
-- File download tracking is integrated into the `CrawlResult` object.  Successfully downloaded files are listed in the `downloaded_files` attribute, providing their paths.
-- Added `accept_downloads` parameter to the crawler strategies (defaults to `False`). If set to True you can add JS code and `wait_for` parameter for file download.
-
-**Example:**
-
-```python
-import asyncio
-import os
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler
-
-async def download_example():
-    downloads_path = os.path.join(Path.home(), ".crawl4ai", "downloads")
-    os.makedirs(downloads_path, exist_ok=True)
-
-    async with AsyncWebCrawler(
-        accept_downloads=True, 
-        downloads_path=downloads_path, 
-        verbose=True
-    ) as crawler:
-        result = await crawler.arun(
-            url="https://www.python.org/downloads/",
-            js_code="""
-                const downloadLink = document.querySelector('a[href$=".exe"]');
-                if (downloadLink) { downloadLink.click(); }
-            """,
-            wait_for=5 # To ensure download has started
-        )
-
-        if result.downloaded_files:
-            print("Downloaded files:")
-            for file in result.downloaded_files:
-                print(f"- {file}")
-
-asyncio.run(download_example())
-
-```
-
-### 2. Refined Content Filtering
-
-- Introduced the `RelevanceContentFilter` strategy (and its implementation `BM25ContentFilter`) for extracting relevant content from web pages, replacing Fit Markdown and other content cleaning strategy. This new strategy leverages the BM25 algorithm to identify chunks of text relevant to the page's title, description, keywords, or a user-provided query.
-- The `fit_markdown` flag in the content scraper is used to filter content based on title, meta description, and keywords.
-
-**Example:**
-
-```python
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-
-async def filter_content(url, query):
-    async with AsyncWebCrawler() as crawler:
-        content_filter = BM25ContentFilter(user_query=query)
-        result = await crawler.arun(url=url, extraction_strategy=content_filter, fit_markdown=True)
-        print(result.extracted_content)  # Or result.fit_markdown for the markdown version
-        print(result.fit_html) # Or result.fit_html to show HTML with only the filtered content
-
-asyncio.run(filter_content("https://en.wikipedia.org/wiki/Apple", "fruit nutrition health"))
-```
-
-### 3. Raw HTML and Local File Support
-
-- Added support for crawling local files and raw HTML content directly.
-- Use the `file://` prefix for local file paths.
-- Use the `raw:` prefix for raw HTML strings.
-
-**Example:**
-
-```python
-async def crawl_local_or_raw(crawler, content, content_type):
-    prefix = "file://" if content_type == "local" else "raw:"
-    url = f"{prefix}{content}"
-    result = await crawler.arun(url=url)
-    if result.success:
-        print(f"Markdown Content from {content_type.title()} Source:")
-        print(result.markdown)
-
-# Example usage with local file and raw HTML
-async def main():
-    async with AsyncWebCrawler() as crawler:
-        # Local File
-        await crawl_local_or_raw(
-            crawler, os.path.abspath('tests/async/sample_wikipedia.html'), "local"
-        )
-        # Raw HTML
-        await crawl_raw_html(crawler, "<h1>Raw Test</h1><p>This is raw HTML.</p>")
-        
-
-asyncio.run(main())
-```
-
-### 4. Browser Management
-
-- New asynchronous crawler strategy implemented using Playwright.
-- `ManagedBrowser` class introduced for improved browser session handling, offering features like persistent browser sessions between requests (using  `session_id`  parameter) and browser process monitoring.
-- Updated to tf-playwright-stealth for enhanced stealth capabilities.
-- Added `use_managed_browser`, `use_persistent_context`, and `chrome_channel` parameters to AsyncPlaywrightCrawlerStrategy.
-
-
-**Example:**
-```python
-async def browser_management_demo():
-    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "user-data-dir")
-    os.makedirs(user_data_dir, exist_ok=True)  # Ensure directory exists
-    async with AsyncWebCrawler(
-        use_managed_browser=True,
-        user_data_dir=user_data_dir,
-        use_persistent_context=True,
-        verbose=True
-    ) as crawler:
-        result1 = await crawler.arun(
-            url="https://example.com", session_id="my_session"
-        )
-        result2 = await crawler.arun(
-            url="https://example.com/anotherpage", session_id="my_session"
-        )
-
-asyncio.run(browser_management_demo())
-```
-
-
-### 5. API Server & Cache Improvements
-
-- Added CORS support to API server.
-- Implemented static file serving.
-- Enhanced root redirect functionality.
-- Cache database updated to store response headers and downloaded files information. It utilizes a file system approach to manage large content efficiently.
-- New, more efficient caching database built using xxhash and file system approach.
-- Introduced `CacheMode` enum (`ENABLED`, `DISABLED`, `READ_ONLY`, `WRITE_ONLY`, `BYPASS`) and `always_bypass_cache` parameter in AsyncWebCrawler for fine-grained cache control. This replaces `bypass_cache`, `no_cache_read`, `no_cache_write`, and `always_by_pass_cache`.
-
-
-### 🗑️ Removals
-
-- Removed deprecated: `crawl4ai/content_cleaning_strategy.py`.
-- Removed internal class ContentCleaningStrategy
-- Removed legacy cache control flags:  `bypass_cache`,  `disable_cache`,  `no_cache_read`,  `no_cache_write`, and `always_by_pass_cache`.  These have been superseded by  `cache_mode`.
-
-
-### ⚙️ Other Changes
-
-- Moved version file to `crawl4ai/__version__.py`.
-- Added `crawl4ai/cache_context.py`.
-- Added `crawl4ai/version_manager.py`.
-- Added `crawl4ai/migrations.py`.
-- Added `crawl4ai-migrate` entry point.
-- Added config `NEED_MIGRATION` and `SHOW_DEPRECATION_WARNINGS`.
-- API server now requires an API token for authentication, configurable with the `CRAWL4AI_API_TOKEN` environment variable.  This enhances API security.
-- Added synchronous crawl endpoint `/crawl_sync` for immediate result retrieval, and direct crawl endpoint `/crawl_direct` bypassing the task queue.
-
-
-### ⚠️ Deprecation Notices
-
-- The synchronous version of `WebCrawler` is being phased out.  While still available via `crawl4ai[sync]`, it will eventually be removed. Transition to `AsyncWebCrawler` is strongly recommended. Boolean cache control flags in `arun` are also deprecated, migrate to using the `cache_mode` parameter.  See examples in the "New Features" section above for correct usage.
-
-
-### 🐛 Bug Fixes
-
-- Resolved issue with browser context closing unexpectedly in Docker. This significantly improves stability, particularly within containerized environments. 
-- Fixed memory leaks associated with incorrect asynchronous cleanup by removing the `__del__` method and ensuring the browser context is closed explicitly using context managers.
-- Improved error handling in `WebScrapingStrategy`. More detailed error messages and suggestions for debugging will minimize frustration when running into unexpected issues.
-- Fixed issue with incorrect text parsing in specific HTML structures.
-
-
-### Example of migrating to the new CacheMode:
-
-**Old way:**
-
-```python
-crawler = AsyncWebCrawler(always_by_pass_cache=True)
-result = await crawler.arun(url="https://example.com", bypass_cache=True)
-```
-
-**New way:**
-
-```python
-from crawl4ai import CacheMode
-
-crawler = AsyncWebCrawler(always_bypass_cache=True)
-result = await crawler.arun(url="https://example.com", cache_mode=CacheMode.BYPASS)
-```
-
-
-## [0.3.74] - November 13, 2024
-
-1. **File Download Processing** (Nov 14, 2024)
-   - Added capability for users to specify download folders
-   - Implemented file download tracking in crowd result object
-   - Created new file: `tests/async/test_async_doanloader.py`
-
-2. **Content Filtering Improvements** (Nov 14, 2024)
-   - Introduced Relevance Content Filter as an improvement over Fit Markdown
-   - Implemented BM25 algorithm for content relevance matching
-   - Added new file: `crawl4ai/content_filter_strategy.py`
-   - Removed deprecated: `crawl4ai/content_cleaning_strategy.py`
-
-3. **Local File and Raw HTML Support** (Nov 13, 2024)
-   - Added support for processing local files
-   - Implemented raw HTML input handling in AsyncWebCrawler
-   - Enhanced `crawl4ai/async_webcrawler.py` with significant performance improvements
-
-4. **Browser Management Enhancements** (Nov 12, 2024)
-   - Implemented new async crawler strategy using Playwright
-   - Introduced ManagedBrowser for better browser session handling
-   - Added support for persistent browser sessions
-   - Updated from playwright_stealth to tf-playwright-stealth
-
-5. **API Server Component**
-   - Added CORS support
-   - Implemented static file serving
-   - Enhanced root redirect functionality
-
-
-
-## [0.3.731] - November 13, 2024
-
-### Added
-- Support for raw HTML and local file crawling via URL prefixes ('raw:', 'file://')
-- Browser process monitoring for managed browser instances
-- Screenshot capability for raw HTML and local file content
-- Response headers storage in cache database
-- New `fit_markdown` flag for optional markdown generation
-
-### Changed
-- Switched HTML parser from 'html.parser' to 'lxml' for ~4x performance improvement 
-- Optimized BeautifulSoup text conversion and element selection
-- Pre-compiled regular expressions for better performance
-- Improved metadata extraction efficiency
-- Response headers now stored alongside HTML in cache
-
-### Removed
-- `__del__` method from AsyncPlaywrightCrawlerStrategy to prevent async cleanup issues
-
-### Fixed 
-- Issue #256: Added support for crawling raw HTML content
-- Issue #253: Implemented file:// protocol handling
-- Missing response headers in cached results
-- Memory leaks from improper async cleanup
-
-## [v0.3.731] - 2024-11-13 Changelog for Issue 256 Fix
-- Fixed: Browser context unexpectedly closing in Docker environment during crawl operations.
-- Removed: __del__ method from AsyncPlaywrightCrawlerStrategy to prevent unreliable asynchronous cleanup, ensuring - browser context is closed explicitly within context managers.
-- Added: Monitoring for ManagedBrowser subprocess to detect and log unexpected terminations.
-- Updated: Dockerfile configurations to expose debugging port (9222) and allocate additional shared memory for improved browser stability.
-- Improved: Error handling and resource cleanup processes for browser lifecycle management within the Docker environment.
+# CHANGELOG
 
 ## [v0.3.73] - 2024-11-05
 
@@ -367,7 +70,7 @@ result = await crawler.arun(url="https://example.com", cache_mode=CacheMode.BYPA
 - Modified database connection management approach
 - Updated API response structure for better consistency
 
-### Migration Guide
+## Migration Guide
 When upgrading to v0.3.73, be aware of the following changes:
 
 1. Docker Deployment:
@@ -389,7 +92,7 @@ When upgrading to v0.3.73, be aware of the following changes:
    - Follow recommended fixes for any identified problems
 
 
-## [v0.3.73] - 2024-11-04
+## [2024-11-04 - 13:21:42] Comprehensive Update of Crawl4AI Features and Dependencies
 This commit introduces several key enhancements, including improved error handling and robust database operations in `async_database.py`, which now features a connection pool and retry logic for better reliability. Updates to the README.md provide clearer instructions and a better user experience with links to documentation sections. The `.gitignore` file has been refined to include additional directories, while the async web crawler now utilizes a managed browser for more efficient crawling. Furthermore, multiple dependency updates and introduction of the `CustomHTML2Text` class enhance text extraction capabilities.
 
 ## [v0.3.73] - 2024-10-24
@@ -477,7 +180,7 @@ This commit introduces several key enhancements, including improved error handli
 ## [v0.3.72] - 2024-10-20
 
 ### Fixed
-- Added support for parsing Base64 encoded images in WebScrapingStrategy
+- 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
@@ -500,7 +203,7 @@ This commit introduces several key enhancements, including improved error handli
 ### 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 `WebScrapingStrategy` class has been updated to accommodate new external content exclusion options
+- The `WebScrappingStrategy` class has been updated to accommodate new external content exclusion options
 
 ## [v0.3.71] - 2024-10-19
 
@@ -577,7 +280,7 @@ These updates aim to provide more flexibility in text processing, improve perfor
 
 ### Improvements
 1. **Better Error Handling**:
-   - Enhanced error reporting in WebScrapingStrategy with detailed error messages and suggestions.
+   - Enhanced error reporting in WebScrappingStrategy with detailed error messages and suggestions.
    - Added console message and error logging for better debugging.
 
 2. **Image Processing Enhancements**:
@@ -635,43 +338,43 @@ These updates aim to provide more flexibility in text processing, improve perfor
 - 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
+## Improvements and Optimizations
 
-#### 1. AsyncWebCrawler Enhancements
+### 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 WebScrapingStrategy.
+### 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
+### 3. Database Schema Auto-updates
 - Automatic database schema updates ensure compatibility with the latest version.
 
-#### 4. Enhanced Error Handling and Logging
+### 4. Enhanced Error Handling and Logging
 - Improved error messages and logging for easier debugging.
 
-#### 5. Content Extraction Refinements
+### 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
+### 6. Utility Function Enhancements
 - `perform_completion_with_backoff` function now supports additional arguments for more customized API calls to LLM providers.
 
-### Bug Fixes
+## Bug Fixes
 - Fixed an issue where image tags were being prematurely removed during content extraction.
 
-### Examples and Documentation
+## 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
+## 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.

CONTRIBUTORS.md

@@ -10,20 +10,11 @@ We would like to thank the following people for their contributions to Crawl4AI:
 
 ## Community Contributors
 
-- [aadityakanjolia4](https://github.com/aadityakanjolia4) - Fix for `CustomHTML2Text` is not defined.
 - [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
 
-## Pull Requests
-
-- [nelzomal](https://github.com/nelzomal) - Enhance development installation instructions [#286](https://github.com/unclecode/crawl4ai/pull/286)
-- [HamzaFarhan](https://github.com/HamzaFarhan) - Handled the cases where markdown_with_citations, references_markdown, and filtered_html might not be defined [#293](https://github.com/unclecode/crawl4ai/pull/293)
-- [NanmiCoder](https://github.com/NanmiCoder) - fix: crawler strategy exception handling and fixes [#271](https://github.com/unclecode/crawl4ai/pull/271)
-- [paulokuong](https://github.com/paulokuong) - fix: RAWL4_AI_BASE_DIRECTORY should be Path object instead of string [#298](https://github.com/unclecode/crawl4ai/pull/298)
-
-
 ## Other Contributors
 
 - [Gokhan](https://github.com/gkhngyk) 

Dockerfile

@@ -12,7 +12,7 @@ ARG ENABLE_GPU=false
 
 # Platform-specific labels
 LABEL maintainer="unclecode"
-LABEL description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & scraper"
+LABEL description="Crawl4AI - Advanced Web Crawler with AI capabilities"
 LABEL version="1.0"
 
 # Environment setup
@@ -62,13 +62,11 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libatspi2.0-0 \
     && rm -rf /var/lib/apt/lists/*
 
-# GPU support if enabled and architecture is supported
-RUN if [ "$ENABLE_GPU" = "true" ] && [ "$(dpkg --print-architecture)" != "arm64" ] ; then \
-        apt-get update && apt-get install -y --no-install-recommends \
-        nvidia-cuda-toolkit \
-        && rm -rf /var/lib/apt/lists/* ; \
-    else \
-        echo "Skipping NVIDIA CUDA Toolkit installation (unsupported architecture or GPU disabled)"; \
+# GPU support if enabled
+RUN if [ "$ENABLE_GPU" = "true" ] ; then \
+    apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-cuda-toolkit \
+    && rm -rf /var/lib/apt/lists/* ; \
     fi
 
 # Create and set working directory
@@ -98,32 +96,26 @@ RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
 
 # Install the package
 RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
-        pip install ".[all]" && \
+        pip install -e ".[all]" && \
         python -m crawl4ai.model_loader ; \
     elif [ "$INSTALL_TYPE" = "torch" ] ; then \
-        pip install ".[torch]" ; \
+        pip install -e ".[torch]" ; \
     elif [ "$INSTALL_TYPE" = "transformer" ] ; then \
-        pip install ".[transformer]" && \
+        pip install -e ".[transformer]" && \
         python -m crawl4ai.model_loader ; \
     else \
-        pip install "." ; \
+        pip install -e "." ; \
     fi
 
-    # Install MkDocs and required plugins
-RUN pip install --no-cache-dir \
-    mkdocs \
-    mkdocs-material \
-    mkdocs-terminal \
-    pymdown-extensions
-
-# Build MkDocs documentation
-RUN mkdocs build
-
 # Install Playwright and browsers
 RUN playwright install
 
+# Health check
+HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/health || exit 1
+
 # Expose port
-EXPOSE 8000 11235 9222 8080
+EXPOSE 8000
 
 # Start the FastAPI server
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "11235"]

README.md

@@ -1,4 +1,4 @@
-# 🔥🕷️ Crawl4AI: Crawl Smarter, Faster, Freely. For AI.
+# 🔥🕷️ Crawl4AI: LLM Friendly Web Crawler & Scraper
 
 <a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
@@ -9,115 +9,23 @@
 [![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 is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.  
+Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
 
-[✨ Check out latest update v0.3.745](#-recent-updates)
+## 🌟 Meet the Crawl4AI Assistant: Your Copilot for Crawling
 
-## 🧐 Why Crawl4AI?
+Use the [Crawl4AI GPT Assistant](https://tinyurl.com/crawl4ai-gpt) as your AI-powered copilot! With this assistant, you can:
 
-1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.  
-2. **Lightning Fast**: Delivers results 6x faster with real-time, cost-efficient performance.  
-3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.  
-4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.  
-5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.  
-6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
-
-## 🚀 Quick Start 
-
-1. Install Crawl4AI:
-```bash
-pip install crawl4ai
-```
-
-2. Run a simple web crawl:
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
-
-async def main():
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(url="https://www.nbcnews.com/business")
-        # Soone will be change to result.markdown
-        print(result.markdown_v2.raw_markdown) 
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-## ✨ Features 
-
-<details>
-<summary>📝 <strong>Markdown Generation</strong></summary>
-
-- 🧹 **Clean Markdown**: Generates clean, structured Markdown with accurate formatting.
-- 🎯 **Fit Markdown**: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing.
-- 🔗 **Citations and References**: Converts page links into a numbered reference list with clean citations.
-- 🛠️ **Custom Strategies**: Users can create their own Markdown generation strategies tailored to specific needs.
-- 📚 **BM25 Algorithm**: Employs BM25-based filtering for extracting core information and removing irrelevant content. 
-</details>
-
-<details>
-<summary>📊 <strong>Structured Data Extraction</strong></summary>
-
-- 🤖 **LLM-Driven Extraction**: Supports all LLMs (open-source and proprietary) for structured data extraction.
-- 🧱 **Chunking Strategies**: Implements chunking (topic-based, regex, sentence-level) for targeted content processing.
-- 🌌 **Cosine Similarity**: Find relevant content chunks based on user queries for semantic extraction.
-- 🔎 **CSS-Based Extraction**: Fast schema-based data extraction using XPath and CSS selectors.
-- 🔧 **Schema Definition**: Define custom schemas for extracting structured JSON from repetitive patterns.
-
-</details>
-
-<details>
-<summary>🌐 <strong>Browser Integration</strong></summary>
-
-- 🖥️ **Managed Browser**: Use user-owned browsers with full control, avoiding bot detection.
-- 🔄 **Remote Browser Control**: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction.
-- 🔒 **Session Management**: Preserve browser states and reuse them for multi-step crawling.
-- 🧩 **Proxy Support**: Seamlessly connect to proxies with authentication for secure access.
-- ⚙️ **Full Browser Control**: Modify headers, cookies, user agents, and more for tailored crawling setups.
-- 🌍 **Multi-Browser Support**: Compatible with Chromium, Firefox, and WebKit.
-
-</details>
-
-<details>
-<summary>🔎 <strong>Crawling & Scraping</strong></summary>
-
-- 🖼️ **Media Support**: Extract images, audio, videos, and responsive image formats like `srcset` and `picture`.
-- 🚀 **Dynamic Crawling**: Execute JS and wait for async or sync for dynamic content extraction.
-- 📸 **Screenshots**: Capture page screenshots during crawling for debugging or analysis.
-- 📂 **Raw Data Crawling**: Directly process raw HTML (`raw:`) or local files (`file://`).
-- 🔗 **Comprehensive Link Extraction**: Extracts internal, external links, and embedded iframe content.
-- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior.
-- 💾 **Caching**: Cache data for improved speed and to avoid redundant fetches.
-- 📄 **Metadata Extraction**: Retrieve structured metadata from web pages.
-- 📡 **IFrame Content Extraction**: Seamless extraction from embedded iframe content.
-
-</details>
-
-<details>
-<summary>🚀 <strong>Deployment</strong></summary>
-
-- 🐳 **Dockerized Setup**: Optimized Docker image with API server for easy deployment.
-- 🔄 **API Gateway**: One-click deployment with secure token authentication for API-based workflows.
-- 🌐 **Scalable Architecture**: Designed for mass-scale production and optimized server performance.
-- ⚙️ **DigitalOcean Deployment**: Ready-to-deploy configurations for DigitalOcean and similar platforms.
-
-</details>
-
-<details>
-<summary>🎯 <strong>Additional Features</strong></summary>
-
-- 🕶️ **Stealth Mode**: Avoid bot detection by mimicking real users.
-- 🏷️ **Tag-Based Content Extraction**: Refine crawling based on custom tags, headers, or metadata.
-- 🔗 **Link Analysis**: Extract and analyze all links for detailed data exploration.
-- 🛡️ **Error Handling**: Robust error management for seamless execution.
-- 🔐 **CORS & Static Serving**: Supports filesystem-based caching and cross-origin requests.
-- 📖 **Clear Documentation**: Simplified and updated guides for onboarding and advanced usage.
-- 🙌 **Community Recognition**: Acknowledges contributors and pull requests for transparency.
-
-</details>
+- 🧑‍💻 Generate code for complex crawling and extraction tasks
+- 💡 Get tailored support and examples
+- 📘 Learn Crawl4AI faster with step-by-step guidance
 
+## New in 0.3.73 ✨
 
+- 🐳 Docker Ready: Full API server with seamless deployment & scaling
+- 🎯 Browser Takeover: Use your own browser with cookies & history intact (CDP support)
+- 📝 Mockdown+: Enhanced tag preservation & content extraction
+- ⚡️ Parallel Power: Supercharged multi-URL crawling performance
+- 🌟 And many more exciting updates...
 
 ## Try it Now!
 
@@ -157,12 +65,11 @@ if __name__ == "__main__":
 
 Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
 
-<details>
-<summary>🐍 <strong>Using pip</strong></summary>
+### Using pip 🐍
 
 Choose the installation option that best fits your needs:
 
-### Basic Installation
+#### Basic Installation
 
 For basic web crawling and scraping tasks:
 
@@ -172,7 +79,7 @@ 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:
+👉 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:
 
@@ -188,65 +95,29 @@ By default, this will install the asynchronous version of Crawl4AI, using Playwr
 
 This second method has proven to be more reliable in some cases.
 
----
+#### Installation with Synchronous Version
 
-### Installation with Synchronous Version
-
-The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
+If you need the synchronous version using Selenium:
 
 ```bash
 pip install crawl4ai[sync]
 ```
 
----
-
-### Development Installation
+#### Development Installation
 
 For contributors who plan to modify the source code:
 
 ```bash
 git clone https://github.com/unclecode/crawl4ai.git
 cd crawl4ai
-pip install -e .                    # Basic installation in editable mode
+pip install -e .
 ```
 
-Install optional features:
-
-```bash
-pip install -e ".[torch]"           # With PyTorch features
-pip install -e ".[transformer]"     # With Transformer features
-pip install -e ".[cosine]"          # With cosine similarity features
-pip install -e ".[sync]"            # With synchronous crawling (Selenium)
-pip install -e ".[all]"             # Install all optional features
-```
-
-</details>
-
-<details>
-<summary>🚀 <strong>One-Click Deployment</strong></summary>
-
-Deploy your own instance of Crawl4AI with one click:
-
-[![DigitalOcean Referral Badge](https://web-platforms.sfo2.cdn.digitaloceanspaces.com/WWW/Badge%203.svg)](https://www.digitalocean.com/?repo=https://github.com/unclecode/crawl4ai/tree/0.3.74&refcode=a0780f1bdb3d&utm_campaign=Referral_Invite&utm_medium=Referral_Program&utm_source=badge)
-
-> 💡 **Recommended specs**: 4GB RAM minimum. Select "professional-xs" or higher when deploying for stable operation.
-
-The deploy will:
-- Set up a Docker container with Crawl4AI
-- Configure Playwright and all dependencies
-- Start the FastAPI server on port `11235`
-- Set up health checks and auto-deployment
-
-</details>
-
-<details>
-<summary>🐳 <strong>Using Docker</strong></summary>
+### Using Docker 🐳
 
 Crawl4AI is available as Docker images for easy deployment. You can either pull directly from Docker Hub (recommended) or build from the repository.
 
----
-
-### Option 1: Docker Hub (Recommended)
+#### Option 1: Docker Hub (Recommended)
 
 ```bash
 # Pull and run from Docker Hub (choose one):
@@ -256,17 +127,9 @@ docker pull unclecode/crawl4ai:gpu      # GPU-enabled version
 
 # Run the container
 docker run -p 11235:11235 unclecode/crawl4ai:basic  # Replace 'basic' with your chosen version
-
-# In case you want to set platform to arm64
-docker run --platform linux/arm64 -p 11235:11235 unclecode/crawl4ai:basic
-
-# In case to allocate more shared memory for the container
-docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic
 ```
 
----
-
-### Option 2: Build from Repository
+#### Option 2: Build from Repository
 
 ```bash
 # Clone the repository
@@ -278,22 +141,11 @@ docker build -t crawl4ai:local \
   --build-arg INSTALL_TYPE=basic \  # Options: basic, all
   .
 
-# In case you want to set platform to arm64
-docker build -t crawl4ai:local \
-  --build-arg INSTALL_TYPE=basic \  # Options: basic, all
-  --platform linux/arm64 \
-  .
-
 # Run your local build
 docker run -p 11235:11235 crawl4ai:local
 ```
 
----
-
-### Quick Test
-
-Run a quick test (works for both Docker options):
-
+Quick test (works for both options):
 ```python
 import requests
 
@@ -310,134 +162,143 @@ result = requests.get(f"http://localhost:11235/task/{task_id}")
 
 For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://crawl4ai.com/mkdocs/basic/docker-deployment/).
 
-</details>
 
-
-## 🔬 Advanced Usage Examples 🔬
-
-You can check the project structure in the directory [https://github.com/unclecode/crawl4ai/docs/examples](docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
-
-<details>
-<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
+## Quick Start 🚀
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+from crawl4ai import AsyncWebCrawler
 
 async def main():
-    async with AsyncWebCrawler(
-        headless=True,  
-        verbose=True,
-    ) as crawler:
-        result = await crawler.arun(
-            url="https://docs.micronaut.io/4.7.6/guide/",
-            cache_mode=CacheMode.ENABLED,
-            markdown_generator=DefaultMarkdownGenerator(
-                content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
-            ),
-        )
-        print(len(result.markdown))
-        print(len(result.fit_markdown))
-        print(len(result.markdown_v2.fit_markdown))
+    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())
 ```
 
-</details>
+## Advanced Usage 🔬
 
-<details>
-<summary>🖥️ <strong>Executing JavaScript & Extract Structured Data without LLMs</strong></summary>
+### Executing JavaScript and Using CSS Selectors
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
-from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
-import json
+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())
+```
+
+### Using a Proxy
+
+```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": "KidoCode Courses",
-    "baseSelector": "section.charge-methodology .w-tab-content > div",
-    "fields": [
-        {
-            "name": "section_title",
-            "selector": "h3.heading-50",
-            "type": "text",
-        },
-        {
-            "name": "section_description",
-            "selector": ".charge-content",
-            "type": "text",
-        },
-        {
-            "name": "course_name",
-            "selector": ".text-block-93",
-            "type": "text",
-        },
-        {
-            "name": "course_description",
-            "selector": ".course-content-text",
-            "type": "text",
-        },
-        {
-            "name": "course_icon",
-            "selector": ".image-92",
-            "type": "attribute",
-            "attribute": "src"
-        }
-    ]
-}
+        "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(
-        headless=False,
-        verbose=True
-    ) as crawler:
-        
-        # Create the JavaScript that handles clicking multiple times
-        js_click_tabs = """
-        (async () => {
-            const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");
-            
-            for(let tab of tabs) {
-                // scroll to the tab
-                tab.scrollIntoView();
-                tab.click();
-                // Wait for content to load and animations to complete
-                await new Promise(r => setTimeout(r, 500));
-            }
-        })();
-        """     
-
+    async with AsyncWebCrawler(verbose=True) as crawler:
         result = await crawler.arun(
-            url="https://www.kidocode.com/degrees/technology",
-            extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True),
-            js_code=[js_click_tabs],
-            cache_mode=CacheMode.BYPASS
+            url="https://www.nbcnews.com/business",
+            extraction_strategy=extraction_strategy,
+            bypass_cache=True,
         )
 
-        companies = json.loads(result.extracted_content)
-        print(f"Successfully extracted {len(companies)} companies")
-        print(json.dumps(companies[0], indent=2))
+        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(main())
+    asyncio.run(extract_news_teasers())
 ```
 
-</details>
+For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/extraction/css-advanced/) section in the documentation.
 
-<details>
-<summary>📚 <strong>Extracting Structured Data with LLMs</strong></summary>
+### Extracting Structured Data with OpenAI
 
 ```python
 import os
 import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
+from crawl4ai import AsyncWebCrawler
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 from pydantic import BaseModel, Field
 
@@ -452,8 +313,6 @@ async def main():
             url='https://openai.com/api/pricing/',
             word_count_threshold=1,
             extraction_strategy=LLMExtractionStrategy(
-                # Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
-                # provider="ollama/qwen2", api_token="no-token", 
                 provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'), 
                 schema=OpenAIModelFee.schema(),
                 extraction_type="schema",
@@ -461,7 +320,7 @@ async def main():
                 Do not miss any models in the entire content. One extracted model JSON format should look like this: 
                 {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
             ),            
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
         )
         print(result.extracted_content)
 
@@ -469,98 +328,143 @@ if __name__ == "__main__":
     asyncio.run(main())
 ```
 
-</details>
+### Session Management and Dynamic Content Crawling
 
-<details>
-<summary>🤖 <strong>Using You own Browswer with Custome User Profile</strong></summary>
+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
-import os, sys
-from pathlib import Path
-import asyncio, time
+import asyncio
+import re
+from bs4 import BeautifulSoup
 from crawl4ai import AsyncWebCrawler
 
-async def test_news_crawl():
-    # Create a persistent user data directory
-    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
-    os.makedirs(user_data_dir, exist_ok=True)
+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}")
 
-    async with AsyncWebCrawler(
-        verbose=True,
-        headless=True,
-        user_data_dir=user_data_dir,
-        use_persistent_context=True,
-        headers={
-            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
-            "Accept-Language": "en-US,en;q=0.5",
-            "Accept-Encoding": "gzip, deflate, br",
-            "DNT": "1",
-            "Connection": "keep-alive",
-            "Upgrade-Insecure-Requests": "1",
-            "Sec-Fetch-Dest": "document",
-            "Sec-Fetch-Mode": "navigate",
-            "Sec-Fetch-Site": "none",
-            "Sec-Fetch-User": "?1",
-            "Cache-Control": "max-age=0",
-        }
-    ) as crawler:
-        url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
-        
-        result = await crawler.arun(
-            url,
-            cache_mode=CacheMode.BYPASS,
-            magic=True,
-        )
-        
-        print(f"Successfully crawled {url}")
-        print(f"Content length: {len(result.markdown)}")
+    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
+            )
+
+            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())
 ```
 
+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.
+
+For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/tutorial/episode_12_Session-Based_Crawling_for_Dynamic_Websites/) section in the documentation.
 </details>
 
 
-## ✨ Recent Updates   
+## Speed Comparison 🚀
 
-- 🚀 **Improved ManagedBrowser Configuration**: Dynamic host and port support for more flexible browser management.  
-- 📝 **Enhanced Markdown Generation**: New generator class for better formatting and customization.  
-- ⚡ **Fast HTML Formatting**: Significantly optimized HTML formatting in the web crawler.  
-- 🛠️ **Utility & Sanitization Upgrades**: Improved sanitization and expanded utility functions for streamlined workflows.  
-- 👥 **Acknowledgments**: Added contributor details and pull request acknowledgments for better transparency.  
+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.
 
+We've conducted a speed comparison between Crawl4AI and Firecrawl, a paid service. The results demonstrate Crawl4AI's superior performance:
 
-## 📖 Documentation & Roadmap 
+```bash
+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 📚
 
 For detailed documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://crawl4ai.com/mkdocs/).
 
-Moreover to check our development plans and upcoming features, check out our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
+## Crawl4AI Roadmap 🗺️
 
-<details>
-<summary>📈 <strong>Development TODOs</strong></summary>
+For detailed information on our development plans and upcoming features, check out our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
 
+### Advanced Crawling Systems 🔧
 - [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
 - [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
 - [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
 - [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
+
+### Specialized Features 🛠️
 - [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
 - [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
 - [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
+
+### Development Tools 🔨
 - [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
 - [ ] 8. Performance Monitor: Real-time insights into crawler operations
 - [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
+
+### Community & Growth 🌱
 - [ ] 10. Sponsorship Program: Structured support system with tiered benefits
 - [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
 
-</details>
-
-## 🤝 Contributing 
+## 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 
+## License 📄
 
 Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
 
-## 📧 Contact 
+## Contact 📧
 
 For questions, suggestions, or feedback, feel free to reach out:
 
@@ -570,32 +474,32 @@ For questions, suggestions, or feedback, feel free to reach out:
 
 Happy Crawling! 🕸️🚀
 
-## 🗾 Mission
 
-Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.  
+# Mission
 
-We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.
+Our mission is to unlock the untapped potential of personal and enterprise data in the digital age. In today's world, individuals and organizations generate vast amounts of valuable digital footprints, yet this data remains largely uncapitalized as a true asset. 
 
-<details>
-<summary>🔑 <strong>Key Opportunities</strong></summary>
- 
-- **Data Capitalization**: Transform digital footprints into measurable, valuable assets.  
-- **Authentic AI Data**: Provide AI systems with real human insights.  
-- **Shared Economy**: Create a fair data marketplace that benefits data creators.  
+Our open-source solution empowers developers and innovators to build tools for data extraction and structuring, laying the foundation for a new era of data ownership. By transforming personal and enterprise data into structured, tradeable assets, we're creating opportunities for individuals to capitalize on their digital footprints and for organizations to unlock the value of their collective knowledge.
 
-</details>
+This democratization of data represents the first step toward a shared data economy, where willing participation in data sharing drives AI advancement while ensuring the benefits flow back to data creators. Through this approach, we're building a future where AI development is powered by authentic human knowledge rather than synthetic alternatives.
 
-<details>
-<summary>🚀 <strong>Development Pathway</strong></summary>
+![Mission Diagram](./docs/assets/pitch-dark.svg)
 
-1. **Open-Source Tools**: Community-driven platforms for transparent data extraction.  
-2. **Digital Asset Structuring**: Tools to organize and value digital knowledge.  
-3. **Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.  
+For a detailed exploration of our vision, opportunities, and pathway forward, please see our [full mission statement](./MISSION.md).
 
-For more details, see our [full mission statement](./MISSION.md).
-</details>
+## Key Opportunities
 
+- **Data Capitalization**: Transform digital footprints into valuable assets that can appear on personal and enterprise balance sheets
+- **Authentic Data**: Unlock the vast reservoir of real human insights and knowledge for AI advancement
+- **Shared Economy**: Create new value streams where data creators directly benefit from their contributions
 
+## Development Pathway
+
+1. **Open-Source Foundation**: Building transparent, community-driven data extraction tools
+2. **Data Capitalization Platform**: Creating tools to structure and value digital assets
+3. **Shared Data Marketplace**: Establishing an economic platform for ethical data exchange
+
+For a detailed exploration of our vision, challenges, and solutions, please see our [full mission statement](./MISSION.md).
 
 
 ## Star History

crawl4ai/__init__.py

@@ -1,15 +1,13 @@
 # __init__.py
 
-from .async_webcrawler import AsyncWebCrawler, CacheMode
-
+from .async_webcrawler import AsyncWebCrawler
 from .models import CrawlResult
-from .__version__ import __version__
+from ._version import __version__
 # __version__ = "0.3.73"
 
 __all__ = [
     "AsyncWebCrawler",
     "CrawlResult",
-    "CacheMode",
 ]
 
 def is_sync_version_installed():
@@ -28,5 +26,5 @@ if is_sync_version_installed():
         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.")
+    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/__version__.py

@@ -1,2 +0,0 @@
-# crawl4ai/_version.py
-__version__ = "0.3.745"

crawl4ai/_version.py

@@ -0,0 +1,2 @@
+# crawl4ai/_version.py
+__version__ = "0.3.73"

crawl4ai/async_crawler_strategy.py

@@ -14,8 +14,7 @@ from pydantic import BaseModel
 import hashlib
 import json
 import uuid
-from .models import AsyncCrawlResponse
-from .utils import create_box_message
+
 from playwright_stealth import StealthConfig, stealth_async
 
 stealth_config = StealthConfig(
@@ -35,16 +34,13 @@ stealth_config = StealthConfig(
 
 
 class ManagedBrowser:
-    def __init__(self, browser_type: str = "chromium", user_data_dir: Optional[str] = None, headless: bool = False, logger = None, host: str = "localhost", debugging_port: int = 9222):
+    def __init__(self, browser_type: str = "chromium", user_data_dir: Optional[str] = None, headless: bool = False):
         self.browser_type = browser_type
         self.user_data_dir = user_data_dir
         self.headless = headless
         self.browser_process = None
         self.temp_dir = None
-        self.debugging_port = debugging_port
-        self.host = host
-        self.logger = logger
-        self.shutting_down = False
+        self.debugging_port = 9222
 
     async def start(self) -> str:
         """
@@ -68,50 +64,12 @@ class ManagedBrowser:
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE
             )
-            # Monitor browser process output for errors
-            asyncio.create_task(self._monitor_browser_process())
             await asyncio.sleep(2)  # Give browser time to start
-            return f"http://{self.host}:{self.debugging_port}"
+            return f"http://localhost:{self.debugging_port}"
         except Exception as e:
             await self.cleanup()
             raise Exception(f"Failed to start browser: {e}")
 
-    async def _monitor_browser_process(self):
-        """Monitor the browser process for unexpected termination."""
-        if self.browser_process:
-            try:
-                stdout, stderr = await asyncio.gather(
-                    asyncio.to_thread(self.browser_process.stdout.read),
-                    asyncio.to_thread(self.browser_process.stderr.read)
-                )
-                
-                # Check shutting_down flag BEFORE logging anything
-                if self.browser_process.poll() is not None:
-                    if not self.shutting_down:
-                        self.logger.error(
-                            message="Browser process terminated unexpectedly | Code: {code} | STDOUT: {stdout} | STDERR: {stderr}",
-                            tag="ERROR",
-                            params={
-                                "code": self.browser_process.returncode,
-                                "stdout": stdout.decode(),
-                                "stderr": stderr.decode()
-                            }
-                        )                
-                        await self.cleanup()
-                    else:
-                        self.logger.info(
-                            message="Browser process terminated normally | Code: {code}",
-                            tag="INFO",
-                            params={"code": self.browser_process.returncode}
-                        )
-            except Exception as e:
-                if not self.shutting_down:
-                    self.logger.error(
-                        message="Error monitoring browser process: {error}",
-                        tag="ERROR",
-                        params={"error": str(e)}
-                    )
-
     def _get_browser_path(self) -> str:
         """Returns the browser executable path based on OS and browser type"""
         if sys.platform == "darwin":  # macOS
@@ -160,40 +118,30 @@ class ManagedBrowser:
 
     async def cleanup(self):
         """Cleanup browser process and temporary directory"""
-        # Set shutting_down flag BEFORE any termination actions
-        self.shutting_down = True
-        
         if self.browser_process:
             try:
                 self.browser_process.terminate()
-                # Wait for process to end gracefully
-                for _ in range(10):  # 10 attempts, 100ms each
-                    if self.browser_process.poll() is not None:
-                        break
-                    await asyncio.sleep(0.1)
-                
-                # Force kill if still running
+                await asyncio.sleep(1)
                 if self.browser_process.poll() is None:
                     self.browser_process.kill()
-                    await asyncio.sleep(0.1)  # Brief wait for kill to take effect
-                    
             except Exception as e:
-                self.logger.error(
-                    message="Error terminating browser: {error}",
-                    tag="ERROR",
-                    params={"error": str(e)}
-                )
+                print(f"Error terminating browser: {e}")
 
         if self.temp_dir and os.path.exists(self.temp_dir):
             try:
                 shutil.rmtree(self.temp_dir)
             except Exception as e:
-                self.logger.error(
-                    message="Error removing temporary directory: {error}",
-                    tag="ERROR",
-                    params={"error": str(e)}
-                )
+                print(f"Error removing temporary directory: {e}")
 
+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
@@ -217,8 +165,7 @@ class AsyncCrawlerStrategy(ABC):
         pass
 
 class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
-    def __init__(self, use_cached_html=False, js_code=None, logger = None, **kwargs):
-        self.logger = logger
+    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",
@@ -230,7 +177,6 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         self.headless = kwargs.get("headless", True)
         self.browser_type = kwargs.get("browser_type", "chromium")
         self.headers = kwargs.get("headers", {})
-        self.cookies = kwargs.get("cookies", [])
         self.sessions = {}
         self.session_ttl = 1800 
         self.js_code = js_code
@@ -240,8 +186,6 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         self.sleep_on_close = kwargs.get("sleep_on_close", False)
         self.use_managed_browser = kwargs.get("use_managed_browser", False)
         self.user_data_dir = kwargs.get("user_data_dir", None)
-        self.use_persistent_context = kwargs.get("use_persistent_context", False)
-        self.chrome_channel = kwargs.get("chrome_channel", "chrome")
         self.managed_browser = None
         self.default_context = None
         self.hooks = {
@@ -253,14 +197,6 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             'before_return_html': None,
             'before_retrieve_html': None
         }
-        self.extra_args = kwargs.get("extra_args", [])
-        self.accept_downloads = kwargs.get("accept_downloads", False)
-        self.downloads_path = kwargs.get("downloads_path")
-        self._downloaded_files = []  # Track downloaded files for current crawl
-        if self.accept_downloads and not self.downloads_path:
-            self.downloads_path = os.path.join(os.getcwd(), "downloads")
-            os.makedirs(self.downloads_path, exist_ok=True)        
-        
 
     async def __aenter__(self):
         await self.start()
@@ -278,8 +214,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 self.managed_browser = ManagedBrowser(
                     browser_type=self.browser_type,
                     user_data_dir=self.user_data_dir,
-                    headless=self.headless,
-                    logger=self.logger
+                    headless=self.headless
                 )
                 cdp_url = await self.managed_browser.start()
                 self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
@@ -297,90 +232,42 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 # Set up the default context
                 if self.default_context:
                     await self.default_context.set_extra_http_headers(self.headers)
-                    if self.cookies:
-                        await self.default_context.add_cookies(self.cookies)                    
-                    if self.accept_downloads:
-                        await self.default_context.set_default_timeout(60000)
-                        await self.default_context.set_default_navigation_timeout(60000)
-                        self.default_context._impl_obj._options["accept_downloads"] = True
-                        self.default_context._impl_obj._options["downloads_path"] = self.downloads_path
-                        
+                    
                     if self.user_agent:
                         await self.default_context.set_extra_http_headers({
                             "User-Agent": self.user_agent
                         })
             else:
-                # Base browser arguments
                 browser_args = {
                     "headless": self.headless,
                     "args": [
+                        "--disable-gpu",
                         "--no-sandbox",
                         "--disable-dev-shm-usage",
-                        "--no-first-run",
-                        "--no-default-browser-check",
+                        "--disable-blink-features=AutomationControlled",
                         "--disable-infobars",
                         "--window-position=0,0",
                         "--ignore-certificate-errors",
-                        "--ignore-certificate-errors-spki-list"
+                        "--ignore-certificate-errors-spki-list",
+                        # "--headless=new",  # Use the new headless mode
                     ]
                 }
                 
-                # Add channel if specified (try Chrome first)
-                if self.chrome_channel:
-                    browser_args["channel"] = self.chrome_channel
-                
-                # Add extra args if provided
-                if self.extra_args:
-                    browser_args["args"].extend(self.extra_args)
-                    
-                # Add downloads path if downloads are enabled
-                if self.accept_downloads:
-                    browser_args["downloads_path"] = self.downloads_path
-                
                 # 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")
-                    )
+                    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
                     
-                try:
-                    # 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:
-                        if self.use_persistent_context and self.user_data_dir:
-                            self.browser = await self.playwright.chromium.launch_persistent_context(
-                                user_data_dir=self.user_data_dir,
-                                accept_downloads=self.accept_downloads,
-                                downloads_path=self.downloads_path if self.accept_downloads else None,                                
-                                **browser_args
-                            )
-                            self.default_context = self.browser
-                        else:
-                            self.browser = await self.playwright.chromium.launch(**browser_args)
-                                
-                except Exception as e:
-                    # Fallback to chromium if Chrome channel fails
-                    if "chrome" in str(e) and browser_args.get("channel") == "chrome":
-                        browser_args["channel"] = "chromium"
-                        if self.use_persistent_context and self.user_data_dir:
-                            self.browser = await self.playwright.chromium.launch_persistent_context(
-                                user_data_dir=self.user_data_dir,
-                                **browser_args
-                            )
-                            self.default_context = self.browser
-                        else:
-                            self.browser = await self.playwright.chromium.launch(**browser_args)
-                    else:
-                        raise
+                # 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)
 
@@ -398,7 +285,6 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             self.browser = None
             
         if self.managed_browser:
-            await asyncio.sleep(0.5)
             await self.managed_browser.cleanup()
             self.managed_browser = None
             
@@ -406,10 +292,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             await self.playwright.stop()
             self.playwright = None
 
-    # Issue #256: Remove __del__ method to avoid potential issues with async cleanup
-    # def __del__(self):
-    #     if self.browser or self.playwright:
-    #         asyncio.get_event_loop().run_until_complete(self.close())
+    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:
@@ -417,13 +302,13 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         else:
             raise ValueError(f"Invalid hook type: {hook_type}")
 
-    async def execute_hook(self, hook_type: str, *args, **kwargs):
+    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, **kwargs)
+                return await hook(*args)
             else:
-                return hook(*args, **kwargs)
+                return hook(*args)
         return args[0] if args else None
 
     def update_user_agent(self, user_agent: str):
@@ -546,104 +431,21 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                         }}
                     """)
                 else:
-                    # print(f"Warning: Could not access content frame for iframe {i}")
-                    self.logger.warning(
-                        message="Could not access content frame for iframe {index}",
-                        tag="SCRAPE",
-                        params={"index": i}
-                    )                    
+                    print(f"Warning: Could not access content frame for iframe {i}")
             except Exception as e:
-                self.logger.error(
-                    message="Error processing iframe {index}: {error}",
-                    tag="ERROR",
-                    params={"index": i, "error": str(e)}
-                )                
-                # print(f"Error processing iframe {i}: {str(e)}")
+                print(f"Error processing iframe {i}: {str(e)}")
 
         # Return the page object
         return page  
     
     async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
-        """
-        Crawls a given URL or processes raw HTML/local file content based on the URL prefix.
-
-        Args:
-            url (str): The URL to crawl. Supported prefixes:
-                - 'http://' or 'https://': Web URL to crawl.
-                - 'file://': Local file path to process.
-                - 'raw:': Raw HTML content to process.
-            **kwargs: Additional parameters:
-                - 'screenshot' (bool): Whether to take a screenshot.
-                - ... [other existing parameters]
-
-        Returns:
-            AsyncCrawlResponse: The response containing HTML, headers, status code, and optional screenshot.
-        """
-        response_headers = {}
-        status_code = 200  # Default to 200 for local/raw HTML
-        screenshot_requested = kwargs.get('screenshot', False)
-        screenshot_data = None
-
-        if url.startswith(('http://', 'https://')):
-            # Proceed with standard web crawling
-            return await self._crawl_web(url, **kwargs)
-
-        elif url.startswith('file://'):
-            # Process local file
-            local_file_path = url[7:]  # Remove 'file://' prefix
-            if not os.path.exists(local_file_path):
-                raise FileNotFoundError(f"Local file not found: {local_file_path}")
-            with open(local_file_path, 'r', encoding='utf-8') as f:
-                html = f.read()
-            if screenshot_requested:
-                screenshot_data = await self._generate_screenshot_from_html(html)
-            return AsyncCrawlResponse(
-                html=html,
-                response_headers=response_headers,
-                status_code=status_code,
-                screenshot=screenshot_data,
-                get_delayed_content=None
-            )
-
-        elif url.startswith('raw:'):
-            # Process raw HTML content
-            raw_html = url[4:]  # Remove 'raw:' prefix
-            html = raw_html
-            if screenshot_requested:
-                screenshot_data = await self._generate_screenshot_from_html(html)
-            return AsyncCrawlResponse(
-                html=html,
-                response_headers=response_headers,
-                status_code=status_code,
-                screenshot=screenshot_data,
-                get_delayed_content=None
-            )
-        else:
-            raise ValueError("URL must start with 'http://', 'https://', 'file://', or 'raw:'")
-
-
-    async def _crawl_web(self, url: str, **kwargs) -> AsyncCrawlResponse:
-        """
-        Existing web crawling logic remains unchanged.
-
-        Args:
-            url (str): The web URL to crawl.
-            **kwargs: Additional parameters.
-
-        Returns:
-            AsyncCrawlResponse: The response containing HTML, headers, status code, and optional screenshot.
-        """
         response_headers = {}
         status_code = None
         
-        # Reset downloaded files list for new crawl
-        self._downloaded_files = []
-        
         self._cleanup_expired_sessions()
         session_id = kwargs.get("session_id")
         
         # Handle page creation differently for managed browser
-        context = None
         if self.use_managed_browser:
             if session_id:
                 # Reuse existing session if available
@@ -659,41 +461,24 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             if session_id:
                 context, page, _ = self.sessions.get(session_id, (None, None, None))
                 if not context:
-                    if self.use_persistent_context and self.browser_type in ["chrome", "chromium"]:
-                        # In persistent context, browser is the context
-                        context = self.browser
-                        page = await context.new_page()
-                    else:
-                        # Normal context creation for non-persistent or non-Chrome browsers
-                        context = await self.browser.new_context(
-                            user_agent=self.user_agent,
-                            viewport={"width": 1200, "height": 800},
-                            proxy={"server": self.proxy} if self.proxy else None,
-                            java_script_enabled=True,
-                            accept_downloads=self.accept_downloads,
-                            # downloads_path=self.downloads_path if self.accept_downloads else None
-                        )
-                        await context.add_cookies([{"name": "cookiesEnabled", "value": "true", "url": url}])
-                        if self.cookies:
-                            await context.add_cookies(self.cookies)
-                        await context.set_extra_http_headers(self.headers)
-                        page = await context.new_page()
-                    self.sessions[session_id] = (context, page, time.time())
-            else:
-                if self.use_persistent_context and self.browser_type in ["chrome", "chromium"]:
-                    # In persistent context, browser is the context
-                    context = self.browser
-                else:
-                    # Normal context creation
                     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=self.accept_downloads,
+                        accept_downloads=True,
+                        java_script_enabled=True
                     )
-                    if self.cookies:
-                            await context.add_cookies(self.cookies)
+                    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
@@ -727,8 +512,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     """)
                 
                 page = await context.new_page()
-                if kwargs.get("magic", False):
-                    await stealth_async(page, stealth_config)
+                # await stealth_async(page) #, stealth_config)
 
         # Add console message and error logging
         if kwargs.get("log_console", False):
@@ -736,12 +520,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             page.on("pageerror", lambda exc: print(f"Page Error: {exc}"))
         
         try:
-            # Set up download handling if enabled
-            if self.accept_downloads:
-                page.on("download", lambda download: asyncio.create_task(self._handle_download(download)))
-
-            # if self.verbose:
-            #     print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
+            if self.verbose:
+                print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
 
             if self.use_cached_html:
                 cache_file_path = os.path.join(
@@ -762,23 +542,16 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     return response
 
             if not kwargs.get("js_only", False):
-                await self.execute_hook('before_goto', page, context = context)
+                await self.execute_hook('before_goto', page)
                 
-
-                try:
-                    response = await page.goto(
-                        url,
-                        # wait_until=kwargs.get("wait_until", ["domcontentloaded", "networkidle"]),
-                        wait_until=kwargs.get("wait_until", "domcontentloaded"),
-                        timeout=kwargs.get("page_timeout", 60000),
-                    )
-                except Error as e:
-                    raise RuntimeError(f"Failed on navigating ACS-GOTO :\n{str(e)}")
+                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, context = context)
+                await self.execute_hook('after_goto', page)
                 
                 # Get status code and headers
                 status_code = response.status
@@ -840,10 +613,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     for js in js_code:
                         await page.evaluate(js)
                 
-                # await page.wait_for_timeout(100)
-                
+                await page.wait_for_load_state('networkidle')
                 # Check for on execution event
-                await self.execute_hook('on_execution_started', page, context = context)
+                await self.execute_hook('on_execution_started', page)
                 
             if kwargs.get("simulate_user", False) or kwargs.get("magic", False):
                 # Simulate user interactions
@@ -859,9 +631,6 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     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)}")
-            
-            # if not wait_for and js_code:
-            #     await page.wait_for_load_state('networkidle', timeout=5000)
 
             # Update image dimensions
             update_image_dimensions_js = """
@@ -929,7 +698,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             if kwargs.get("process_iframes", False):
                 page = await self.process_iframes(page)
             
-            await self.execute_hook('before_retrieve_html', page, context = context)
+            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:
@@ -940,7 +709,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 await self.remove_overlay_elements(page)
             
             html = await page.content()
-            await self.execute_hook('before_return_html', page, html, context = context)
+            await self.execute_hook('before_return_html', page, html)
             
             # Check if kwargs has screenshot=True then take screenshot
             screenshot_data = None
@@ -951,9 +720,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                     await asyncio.sleep(screenshot_wait_for)
                 screenshot_data = await self.take_screenshot(page)          
 
-            # if self.verbose:
-            #     print(f"[LOG] ✅ Crawled {url} successfully!")
-           
+            if self.verbose:
+                print(f"[LOG] ✅ Crawled {url} successfully!")
+
             if self.use_cached_html:
                 cache_file_path = os.path.join(
                     os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
@@ -978,49 +747,16 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 response_headers=response_headers, 
                 status_code=status_code,
                 screenshot=screenshot_data,
-                get_delayed_content=get_delayed_content,
-                downloaded_files=self._downloaded_files if self._downloaded_files else None
+                get_delayed_content=get_delayed_content
             )
             return response
         except Error as e:
-            raise Error(f"async_crawler_strategy.py:_crawleb(): {str(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 _handle_download(self, download):
-        """Handle file downloads."""
-        try:
-            suggested_filename = download.suggested_filename
-            download_path = os.path.join(self.downloads_path, suggested_filename)
-            
-            self.logger.info(
-                message="Downloading {filename} to {path}",
-                tag="FETCH",
-                params={"filename": suggested_filename, "path": download_path}
-            )
-                
-            start_time = time.perf_counter()
-            await download.save_as(download_path)
-            end_time = time.perf_counter()
-            self._downloaded_files.append(download_path)
-
-            self.logger.success(
-                message="Downloaded {filename} successfully",
-                tag="COMPLETE",
-                params={"filename": suggested_filename, "path": download_path, "duration": f"{end_time - start_time:.2f}s"}
-            )            
-        except Exception as e:
-            self.logger.error(
-                message="Failed to handle download: {error}",
-                tag="ERROR",
-                params={"error": str(e)}
-            )
-            
-            # if self.verbose:
-            #     print(f"[ERROR] Failed to handle download: {str(e)}")
-    
     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)
@@ -1162,36 +898,17 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             await page.evaluate(remove_overlays_js)
             await page.wait_for_timeout(500)  # Wait for any animations to complete
         except Exception as e:
-            self.logger.warning(
-                message="Failed to remove overlay elements: {error}",
-                tag="SCRAPE",
-                params={"error": str(e)}
-            )            
-            # if self.verbose:
-            #     print(f"Warning: Failed to remove overlay elements: {str(e)}")
+            if self.verbose:
+                print(f"Warning: Failed to remove overlay elements: {str(e)}")
 
     async def take_screenshot(self, page: Page) -> str:
-        """
-        Takes a screenshot of the current page.
-        
-        Args:
-            page (Page): The Playwright page instance
-            
-        Returns:
-            str: Base64-encoded screenshot image
-        """
         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)}"
-            self.logger.error(
-                message="Screenshot failed: {error}",
-                tag="ERROR",
-                params={"error": error_message}
-            )
-            
+            print(error_message)
 
             # Generate an error image
             img = Image.new('RGB', (800, 600), color='black')
@@ -1204,41 +921,4 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             return base64.b64encode(buffered.getvalue()).decode('utf-8')
         finally:
             await page.close()
-            
-    async def _generate_screenshot_from_html(self, html: str) -> Optional[str]:
-        """
-        Generates a screenshot from raw HTML content.
-
-        Args:
-            html (str): The HTML content to render and capture.
-
-        Returns:
-            Optional[str]: Base64-encoded screenshot image or an error image if failed.
-        """
-        try:
-            if not self.browser:
-                await self.start()
-            page = await self.browser.new_page()
-            await page.set_content(html, wait_until='networkidle')
-            screenshot = await page.screenshot(full_page=True)
-            await page.close()
-            return base64.b64encode(screenshot).decode('utf-8')
-        except Exception as e:
-            error_message = f"Failed to take screenshot: {str(e)}"
-            # print(error_message)
-            self.logger.error(
-                message="Screenshot failed: {error}",
-                tag="ERROR",
-                params={"error": 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')
 

crawl4ai/async_database.py

@@ -5,89 +5,28 @@ import asyncio
 from typing import Optional, Tuple, Dict
 from contextlib import asynccontextmanager
 import logging
-import json  # Added for serialization/deserialization
-from .utils import ensure_content_dirs, generate_content_hash
-from .models import CrawlResult
-import xxhash
-import aiofiles
-from .config import NEED_MIGRATION
-from .version_manager import VersionManager
-from .async_logger import AsyncLogger
+
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-base_directory = DB_PATH = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
+DB_PATH = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
 os.makedirs(DB_PATH, exist_ok=True)
-DB_PATH = os.path.join(base_directory, "crawl4ai.db")
+DB_PATH = os.path.join(DB_PATH, "crawl4ai.db")
 
 class AsyncDatabaseManager:
     def __init__(self, pool_size: int = 10, max_retries: int = 3):
         self.db_path = DB_PATH
-        self.content_paths = ensure_content_dirs(os.path.dirname(DB_PATH))
         self.pool_size = pool_size
         self.max_retries = max_retries
         self.connection_pool: Dict[int, aiosqlite.Connection] = {}
         self.pool_lock = asyncio.Lock()
-        self.init_lock = asyncio.Lock()
         self.connection_semaphore = asyncio.Semaphore(pool_size)
-        self._initialized = False  
-        self.version_manager = VersionManager()
-        self.logger = AsyncLogger(
-            log_file=os.path.join(base_directory, ".crawl4ai", "crawler_db.log"),
-            verbose=False,
-            tag_width=10
-        )
-        
         
     async def initialize(self):
         """Initialize the database and connection pool"""
-        try:
-            self.logger.info("Initializing database", tag="INIT")
-            # Ensure the database file exists
-            os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
-            
-            # Check if version update is needed
-            needs_update = self.version_manager.needs_update()
-            
-            # Always ensure base table exists
-            await self.ainit_db()
-            
-            # Verify the table exists
-            async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
-                async with db.execute(
-                    "SELECT name FROM sqlite_master WHERE type='table' AND name='crawled_data'"
-                ) as cursor:
-                    result = await cursor.fetchone()
-                    if not result:
-                        raise Exception("crawled_data table was not created")
-            
-            # If version changed or fresh install, run updates
-            if needs_update:
-                self.logger.info("New version detected, running updates", tag="INIT")
-                await self.update_db_schema()
-                from .migrations import run_migration  # Import here to avoid circular imports
-                await run_migration()
-                self.version_manager.update_version()  # Update stored version after successful migration
-                self.logger.success("Version update completed successfully", tag="COMPLETE")
-            else:
-                self.logger.success("Database initialization completed successfully", tag="COMPLETE")
-
-                
-        except Exception as e:
-            self.logger.error(
-                message="Database initialization error: {error}",
-                tag="ERROR",
-                params={"error": str(e)}
-            )
-            self.logger.info(
-                message="Database will be initialized on first use",
-                tag="INIT"
-            )
-                        
-            raise
-
-            
+        await self.ainit_db()
+        
     async def cleanup(self):
         """Cleanup connections when shutting down"""
         async with self.pool_lock:
@@ -98,43 +37,29 @@ class AsyncDatabaseManager:
     @asynccontextmanager
     async def get_connection(self):
         """Connection pool manager"""
-        if not self._initialized:
-            # Use an asyncio.Lock to ensure only one initialization occurs
-            async with self.init_lock:
-                if not self._initialized:
-                    await self.initialize()
-                    self._initialized = True
-
-        await self.connection_semaphore.acquire()
-        task_id = id(asyncio.current_task())
-        try:
-            async with self.pool_lock:
-                if task_id not in self.connection_pool:
-                    conn = await aiosqlite.connect(
-                        self.db_path,
-                        timeout=30.0
-                    )
-                    await conn.execute('PRAGMA journal_mode = WAL')
-                    await conn.execute('PRAGMA busy_timeout = 5000')
-                    self.connection_pool[task_id] = conn
-
-            yield self.connection_pool[task_id]
-
-        except Exception as e:
-            self.logger.error(
-                message="Connection error: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
-            raise
-        finally:
-            async with self.pool_lock:
-                if task_id in self.connection_pool:
-                    await self.connection_pool[task_id].close()
-                    del self.connection_pool[task_id]
-            self.connection_semaphore.release()
-
+        async with self.connection_semaphore:
+            task_id = id(asyncio.current_task())
+            try:
+                async with self.pool_lock:
+                    if task_id not in self.connection_pool:
+                        conn = await aiosqlite.connect(
+                            self.db_path,
+                            timeout=30.0
+                        )
+                        await conn.execute('PRAGMA journal_mode = WAL')
+                        await conn.execute('PRAGMA busy_timeout = 5000')
+                        self.connection_pool[task_id] = conn
+                    
+                yield self.connection_pool[task_id]
+                
+            except Exception as e:
+                logger.error(f"Connection error: {e}")
+                raise
+            finally:
+                async with self.pool_lock:
+                    if task_id in self.connection_pool:
+                        await self.connection_pool[task_id].close()
+                        del self.connection_pool[task_id]
 
     async def execute_with_retry(self, operation, *args):
         """Execute database operations with retry logic"""
@@ -146,21 +71,13 @@ class AsyncDatabaseManager:
                     return result
             except Exception as e:
                 if attempt == self.max_retries - 1:
-                    self.logger.error(
-                        message="Operation failed after {retries} attempts: {error}",
-                        tag="ERROR",
-                        force_verbose=True,
-                        params={
-                            "retries": self.max_retries,
-                            "error": str(e)
-                        }
-                    )                    
+                    logger.error(f"Operation failed after {self.max_retries} attempts: {e}")
                     raise
                 await asyncio.sleep(1 * (attempt + 1))  # Exponential backoff
 
     async def ainit_db(self):
         """Initialize database schema"""
-        async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
+        async def _init(db):
             await db.execute('''
                 CREATE TABLE IF NOT EXISTS crawled_data (
                     url TEXT PRIMARY KEY,
@@ -172,168 +89,71 @@ class AsyncDatabaseManager:
                     media TEXT DEFAULT "{}",
                     links TEXT DEFAULT "{}",
                     metadata TEXT DEFAULT "{}",
-                    screenshot TEXT DEFAULT "",
-                    response_headers TEXT DEFAULT "{}",
-                    downloaded_files TEXT DEFAULT "{}"  -- New column added
+                    screenshot TEXT DEFAULT ""
                 )
             ''')
-            await db.commit()
-
         
+        await self.execute_with_retry(_init)
+        await self.update_db_schema()
 
     async def update_db_schema(self):
         """Update database schema if needed"""
-        async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
+        async def _check_columns(db):
             cursor = await db.execute("PRAGMA table_info(crawled_data)")
             columns = await cursor.fetchall()
-            column_names = [column[1] for column in columns]
-            
-            # List of new columns to add
-            new_columns = ['media', 'links', 'metadata', 'screenshot', 'response_headers', 'downloaded_files']
-            
-            for column in new_columns:
-                if column not in column_names:
-                    await self.aalter_db_add_column(column, db)
-            await db.commit()
+            return [column[1] for column in columns]
 
-    async def aalter_db_add_column(self, new_column: str, db):
+        column_names = await self.execute_with_retry(_check_columns)
+        
+        for column in ['media', '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):
         """Add new column to the database"""
-        if new_column == 'response_headers':
-            await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT "{{}}"')
-        else:
+        async def _alter(db):
             await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
-        self.logger.info(
-            message="Added column '{column}' to the database",
-            tag="INIT",
-            params={"column": new_column}
-        )        
+            logger.info(f"Added column '{new_column}' to the database.")
 
+        await self.execute_with_retry(_alter)
 
-    async def aget_cached_url(self, url: str) -> Optional[CrawlResult]:
-        """Retrieve cached URL data as CrawlResult"""
+    async def aget_cached_url(self, url: str) -> Optional[Tuple[str, str, str, str, str, str, str, bool, str]]:
+        """Retrieve cached URL data"""
         async def _get(db):
             async with db.execute(
-                'SELECT * FROM crawled_data WHERE url = ?', (url,)
+                'SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?',
+                (url,)
             ) as cursor:
-                row = await cursor.fetchone()
-                if not row:
-                    return None
-                    
-                # Get column names
-                columns = [description[0] for description in cursor.description]
-                # Create dict from row data
-                row_dict = dict(zip(columns, row))
-                
-                # Load content from files using stored hashes
-                content_fields = {
-                    'html': row_dict['html'],
-                    'cleaned_html': row_dict['cleaned_html'],
-                    'markdown': row_dict['markdown'],
-                    'extracted_content': row_dict['extracted_content'],
-                    'screenshot': row_dict['screenshot']
-                }
-                
-                for field, hash_value in content_fields.items():
-                    if hash_value:
-                        content = await self._load_content(
-                            hash_value, 
-                            field.split('_')[0]  # Get content type from field name
-                        )
-                        row_dict[field] = content or ""
-                    else:
-                        row_dict[field] = ""
-
-                # Parse JSON fields
-                json_fields = ['media', 'links', 'metadata', 'response_headers']
-                for field in json_fields:
-                    try:
-                        row_dict[field] = json.loads(row_dict[field]) if row_dict[field] else {}
-                    except json.JSONDecodeError:
-                        row_dict[field] = {}
-
-                # Parse downloaded_files
-                try:
-                    row_dict['downloaded_files'] = json.loads(row_dict['downloaded_files']) if row_dict['downloaded_files'] else []
-                except json.JSONDecodeError:
-                    row_dict['downloaded_files'] = []
-
-                # Remove any fields not in CrawlResult model
-                valid_fields = CrawlResult.__annotations__.keys()
-                filtered_dict = {k: v for k, v in row_dict.items() if k in valid_fields}
-                
-                return CrawlResult(**filtered_dict)
+                return await cursor.fetchone()
 
         try:
             return await self.execute_with_retry(_get)
         except Exception as e:
-            self.logger.error(
-                message="Error retrieving cached URL: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
+            logger.error(f"Error retrieving cached URL: {e}")
             return None
 
-    async def acache_url(self, result: CrawlResult):
-        """Cache CrawlResult data"""
-        # Store content files and get hashes
-        content_map = {
-            'html': (result.html, 'html'),
-            'cleaned_html': (result.cleaned_html or "", 'cleaned'),
-            'markdown': (result.markdown or "", 'markdown'),
-            'extracted_content': (result.extracted_content or "", 'extracted'),
-            'screenshot': (result.screenshot or "", 'screenshots')
-        }
-        
-        content_hashes = {}
-        for field, (content, content_type) in content_map.items():
-            content_hashes[field] = await self._store_content(content, content_type)
-
+    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 = ""):
+        """Cache URL data with retry logic"""
         async def _cache(db):
             await db.execute('''
-                INSERT INTO crawled_data (
-                    url, html, cleaned_html, markdown,
-                    extracted_content, success, media, links, metadata,
-                    screenshot, response_headers, downloaded_files
-                )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                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,
-                    response_headers = excluded.response_headers,
-                    downloaded_files = excluded.downloaded_files
-            ''', (
-                result.url,
-                content_hashes['html'],
-                content_hashes['cleaned_html'],
-                content_hashes['markdown'],
-                content_hashes['extracted_content'],
-                result.success,
-                json.dumps(result.media),
-                json.dumps(result.links),
-                json.dumps(result.metadata or {}),
-                content_hashes['screenshot'],
-                json.dumps(result.response_headers or {}),
-                json.dumps(result.downloaded_files or [])
-            ))
+                    media = excluded.media,      
+                    links = excluded.links,    
+                    metadata = excluded.metadata,      
+                    screenshot = excluded.screenshot
+            ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
 
         try:
             await self.execute_with_retry(_cache)
         except Exception as e:
-            self.logger.error(
-                message="Error caching URL: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
-            
+            logger.error(f"Error caching URL: {e}")
 
     async def aget_total_count(self) -> int:
         """Get total number of cached URLs"""
@@ -345,12 +165,7 @@ class AsyncDatabaseManager:
         try:
             return await self.execute_with_retry(_count)
         except Exception as e:
-            self.logger.error(
-                message="Error getting total count: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
+            logger.error(f"Error getting total count: {e}")
             return 0
 
     async def aclear_db(self):
@@ -361,12 +176,7 @@ class AsyncDatabaseManager:
         try:
             await self.execute_with_retry(_clear)
         except Exception as e:
-            self.logger.error(
-                message="Error clearing database: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
+            logger.error(f"Error clearing database: {e}")
 
     async def aflush_db(self):
         """Drop the entire table"""
@@ -376,46 +186,7 @@ class AsyncDatabaseManager:
         try:
             await self.execute_with_retry(_flush)
         except Exception as e:
-            self.logger.error(
-                message="Error flushing database: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)}
-            )
-            
-                
-    async def _store_content(self, content: str, content_type: str) -> str:
-        """Store content in filesystem and return hash"""
-        if not content:
-            return ""
-            
-        content_hash = generate_content_hash(content)
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-        
-        # Only write if file doesn't exist
-        if not os.path.exists(file_path):
-            async with aiofiles.open(file_path, 'w', encoding='utf-8') as f:
-                await f.write(content)
-                
-        return content_hash
-
-    async def _load_content(self, content_hash: str, content_type: str) -> Optional[str]:
-        """Load content from filesystem by hash"""
-        if not content_hash:
-            return None
-            
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-        try:
-            async with aiofiles.open(file_path, 'r', encoding='utf-8') as f:
-                return await f.read()
-        except:
-            self.logger.error(
-                message="Failed to load content: {file_path}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"file_path": file_path}
-            )
-            return None
+            logger.error(f"Error flushing database: {e}")
 
 # Create a singleton instance
-async_db_manager = AsyncDatabaseManager()
+async_db_manager = AsyncDatabaseManager()

crawl4ai/async_logger.py

@@ -1,231 +0,0 @@
-from enum import Enum
-from typing import Optional, Dict, Any, Union
-from colorama import Fore, Back, Style, init
-import time
-import os
-from datetime import datetime
-
-class LogLevel(Enum):
-    DEBUG = 1
-    INFO = 2
-    SUCCESS = 3
-    WARNING = 4
-    ERROR = 5
-
-class AsyncLogger:
-    """
-    Asynchronous logger with support for colored console output and file logging.
-    Supports templated messages with colored components.
-    """
-    
-    DEFAULT_ICONS = {
-        'INIT': '→',
-        'READY': '✓',
-        'FETCH': '↓',
-        'SCRAPE': '◆',
-        'EXTRACT': '■',
-        'COMPLETE': '●',
-        'ERROR': '×',
-        'DEBUG': '⋯',
-        'INFO': 'ℹ',
-        'WARNING': '⚠',
-    }
-
-    DEFAULT_COLORS = {
-        LogLevel.DEBUG: Fore.LIGHTBLACK_EX,
-        LogLevel.INFO: Fore.CYAN,
-        LogLevel.SUCCESS: Fore.GREEN,
-        LogLevel.WARNING: Fore.YELLOW,
-        LogLevel.ERROR: Fore.RED,
-    }
-
-    def __init__(
-        self,
-        log_file: Optional[str] = None,
-        log_level: LogLevel = LogLevel.INFO,
-        tag_width: int = 10,
-        icons: Optional[Dict[str, str]] = None,
-        colors: Optional[Dict[LogLevel, str]] = None,
-        verbose: bool = True
-    ):
-        """
-        Initialize the logger.
-        
-        Args:
-            log_file: Optional file path for logging
-            log_level: Minimum log level to display
-            tag_width: Width for tag formatting
-            icons: Custom icons for different tags
-            colors: Custom colors for different log levels
-            verbose: Whether to output to console
-        """
-        init()  # Initialize colorama
-        self.log_file = log_file
-        self.log_level = log_level
-        self.tag_width = tag_width
-        self.icons = icons or self.DEFAULT_ICONS
-        self.colors = colors or self.DEFAULT_COLORS
-        self.verbose = verbose
-        
-        # Create log file directory if needed
-        if log_file:
-            os.makedirs(os.path.dirname(os.path.abspath(log_file)), exist_ok=True)
-
-    def _format_tag(self, tag: str) -> str:
-        """Format a tag with consistent width."""
-        return f"[{tag}]".ljust(self.tag_width, ".")
-
-    def _get_icon(self, tag: str) -> str:
-        """Get the icon for a tag, defaulting to info icon if not found."""
-        return self.icons.get(tag, self.icons['INFO'])
-
-    def _write_to_file(self, message: str):
-        """Write a message to the log file if configured."""
-        if self.log_file:
-            timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
-            with open(self.log_file, 'a', encoding='utf-8') as f:
-                # Strip ANSI color codes for file output
-                clean_message = message.replace(Fore.RESET, '').replace(Style.RESET_ALL, '')
-                for color in vars(Fore).values():
-                    if isinstance(color, str):
-                        clean_message = clean_message.replace(color, '')
-                f.write(f"[{timestamp}] {clean_message}\n")
-
-    def _log(
-        self,
-        level: LogLevel,
-        message: str,
-        tag: str,
-        params: Optional[Dict[str, Any]] = None,
-        colors: Optional[Dict[str, str]] = None,
-        base_color: Optional[str] = None,
-        **kwargs
-    ):
-        """
-        Core logging method that handles message formatting and output.
-        
-        Args:
-            level: Log level for this message
-            message: Message template string
-            tag: Tag for the message
-            params: Parameters to format into the message
-            colors: Color overrides for specific parameters
-            base_color: Base color for the entire message
-        """
-        if level.value < self.log_level.value:
-            return
-
-        # Format the message with parameters if provided
-        if params:
-            try:
-                # First format the message with raw parameters
-                formatted_message = message.format(**params)
-                
-                # Then apply colors if specified
-                if colors:
-                    for key, color in colors.items():
-                        # Find the formatted value in the message and wrap it with color
-                        if key in params:
-                            value_str = str(params[key])
-                            formatted_message = formatted_message.replace(
-                                value_str, 
-                                f"{color}{value_str}{Style.RESET_ALL}"
-                            )
-                            
-            except KeyError as e:
-                formatted_message = f"LOGGING ERROR: Missing parameter {e} in message template"
-                level = LogLevel.ERROR
-        else:
-            formatted_message = message
-
-        # Construct the full log line
-        color = base_color or self.colors[level]
-        log_line = f"{color}{self._format_tag(tag)} {self._get_icon(tag)} {formatted_message}{Style.RESET_ALL}"
-
-        # Output to console if verbose
-        if self.verbose or kwargs.get("force_verbose", False):
-            print(log_line)
-
-        # Write to file if configured
-        self._write_to_file(log_line)
-
-    def debug(self, message: str, tag: str = "DEBUG", **kwargs):
-        """Log a debug message."""
-        self._log(LogLevel.DEBUG, message, tag, **kwargs)
-
-    def info(self, message: str, tag: str = "INFO", **kwargs):
-        """Log an info message."""
-        self._log(LogLevel.INFO, message, tag, **kwargs)
-
-    def success(self, message: str, tag: str = "SUCCESS", **kwargs):
-        """Log a success message."""
-        self._log(LogLevel.SUCCESS, message, tag, **kwargs)
-
-    def warning(self, message: str, tag: str = "WARNING", **kwargs):
-        """Log a warning message."""
-        self._log(LogLevel.WARNING, message, tag, **kwargs)
-
-    def error(self, message: str, tag: str = "ERROR", **kwargs):
-        """Log an error message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-
-    def url_status(
-        self,
-        url: str,
-        success: bool,
-        timing: float,
-        tag: str = "FETCH",
-        url_length: int = 50
-    ):
-        """
-        Convenience method for logging URL fetch status.
-        
-        Args:
-            url: The URL being processed
-            success: Whether the operation was successful
-            timing: Time taken for the operation
-            tag: Tag for the message
-            url_length: Maximum length for URL in log
-        """
-        self._log(
-            level=LogLevel.SUCCESS if success else LogLevel.ERROR,
-            message="{url:.{url_length}}... | Status: {status} | Time: {timing:.2f}s",
-            tag=tag,
-            params={
-                "url": url,
-                "url_length": url_length,
-                "status": success,
-                "timing": timing
-            },
-            colors={
-                "status": Fore.GREEN if success else Fore.RED,
-                "timing": Fore.YELLOW
-            }
-        )
-
-    def error_status(
-        self,
-        url: str,
-        error: str,
-        tag: str = "ERROR",
-        url_length: int = 50
-    ):
-        """
-        Convenience method for logging error status.
-        
-        Args:
-            url: The URL being processed
-            error: Error message
-            tag: Tag for the message
-            url_length: Maximum length for URL in log
-        """
-        self._log(
-            level=LogLevel.ERROR,
-            message="{url:.{url_length}}... | Error: {error}",
-            tag=tag,
-            params={
-                "url": url,
-                "url_length": url_length,
-                "error": error
-            }
-        )

crawl4ai/async_webcrawler.py

@@ -1,109 +1,36 @@
 import os
 import time
-import warnings
-from enum import Enum
-from colorama import init, Fore, Back, Style
 from pathlib import Path
-from typing import Optional, List, Union
+from typing import Optional
 import json
 import asyncio
-from .models import CrawlResult, MarkdownGenerationResult
+from .models import CrawlResult
 from .async_database import async_db_manager
 from .chunking_strategy import *
-from .content_filter_strategy import *
 from .extraction_strategy import *
 from .async_crawler_strategy import AsyncCrawlerStrategy, AsyncPlaywrightCrawlerStrategy, AsyncCrawlResponse
-from .cache_context import CacheMode, CacheContext, _legacy_to_cache_mode
-from .content_scraping_strategy import WebScrapingStrategy
-from .async_logger import AsyncLogger
-
-from .config import (
-    MIN_WORD_THRESHOLD, 
-    IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
-    URL_LOG_SHORTEN_LENGTH
-)
+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,
-    fast_format_html,
-    create_box_message
+    format_html
 )
-
-from urllib.parse import urlparse
-import random
-from .__version__ import __version__ as crawl4ai_version
-
+from ._version import __version__ as crawl4ai_version
 
 class AsyncWebCrawler:
-    """
-    Asynchronous web crawler with flexible caching capabilities.
-    
-    Migration Guide (from version X.X.X):
-    Old way (deprecated):
-        crawler = AsyncWebCrawler(always_by_pass_cache=True)
-        result = await crawler.arun(
-            url="https://example.com",
-            bypass_cache=True,
-            no_cache_read=True,
-            no_cache_write=False
-        )
-    
-    New way (recommended):
-        crawler = AsyncWebCrawler(always_bypass_cache=True)
-        result = await crawler.arun(
-            url="https://example.com",
-            cache_mode=CacheMode.WRITE_ONLY
-        )
-    
-    To disable deprecation warnings:
-        Pass warning=False to suppress the warning.
-    """
-    _domain_last_hit = {}
-
     def __init__(
         self,
         crawler_strategy: Optional[AsyncCrawlerStrategy] = None,
-        always_bypass_cache: bool = False,
-        always_by_pass_cache: Optional[bool] = None,  # Deprecated parameter
+        always_by_pass_cache: bool = False,
         base_directory: str = str(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home())),
         **kwargs,
     ):
-        """
-        Initialize the AsyncWebCrawler.
-
-        Args:
-            crawler_strategy: Strategy for crawling web pages
-            always_bypass_cache: Whether to always bypass cache (new parameter)
-            always_by_pass_cache: Deprecated, use always_bypass_cache instead
-            base_directory: Base directory for storing cache
-        """  
-        self.verbose = kwargs.get("verbose", False)
-        self.logger = AsyncLogger(
-            log_file=os.path.join(base_directory, ".crawl4ai", "crawler.log"),
-            verbose=self.verbose,
-            tag_width=10
-        )
-        
         self.crawler_strategy = crawler_strategy or AsyncPlaywrightCrawlerStrategy(
-            logger = self.logger,
             **kwargs
         )
-        
-        # Handle deprecated parameter
-        if always_by_pass_cache is not None:
-            if kwargs.get("warning", True):
-                warnings.warn(
-                    "'always_by_pass_cache' is deprecated and will be removed in version X.X.X. "
-                    "Use 'always_bypass_cache' instead. "
-                    "Pass warning=False to suppress this warning.",
-                    DeprecationWarning,
-                    stacklevel=2
-                )
-            self.always_bypass_cache = always_by_pass_cache
-        else:
-            self.always_bypass_cache = always_bypass_cache
-
+        self.always_by_pass_cache = always_by_pass_cache
+        # self.crawl4ai_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", 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)
@@ -119,14 +46,21 @@ class AsyncWebCrawler:
         await self.crawler_strategy.__aexit__(exc_type, exc_val, exc_tb)
 
     async def awarmup(self):
-        """Initialize the crawler with warm-up sequence."""
-        self.logger.info(f"Crawl4AI {crawl4ai_version}", tag="INIT")
-        # if self.verbose:
-        #     print(f"{Fore.CYAN}{self.tag_format('INIT')} {self.log_icons['INIT']} Crawl4AI {crawl4ai_version}{Style.RESET_ALL}")
-        #     print(f"{Fore.CYAN}{self.tag_format('INIT')} {self.log_icons['INIT']} Warming up AsyncWebCrawler{Style.RESET_ALL}")
+        # Print a message for crawl4ai and its version
+        print(f"[LOG] 🚀 Crawl4AI {crawl4ai_version}")
+        if self.verbose:
+            print("[LOG] 🌤️  Warming up the AsyncWebCrawler")
+        # await async_db_manager.ainit_db()
+        await async_db_manager.initialize()
+        await self.arun(
+            url="https://google.com/",
+            word_count_threshold=5,
+            bypass_cache=False,
+            verbose=False,
+        )
         self.ready = True
-        # if self.verbose:
-        #     print(f"{Fore.GREEN}{self.tag_format('READY')} {self.log_icons['READY']} AsyncWebCrawler initialized{Style.RESET_ALL}")
+        if self.verbose:
+            print("[LOG] 🌞 AsyncWebCrawler is ready to crawl")
 
     async def arun(
         self,
@@ -134,82 +68,14 @@ class AsyncWebCrawler:
         word_count_threshold=MIN_WORD_THRESHOLD,
         extraction_strategy: ExtractionStrategy = None,
         chunking_strategy: ChunkingStrategy = RegexChunking(),
-        content_filter: RelevantContentFilter = None,
-        cache_mode: Optional[CacheMode] = None,
-        # Deprecated parameters
         bypass_cache: bool = False,
-        disable_cache: bool = False,
-        no_cache_read: bool = False,
-        no_cache_write: bool = False,
-        # Other parameters
         css_selector: str = None,
         screenshot: bool = False,
         user_agent: str = None,
         verbose=True,
         **kwargs,
     ) -> CrawlResult:
-        """
-        Runs the crawler for a single source: URL (web, local file, or raw HTML).
-
-        Migration from legacy cache parameters:
-            Old way (deprecated):
-                await crawler.arun(url, bypass_cache=True, no_cache_read=True)
-            
-            New way:
-                await crawler.arun(url, cache_mode=CacheMode.BYPASS)
-
-        Args:
-            url: The URL to crawl (http://, https://, file://, or raw:)
-            cache_mode: Cache behavior control (recommended)
-            word_count_threshold: Minimum word count threshold
-            extraction_strategy: Strategy for content extraction
-            chunking_strategy: Strategy for content chunking
-            css_selector: CSS selector for content extraction
-            screenshot: Whether to capture screenshot
-            user_agent: Custom user agent
-            verbose: Enable verbose logging
-            
-            Deprecated Args:
-                bypass_cache: Use cache_mode=CacheMode.BYPASS instead
-                disable_cache: Use cache_mode=CacheMode.DISABLED instead
-                no_cache_read: Use cache_mode=CacheMode.WRITE_ONLY instead
-                no_cache_write: Use cache_mode=CacheMode.READ_ONLY instead
-
-        Returns:
-            CrawlResult: The result of crawling and processing
-        """
         try:
-            # Handle deprecated parameters
-            if any([bypass_cache, disable_cache, no_cache_read, no_cache_write]):
-                if kwargs.get("warning", True):
-                    warnings.warn(
-                        "Cache control boolean flags are deprecated and will be removed in version X.X.X. "
-                        "Use 'cache_mode' parameter instead. Examples:\n"
-                        "- For bypass_cache=True, use cache_mode=CacheMode.BYPASS\n"
-                        "- For disable_cache=True, use cache_mode=CacheMode.DISABLED\n"
-                        "- For no_cache_read=True, use cache_mode=CacheMode.WRITE_ONLY\n"
-                        "- For no_cache_write=True, use cache_mode=CacheMode.READ_ONLY\n"
-                        "Pass warning=False to suppress this warning.",
-                        DeprecationWarning,
-                        stacklevel=2
-                    )
-                
-                # Convert legacy parameters if cache_mode not provided
-                if cache_mode is None:
-                    cache_mode = _legacy_to_cache_mode(
-                        disable_cache=disable_cache,
-                        bypass_cache=bypass_cache,
-                        no_cache_read=no_cache_read,
-                        no_cache_write=no_cache_write
-                    )
-            
-            # Default to ENABLED if no cache mode specified
-            if cache_mode is None:
-                cache_mode = CacheMode.ENABLED
-
-            # Create cache context
-            cache_context = CacheContext(url, cache_mode, self.always_bypass_cache)
-
             extraction_strategy = extraction_strategy or NoExtractionStrategy()
             extraction_strategy.verbose = verbose
             if not isinstance(extraction_strategy, ExtractionStrategy):
@@ -220,126 +86,61 @@ class AsyncWebCrawler:
             word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
 
             async_response: AsyncCrawlResponse = None
-            cached_result = None
+            cached = None
             screenshot_data = None
             extracted_content = None
-            
-            start_time = time.perf_counter()
-            
-            # Try to get cached result if appropriate
-            if cache_context.should_read():
-                cached_result = await async_db_manager.aget_cached_url(url)
-                        
-            if cached_result:
-                html = sanitize_input_encode(cached_result.html)
-                extracted_content = sanitize_input_encode(cached_result.extracted_content or "")
+            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_result.screenshot
+                    screenshot_data = cached[9]
                     if not screenshot_data:
-                        cached_result = None
-                # if verbose:
-                #     print(f"{Fore.BLUE}{self.tag_format('FETCH')} {self.log_icons['FETCH']} Cache hit for {cache_context.display_url} | Status: {Fore.GREEN if bool(html) else Fore.RED}{bool(html)}{Style.RESET_ALL} | Time: {time.perf_counter() - start_time:.2f}s")
-                self.logger.url_status(
-                        url=cache_context.display_url,
-                        success=bool(html),
-                        timing=time.perf_counter() - start_time,
-                        tag="FETCH"
-                    )                    
+                        cached = None
 
-
-            # Fetch fresh content if needed
-            if not cached_result or not html:
-                t1 = time.perf_counter()
-                
+            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
-                )
+                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.perf_counter()
-                self.logger.url_status(
-                    url=cache_context.display_url,
-                    success=bool(html),
-                    timing=t2 - t1,
-                    tag="FETCH"
-                )
-                # if verbose:
-                #     print(f"{Fore.BLUE}{self.tag_format('FETCH')} {self.log_icons['FETCH']} Live fetch for {cache_context.display_url}... | Status: {Fore.GREEN if bool(html) else Fore.RED}{bool(html)}{Style.RESET_ALL} | Time: {t2 - t1:.2f}s")
+                t2 = time.time()
+                if verbose:
+                    print(
+                        f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds"
+                    )
 
-            # Process the HTML content
             crawl_result = await self.aprocess_html(
-                url=url,
-                html=html,
-                extracted_content=extracted_content,
-                word_count_threshold=word_count_threshold,
-                extraction_strategy=extraction_strategy,
-                chunking_strategy=chunking_strategy,
-                content_filter=content_filter,
-                css_selector=css_selector,
-                screenshot=screenshot_data,
-                verbose=verbose,
-                is_cached=bool(cached_result),
+                url,
+                html,
+                extracted_content,
+                word_count_threshold,
+                extraction_strategy,
+                chunking_strategy,
+                css_selector,
+                screenshot_data,
+                verbose,
+                bool(cached),
                 async_response=async_response,
-                is_web_url=cache_context.is_web_url,
-                is_local_file=cache_context.is_local_file,
-                is_raw_html=cache_context.is_raw_html,
+                bypass_cache=bypass_cache,
                 **kwargs,
             )
-            
-            # Set response data
-            if async_response:
-                crawl_result.status_code = async_response.status_code
-                crawl_result.response_headers = async_response.response_headers
-                crawl_result.downloaded_files = async_response.downloaded_files
-            else:
-                crawl_result.status_code = 200
-                crawl_result.response_headers = cached_result.response_headers if cached_result else {}
-
+            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)
-
-            # if verbose:
-            #     print(f"{Fore.GREEN}{self.tag_format('COMPLETE')} {self.log_icons['COMPLETE']} {cache_context.display_url[:URL_LOG_SHORTEN_LENGTH]}... | Status: {Fore.GREEN if crawl_result.success else Fore.RED}{crawl_result.success} | {Fore.YELLOW}Total: {time.perf_counter() - start_time:.2f}s{Style.RESET_ALL}")
-            self.logger.success(
-                    message="{url:.50}... | Status: {status} | Total: {timing}",
-                    tag="COMPLETE",
-                    params={
-                        "url": cache_context.display_url,
-                        "status": crawl_result.success,
-                        "timing": f"{time.perf_counter() - start_time:.2f}s"
-                    },
-                    colors={
-                        "status": Fore.GREEN if crawl_result.success else Fore.RED,
-                        "timing": Fore.YELLOW
-                    }
-                )
-
-            # Update cache if appropriate
-            if cache_context.should_write() and not bool(cached_result):
-                await async_db_manager.acache_url(crawl_result)
-
             return crawl_result
-        
         except Exception as e:
             if not hasattr(e, "msg"):
                 e.msg = str(e)
-            # print(f"{Fore.RED}{self.tag_format('ERROR')} {self.log_icons['ERROR']} Failed to crawl {cache_context.display_url[:URL_LOG_SHORTEN_LENGTH]}... | {e.msg}{Style.RESET_ALL}")
-            
-            self.logger.error_status(
-                url=cache_context.display_url,
-                error=create_box_message(e.msg, type = "error"),
-                tag="ERROR"
-            )            
-            return CrawlResult(
-                url=url, 
-                html="", 
-                success=False, 
-                error_message=e.msg
-            )
+            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,
@@ -347,9 +148,6 @@ class AsyncWebCrawler:
         word_count_threshold=MIN_WORD_THRESHOLD,
         extraction_strategy: ExtractionStrategy = None,
         chunking_strategy: ChunkingStrategy = RegexChunking(),
-        content_filter: RelevantContentFilter = None,
-        cache_mode: Optional[CacheMode] = None,
-        # Deprecated parameters
         bypass_cache: bool = False,
         css_selector: str = None,
         screenshot: bool = False,
@@ -357,102 +155,22 @@ class AsyncWebCrawler:
         verbose=True,
         **kwargs,
     ) -> List[CrawlResult]:
-        """
-        Runs the crawler for multiple URLs concurrently.
-
-        Migration from legacy parameters:
-            Old way (deprecated):
-                results = await crawler.arun_many(urls, bypass_cache=True)
-            
-            New way:
-                results = await crawler.arun_many(urls, cache_mode=CacheMode.BYPASS)
-
-        Args:
-            urls: List of URLs to crawl
-            cache_mode: Cache behavior control (recommended)
-            [other parameters same as arun()]
-
-        Returns:
-            List[CrawlResult]: Results for each URL
-        """
-        if bypass_cache:
-            if kwargs.get("warning", True):
-                warnings.warn(
-                    "'bypass_cache' is deprecated and will be removed in version X.X.X. "
-                    "Use 'cache_mode=CacheMode.BYPASS' instead. "
-                    "Pass warning=False to suppress this warning.",
-                    DeprecationWarning,
-                    stacklevel=2
-                )
-            if cache_mode is None:
-                cache_mode = CacheMode.BYPASS
-
-        semaphore_count = kwargs.get('semaphore_count', 10)
-        semaphore = asyncio.Semaphore(semaphore_count)
-
-        async def crawl_with_semaphore(url):
-            domain = urlparse(url).netloc
-            current_time = time.time()
-            
-            # print(f"{Fore.LIGHTBLACK_EX}{self.tag_format('PARALLEL')} Started task for {url[:50]}...{Style.RESET_ALL}")
-            self.logger.debug(
-                message="Started task for {url:.50}...",
-                tag="PARALLEL",
-                params={"url": url}
-            )            
-            
-            # Get delay settings from kwargs or use defaults
-            mean_delay = kwargs.get('mean_delay', 0.1)  # 0.5 seconds default mean delay
-            max_range = kwargs.get('max_range', 0.3)    # 1 seconds default max additional delay
-            
-            # Check if we need to wait
-            if domain in self._domain_last_hit:
-                time_since_last = current_time - self._domain_last_hit[domain]
-                if time_since_last < mean_delay:
-                    delay = mean_delay + random.uniform(0, max_range)
-                    await asyncio.sleep(delay)
-            
-            # Update last hit time
-            self._domain_last_hit[domain] = current_time    
-                    
-            async with semaphore:
-                return await self.arun(
-                    url,
-                    word_count_threshold=word_count_threshold,
-                    extraction_strategy=extraction_strategy,
-                    chunking_strategy=chunking_strategy,
-                    content_filter=content_filter,
-                    cache_mode=cache_mode,
-                    css_selector=css_selector,
-                    screenshot=screenshot,
-                    user_agent=user_agent,
-                    verbose=verbose,
-                    **kwargs,
-                )
-
-        # Print start message
-        # print(f"{Fore.CYAN}{self.tag_format('INIT')} {self.log_icons['INIT']} Starting concurrent crawling for {len(urls)} URLs...{Style.RESET_ALL}")
-        self.logger.info(
-            message="Starting concurrent crawling for {count} URLs...",
-            tag="INIT",
-            params={"count": len(urls)}
-        )        
-        start_time = time.perf_counter()
-        tasks = [crawl_with_semaphore(url) for url in urls]
-        results = await asyncio.gather(*tasks, return_exceptions=True)
-        end_time = time.perf_counter()
-        # print(f"{Fore.YELLOW}{self.tag_format('COMPLETE')} {self.log_icons['COMPLETE']} Concurrent crawling completed for {len(urls)} URLs | Total time: {end_time - start_time:.2f}s{Style.RESET_ALL}")
-        self.logger.success(
-            message="Concurrent crawling completed for {count} URLs | " + Fore.YELLOW + " Total time: {timing}" + Style.RESET_ALL,
-            tag="COMPLETE",
-            params={
-                "count": len(urls),
-                "timing": f"{end_time - start_time:.2f}s"
-            },
-            colors={"timing": Fore.YELLOW}
-        )        
-        return [result if not isinstance(result, Exception) else str(result) for result in results]
-
+        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,
@@ -462,30 +180,33 @@ class AsyncWebCrawler:
         word_count_threshold: int,
         extraction_strategy: ExtractionStrategy,
         chunking_strategy: ChunkingStrategy,
-        content_filter: RelevantContentFilter,
         css_selector: str,
         screenshot: str,
         verbose: bool,
+        is_cached: bool,
         **kwargs,
     ) -> CrawlResult:
+        t = time.time()
         # Extract content from HTML
         try:
-            _url = url if not kwargs.get("is_raw_html", False) else "Raw HTML"
-            t1 = time.perf_counter()
-            scrapping_strategy = WebScrapingStrategy()
+            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.pop("only_text", False),
-                image_description_min_word_threshold=kwargs.pop(
+                only_text=kwargs.get("only_text", False),
+                image_description_min_word_threshold=kwargs.get(
                     "image_description_min_word_threshold", IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
                 ),
-                content_filter = content_filter,
                 **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}")
@@ -494,8 +215,6 @@ class AsyncWebCrawler:
         except Exception as e:
             raise ValueError(f"Process HTML, Failed to extract content from the website: {url}, error: {str(e)}")
 
-        markdown_v2: MarkdownGenerationResult = result.get("markdown_v2", None)
-        
         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", ""))
@@ -503,21 +222,13 @@ class AsyncWebCrawler:
         media = result.get("media", [])
         links = result.get("links", [])
         metadata = result.get("metadata", {})
-        
-        # if verbose:
-        #     print(f"{Fore.MAGENTA}{self.tag_format('SCRAPE')} {self.log_icons['SCRAPE']} Processed {_url[:URL_LOG_SHORTEN_LENGTH]}...{Style.RESET_ALL} | Time: {int((time.perf_counter() - t1) * 1000)}ms")
-        self.logger.info(
-            message="Processed {url:.50}... | Time: {timing}ms",
-            tag="SCRAPE",
-            params={
-                "url": _url,
-                "timing": int((time.perf_counter() - t1) * 1000)
-            }
-        )
 
+        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__}"
+                )
 
-        if extracted_content is None and extraction_strategy and chunking_strategy and not isinstance(extraction_strategy, NoExtractionStrategy):
-            t1 = time.perf_counter()
             # Check if extraction strategy is type of JsonCssExtractionStrategy
             if isinstance(extraction_strategy, JsonCssExtractionStrategy) or isinstance(extraction_strategy, JsonCssExtractionStrategy):
                 extraction_strategy.verbose = verbose
@@ -527,28 +238,32 @@ class AsyncWebCrawler:
                 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"{Fore.YELLOW}{self.tag_format('EXTRACT')} {self.log_icons['EXTRACT']} Completed for {_url[:URL_LOG_SHORTEN_LENGTH]}...{Style.RESET_ALL} | Time: {time.perf_counter() - t1:.2f}s{Style.RESET_ALL}")
-            self.logger.info(
-                message="Completed for {url:.50}... | Time: {timing}s",
-                tag="EXTRACT",
-                params={
-                    "url": _url,
-                    "timing": time.perf_counter() - t1
-                }
+
+        if verbose:
+            print(
+                f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds."
             )
 
         screenshot = None if not screenshot else screenshot
-        
-        
-        if kwargs.get("prettiify", False):
-            cleaned_html = fast_format_html(cleaned_html)
-        
+
+        if not is_cached or kwargs.get("bypass_cache", False) or self.always_by_pass_cache:
+            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=cleaned_html,
-            markdown_v2=markdown_v2,
+            cleaned_html=format_html(cleaned_html),
             markdown=markdown,
             fit_markdown=fit_markdown,
             fit_html= fit_html,
@@ -562,15 +277,13 @@ class AsyncWebCrawler:
         )
 
     async def aclear_cache(self):
-        """Clear the cache database."""
+        # await async_db_manager.aclear_db()
         await async_db_manager.cleanup()
 
     async def aflush_cache(self):
-        """Flush the cache database."""
         await async_db_manager.aflush_db()
 
     async def aget_cache_size(self):
-        """Get the total number of cached items."""
         return await async_db_manager.aget_total_count()
 
 

crawl4ai/cache_context.py

@@ -1,79 +0,0 @@
-from enum import Enum
-
-
-class CacheMode(Enum):
-    """
-    Defines the caching behavior for web crawling operations.
-    
-    Modes:
-    - ENABLED: Normal caching behavior (read and write)
-    - DISABLED: No caching at all
-    - READ_ONLY: Only read from cache, don't write
-    - WRITE_ONLY: Only write to cache, don't read
-    - BYPASS: Bypass cache for this operation
-    """
-    ENABLED = "enabled"
-    DISABLED = "disabled"
-    READ_ONLY = "read_only"
-    WRITE_ONLY = "write_only"
-    BYPASS = "bypass"
-
-
-class CacheContext:
-    """
-    Encapsulates cache-related decisions and URL handling.
-    
-    This class centralizes all cache-related logic and URL type checking,
-    making the caching behavior more predictable and maintainable.
-    """
-    def __init__(self, url: str, cache_mode: CacheMode, always_bypass: bool = False):
-        self.url = url
-        self.cache_mode = cache_mode
-        self.always_bypass = always_bypass
-        self.is_cacheable = url.startswith(('http://', 'https://', 'file://'))
-        self.is_web_url = url.startswith(('http://', 'https://'))
-        self.is_local_file = url.startswith("file://")
-        self.is_raw_html = url.startswith("raw:")
-        self._url_display = url if not self.is_raw_html else "Raw HTML"
-    
-    def should_read(self) -> bool:
-        """Determines if cache should be read based on context."""
-        if self.always_bypass or not self.is_cacheable:
-            return False
-        return self.cache_mode in [CacheMode.ENABLED, CacheMode.READ_ONLY]
-    
-    def should_write(self) -> bool:
-        """Determines if cache should be written based on context."""
-        if self.always_bypass or not self.is_cacheable:
-            return False
-        return self.cache_mode in [CacheMode.ENABLED, CacheMode.WRITE_ONLY]
-    
-    @property
-    def display_url(self) -> str:
-        """Returns the URL in display format."""
-        return self._url_display
-
-
-def _legacy_to_cache_mode(
-    disable_cache: bool = False,
-    bypass_cache: bool = False,
-    no_cache_read: bool = False,
-    no_cache_write: bool = False
-) -> CacheMode:
-    """
-    Converts legacy cache parameters to the new CacheMode enum.
-    
-    This is an internal function to help transition from the old boolean flags
-    to the new CacheMode system.
-    """
-    if disable_cache:
-        return CacheMode.DISABLED
-    if bypass_cache:
-        return CacheMode.BYPASS
-    if no_cache_read and no_cache_write:
-        return CacheMode.DISABLED
-    if no_cache_read:
-        return CacheMode.WRITE_ONLY
-    if no_cache_write:
-        return CacheMode.READ_ONLY
-    return CacheMode.ENABLED

crawl4ai/config.py

@@ -51,9 +51,3 @@ SOCIAL_MEDIA_DOMAINS = [
 # If image format is in jpg, png or webp
 # If image is in the first half of the total images extracted from the page
 IMAGE_SCORE_THRESHOLD = 2
-
-MAX_METRICS_HISTORY = 1000
-
-NEED_MIGRATION = True
-URL_LOG_SHORTEN_LENGTH = 30
-SHOW_DEPRECATION_WARNINGS = True

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_filter_strategy.py

@@ -1,502 +0,0 @@
-import re
-from bs4 import BeautifulSoup, Tag
-from typing import List, Tuple, Dict
-from rank_bm25 import BM25Okapi
-from time import perf_counter
-from collections import deque
-from bs4 import BeautifulSoup, NavigableString, Tag
-from .utils import clean_tokens
-from abc import ABC, abstractmethod
-
-from snowballstemmer import stemmer
-
-
-# import regex
-# def tokenize_text(text):
-#     # Regular expression to match words or CJK (Chinese, Japanese, Korean) characters
-#     pattern = r'\p{L}+|\p{N}+|[\p{Script=Han}\p{Script=Hiragana}\p{Script=Katakana}ー]|[\p{P}]'
-#     return regex.findall(pattern, text)
-
-# from nltk.stem import PorterStemmer
-# ps = PorterStemmer()
-class RelevantContentFilter(ABC):
-    def __init__(self, user_query: str = None):
-        self.user_query = user_query
-        self.included_tags = {
-            # Primary structure
-            'article', 'main', 'section', 'div', 
-            # List structures
-            'ul', 'ol', 'li', 'dl', 'dt', 'dd',
-            # Text content
-            'p', 'span', 'blockquote', 'pre', 'code',
-            # Headers
-            'h1', 'h2', 'h3', 'h4', 'h5', 'h6',
-            # Tables
-            'table', 'thead', 'tbody', 'tr', 'td', 'th',
-            # Other semantic elements
-            'figure', 'figcaption', 'details', 'summary',
-            # Text formatting
-            'em', 'strong', 'b', 'i', 'mark', 'small',
-            # Rich content
-            'time', 'address', 'cite', 'q'
-        }
-        self.excluded_tags = {
-            'nav', 'footer', 'header', 'aside', 'script',
-            'style', 'form', 'iframe', 'noscript'
-        }
-        self.header_tags = {'h1', 'h2', 'h3', 'h4', 'h5', 'h6'}
-        self.negative_patterns = re.compile(
-            r'nav|footer|header|sidebar|ads|comment|promo|advert|social|share',
-            re.I
-        )
-        self.min_word_count = 2
-        
-    @abstractmethod
-    def filter_content(self, html: str) -> List[str]:
-        """Abstract method to be implemented by specific filtering strategies"""
-        pass
-    
-    def extract_page_query(self, soup: BeautifulSoup, body: Tag) -> str:
-        """Common method to extract page metadata with fallbacks"""
-        if self.user_query:
-            return self.user_query
-
-        query_parts = []
-        
-        # Title
-        try:
-            title = soup.title.string
-            if title:
-                query_parts.append(title)
-        except Exception:
-            pass
-
-        if soup.find('h1'):
-            query_parts.append(soup.find('h1').get_text())
-            
-        # Meta tags
-        temp = ""
-        for meta_name in ['keywords', 'description']:
-            meta = soup.find('meta', attrs={'name': meta_name})
-            if meta and meta.get('content'):
-                query_parts.append(meta['content'])
-                temp += meta['content']
-                
-        # If still empty, grab first significant paragraph
-        if not temp:
-            # Find the first tag P thatits text contains more than 50 characters
-            for p in body.find_all('p'):
-                if len(p.get_text()) > 150:
-                    query_parts.append(p.get_text()[:150])
-                    break        
-                                
-        return ' '.join(filter(None, query_parts))
-
-
-    def extract_text_chunks(self, body: Tag, min_word_threshold: int = None) -> List[Tuple[str, str]]:
-        """
-        Extracts text chunks from a BeautifulSoup body element while preserving order.
-        Returns list of tuples (text, tag_name) for classification.
-        
-        Args:
-            body: BeautifulSoup Tag object representing the body element
-            
-        Returns:
-            List of (text, tag_name) tuples
-        """
-        # Tags to ignore - inline elements that shouldn't break text flow
-        INLINE_TAGS = {
-            'a', 'abbr', 'acronym', 'b', 'bdo', 'big', 'br', 'button', 'cite', 'code',
-            'dfn', 'em', 'i', 'img', 'input', 'kbd', 'label', 'map', 'object', 'q',
-            'samp', 'script', 'select', 'small', 'span', 'strong', 'sub', 'sup',
-            'textarea', 'time', 'tt', 'var'
-        }
-        
-        # Tags that typically contain meaningful headers
-        HEADER_TAGS = {'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'header'}
-        
-        chunks = []
-        current_text = []
-        chunk_index = 0
-    
-        def should_break_chunk(tag: Tag) -> bool:
-            """Determine if a tag should cause a break in the current text chunk"""
-            return (
-                tag.name not in INLINE_TAGS
-                and not (tag.name == 'p' and len(current_text) == 0)
-            )
-        
-        # Use deque for efficient push/pop operations
-        stack = deque([(body, False)])
-        
-        while stack:
-            element, visited = stack.pop()
-            
-            if visited:
-                # End of block element - flush accumulated text
-                if current_text and should_break_chunk(element):
-                    text = ' '.join(''.join(current_text).split())
-                    if text:
-                        tag_type = 'header' if element.name in HEADER_TAGS else 'content'
-                        chunks.append((chunk_index, text, tag_type, element))
-                        chunk_index += 1
-                    current_text = []
-                continue
-                
-            if isinstance(element, NavigableString):
-                if str(element).strip():
-                    current_text.append(str(element).strip())
-                continue
-                
-            # Pre-allocate children to avoid multiple list operations
-            children = list(element.children)
-            if not children:
-                continue
-                
-            # Mark block for revisit after processing children
-            stack.append((element, True))
-            
-            # Add children in reverse order for correct processing
-            for child in reversed(children):
-                if isinstance(child, (Tag, NavigableString)):
-                    stack.append((child, False))
-        
-        # Handle any remaining text
-        if current_text:
-            text = ' '.join(''.join(current_text).split())
-            if text:
-                chunks.append((chunk_index, text, 'content', body))
-        
-        if min_word_threshold:
-            chunks = [chunk for chunk in chunks if len(chunk[1].split()) >= min_word_threshold]
-        
-        return chunks    
-    
-
-    def extract_text_chunks1(self, soup: BeautifulSoup) -> List[Tuple[int, str, Tag]]:
-        """Common method for extracting text chunks"""
-        _text_cache = {}
-        def fast_text(element: Tag) -> str:
-            elem_id = id(element)
-            if elem_id in _text_cache:
-                return _text_cache[elem_id]
-            texts = []
-            for content in element.contents:
-                if isinstance(content, str):
-                    text = content.strip()
-                    if text:
-                        texts.append(text)
-            result = ' '.join(texts)
-            _text_cache[elem_id] = result
-            return result
-        
-        candidates = []
-        index = 0
-        
-        def dfs(element):
-            nonlocal index
-            if isinstance(element, Tag):
-                if element.name in self.included_tags:
-                    if not self.is_excluded(element):
-                        text = fast_text(element)
-                        word_count = len(text.split())
-                        
-                        # Headers pass through with adjusted minimum
-                        if element.name in self.header_tags:
-                            if word_count >= 3:  # Minimal sanity check for headers
-                                candidates.append((index, text, element))
-                                index += 1
-                        # Regular content uses standard minimum
-                        elif word_count >= self.min_word_count:
-                            candidates.append((index, text, element))
-                            index += 1
-                            
-                for child in element.children:
-                    dfs(child)
-
-        dfs(soup.body if soup.body else soup)
-        return candidates
-
-    def is_excluded(self, tag: Tag) -> bool:
-        """Common method for exclusion logic"""
-        if tag.name in self.excluded_tags:
-            return True
-        class_id = ' '.join(filter(None, [
-            ' '.join(tag.get('class', [])),
-            tag.get('id', '')
-        ]))
-        return bool(self.negative_patterns.search(class_id))
-
-    def clean_element(self, tag: Tag) -> str:
-        """Common method for cleaning HTML elements with minimal overhead"""
-        if not tag or not isinstance(tag, Tag):
-            return ""
-            
-        unwanted_tags = {'script', 'style', 'aside', 'form', 'iframe', 'noscript'}
-        unwanted_attrs = {'style', 'onclick', 'onmouseover', 'align', 'bgcolor', 'class', 'id'}
-        
-        # Use string builder pattern for better performance
-        builder = []
-        
-        def render_tag(elem):
-            if not isinstance(elem, Tag):
-                if isinstance(elem, str):
-                    builder.append(elem.strip())
-                return
-                
-            if elem.name in unwanted_tags:
-                return
-                
-            # Start tag
-            builder.append(f'<{elem.name}')
-            
-            # Add cleaned attributes
-            attrs = {k: v for k, v in elem.attrs.items() if k not in unwanted_attrs}
-            for key, value in attrs.items():
-                builder.append(f' {key}="{value}"')
-                
-            builder.append('>')
-            
-            # Process children
-            for child in elem.children:
-                render_tag(child)
-                
-            # Close tag
-            builder.append(f'</{elem.name}>')
-        
-        try:
-            render_tag(tag)
-            return ''.join(builder)
-        except Exception:
-            return str(tag)  # Fallback to original if anything fails
-
-class BM25ContentFilter(RelevantContentFilter):
-    def __init__(self, user_query: str = None, bm25_threshold: float = 1.0, language: str = 'english'):
-        super().__init__(user_query=user_query)
-        self.bm25_threshold = bm25_threshold
-        self.priority_tags = {
-            'h1': 5.0,
-            'h2': 4.0,
-            'h3': 3.0,
-            'title': 4.0,
-            'strong': 2.0,
-            'b': 1.5,
-            'em': 1.5,
-            'blockquote': 2.0,
-            'code': 2.0,
-            'pre': 1.5,
-            'th': 1.5,  # Table headers
-        }
-        self.stemmer = stemmer(language)
-
-    def filter_content(self, html: str, min_word_threshold: int = None) -> List[str]:
-        """Implements content filtering using BM25 algorithm with priority tag handling"""
-        if not html or not isinstance(html, str):
-            return []
-
-        soup = BeautifulSoup(html, 'lxml')
-        
-        # Check if body is present
-        if not soup.body:
-            # Wrap in body tag if missing
-            soup = BeautifulSoup(f'<body>{html}</body>', 'lxml')        
-        body = soup.find('body')
-        
-        query = self.extract_page_query(soup, body)
-        
-        if not query:
-            return []
-            # return [self.clean_element(soup)]
-            
-        candidates = self.extract_text_chunks(body, min_word_threshold)
-
-        if not candidates:
-            return []
-
-        # Tokenize corpus
-        # tokenized_corpus = [chunk.lower().split() for _, chunk, _, _ in candidates]
-        # tokenized_query = query.lower().split()
-                
-        # tokenized_corpus = [[ps.stem(word) for word in chunk.lower().split()] 
-        #                 for _, chunk, _, _ in candidates]
-        # tokenized_query = [ps.stem(word) for word in query.lower().split()]        
-        
-        tokenized_corpus = [[self.stemmer.stemWord(word) for word in chunk.lower().split()] 
-                   for _, chunk, _, _ in candidates]
-        tokenized_query = [self.stemmer.stemWord(word) for word in query.lower().split()]
-
-        # tokenized_corpus = [[self.stemmer.stemWord(word) for word in tokenize_text(chunk.lower())] 
-        #            for _, chunk, _, _ in candidates]
-        # tokenized_query = [self.stemmer.stemWord(word) for word in tokenize_text(query.lower())]
-
-        # Clean from stop words and noise
-        tokenized_corpus = [clean_tokens(tokens) for tokens in tokenized_corpus]
-        tokenized_query = clean_tokens(tokenized_query)
-
-        bm25 = BM25Okapi(tokenized_corpus)
-        scores = bm25.get_scores(tokenized_query)
-
-        # Adjust scores with tag weights
-        adjusted_candidates = []
-        for score, (index, chunk, tag_type, tag) in zip(scores, candidates):
-            tag_weight = self.priority_tags.get(tag.name, 1.0)
-            adjusted_score = score * tag_weight
-            adjusted_candidates.append((adjusted_score, index, chunk, tag))
-
-        # Filter candidates by threshold
-        selected_candidates = [
-            (index, chunk, tag) for adjusted_score, index, chunk, tag in adjusted_candidates
-            if adjusted_score >= self.bm25_threshold
-        ]
-
-        if not selected_candidates:
-            return []
-
-        # Sort selected candidates by original document order
-        selected_candidates.sort(key=lambda x: x[0])
-
-        return [self.clean_element(tag) for _, _, tag in selected_candidates]
-
-
-class HeuristicContentFilter(RelevantContentFilter):
-    def __init__(self):
-        super().__init__()
-        # Weights for different heuristics
-        self.tag_weights = {
-            'article': 10,
-            'main': 8,
-            'section': 5,
-            'div': 3,
-            'p': 2,
-            'pre': 2,
-            'code': 2,
-            'blockquote': 2,
-            'li': 1,
-            'span': 1,
-        }
-        self.max_depth = 5  # Maximum depth from body to consider
-
-    def filter_content(self, html: str) -> List[str]:
-        """Implements heuristic content filtering without relying on a query."""
-        if not html or not isinstance(html, str):
-            return []
-
-        soup = BeautifulSoup(html, 'lxml')
-
-        # Ensure there is a body tag
-        if not soup.body:
-            soup = BeautifulSoup(f'<body>{html}</body>', 'lxml')
-        body = soup.body
-
-        # Extract candidate text chunks
-        candidates = self.extract_text_chunks(body)
-
-        if not candidates:
-            return []
-
-        # Score each candidate
-        scored_candidates = []
-        for index, text, tag_type, tag in candidates:
-            score = self.score_element(tag, text)
-            if score > 0:
-                scored_candidates.append((score, index, text, tag))
-
-        # Sort candidates by score and then by document order
-        scored_candidates.sort(key=lambda x: (-x[0], x[1]))
-
-        # Extract the top candidates (e.g., top 5)
-        top_candidates = scored_candidates[:5]  # Adjust the number as needed
-
-        # Sort the top candidates back to their original document order
-        top_candidates.sort(key=lambda x: x[1])
-
-        # Clean and return the content
-        return [self.clean_element(tag) for _, _, _, tag in top_candidates]
-
-    def score_element(self, tag: Tag, text: str) -> float:
-        """Compute a score for an element based on heuristics."""
-        if not text or not tag:
-            return 0
-
-        # Exclude unwanted tags
-        if self.is_excluded(tag):
-            return 0
-
-        # Text density
-        text_length = len(text.strip())
-        html_length = len(str(tag))
-        text_density = text_length / html_length if html_length > 0 else 0
-
-        # Link density
-        link_text_length = sum(len(a.get_text().strip()) for a in tag.find_all('a'))
-        link_density = link_text_length / text_length if text_length > 0 else 0
-
-        # Tag weight
-        tag_weight = self.tag_weights.get(tag.name, 1)
-
-        # Depth factor (prefer elements closer to the body tag)
-        depth = self.get_depth(tag)
-        depth_weight = max(self.max_depth - depth, 1) / self.max_depth
-
-        # Compute the final score
-        score = (text_density * tag_weight * depth_weight) / (1 + link_density)
-
-        return score
-
-    def get_depth(self, tag: Tag) -> int:
-        """Compute the depth of the tag from the body tag."""
-        depth = 0
-        current = tag
-        while current and current != current.parent and current.name != 'body':
-            current = current.parent
-            depth += 1
-        return depth
-
-    def extract_text_chunks(self, body: Tag) -> List[Tuple[int, str, str, Tag]]:
-        """
-        Extracts text chunks from the body element while preserving order.
-        Returns list of tuples (index, text, tag_type, tag) for scoring.
-        """
-        chunks = []
-        index = 0
-
-        def traverse(element):
-            nonlocal index
-            if isinstance(element, NavigableString):
-                return
-            if not isinstance(element, Tag):
-                return
-            if self.is_excluded(element):
-                return
-            # Only consider included tags
-            if element.name in self.included_tags:
-                text = element.get_text(separator=' ', strip=True)
-                if len(text.split()) >= self.min_word_count:
-                    tag_type = 'header' if element.name in self.header_tags else 'content'
-                    chunks.append((index, text, tag_type, element))
-                    index += 1
-                    # Do not traverse children of this element to prevent duplication
-                    return
-            for child in element.children:
-                traverse(child)
-
-        traverse(body)
-        return chunks
-
-    def is_excluded(self, tag: Tag) -> bool:
-        """Determine if a tag should be excluded based on heuristics."""
-        if tag.name in self.excluded_tags:
-            return True
-        class_id = ' '.join(filter(None, [
-            ' '.join(tag.get('class', [])),
-            tag.get('id', '')
-        ]))
-        if self.negative_patterns.search(class_id):
-            return True
-        # Exclude tags with high link density (e.g., navigation menus)
-        text = tag.get_text(separator=' ', strip=True)
-        link_text_length = sum(len(a.get_text(strip=True)) for a in tag.find_all('a'))
-        text_length = len(text)
-        if text_length > 0 and (link_text_length / text_length) > 0.5:
-            return True
-        return False

crawl4ai/content_scrapping_strategy.py

@@ -1,6 +1,5 @@
-import re  # Point 1: Pre-Compile Regular Expressions
 from abc import ABC, abstractmethod
-from typing import Dict, Any, Optional
+from typing import Dict, Any
 from bs4 import BeautifulSoup
 from concurrent.futures import ThreadPoolExecutor
 import asyncio, requests, re, os
@@ -8,54 +7,105 @@ 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 .content_filter_strategy import RelevantContentFilter, BM25ContentFilter#, HeuristicContentFilter
-from .markdown_generation_strategy import MarkdownGenerationStrategy, DefaultMarkdownGenerator
-from .models import MarkdownGenerationResult
+from .content_cleaning_strategy import ContentCleaningStrategy
+
 from .utils import (
     sanitize_input_encode,
     sanitize_html,
     extract_metadata,
     InvalidCSSSelectorError,
-    CustomHTML2Text,
+    # CustomHTML2Text,
     normalize_url,
-    is_external_url    
+    is_external_url
+    
 )
-from .tools import profile_and_time
 
-# Pre-compile regular expressions for Open Graph and Twitter metadata
-OG_REGEX = re.compile(r'^og:')
-TWITTER_REGEX = re.compile(r'^twitter:')
-DIMENSION_REGEX = re.compile(r"(\d+)(\D*)")
+from .html2text import HTML2Text
+class CustomHTML2Text(HTML2Text):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inside_pre = False
+        self.inside_code = False
+        self.preserve_tags = set()  # Set of tags to preserve
+        self.current_preserved_tag = None
+        self.preserved_content = []
+        self.preserve_depth = 0
+        
+        # Configuration options
+        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
 
-# Function to parse image height/width value and units
-def parse_dimension(dimension):
-    if dimension:
-        # match = re.match(r"(\d+)(\D*)", dimension)
-        match = DIMENSION_REGEX.match(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
+    def update_params(self, **kwargs):
+        """Update parameters and set preserved tags."""
+        for key, value in kwargs.items():
+            if key == 'preserve_tags':
+                self.preserve_tags = set(value)
+            else:
+                setattr(self, key, value)
 
-# 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)
+    def handle_tag(self, tag, attrs, start):
+        # Handle preserved tags
+        if tag in self.preserve_tags:
+            if start:
+                if self.preserve_depth == 0:
+                    self.current_preserved_tag = tag
+                    self.preserved_content = []
+                    # Format opening tag with attributes
+                    attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
+                    self.preserved_content.append(f'<{tag}{attr_str}>')
+                self.preserve_depth += 1
+                return
+            else:
+                self.preserve_depth -= 1
+                if self.preserve_depth == 0:
+                    self.preserved_content.append(f'</{tag}>')
+                    # Output the preserved HTML block with proper spacing
+                    preserved_html = ''.join(self.preserved_content)
+                    self.o('\n' + preserved_html + '\n')
+                    self.current_preserved_tag = None
+                return
+
+        # If we're inside a preserved tag, collect all content
+        if self.preserve_depth > 0:
+            if start:
+                # Format nested tags with attributes
+                attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
+                self.preserved_content.append(f'<{tag}{attr_str}>')
+            else:
+                self.preserved_content.append(f'</{tag}>')
+            return
+
+        # Handle pre tags
+        if tag == 'pre':
+            if start:
+                self.o('```\n')
+                self.inside_pre = True
+            else:
+                self.o('\n```')
+                self.inside_pre = False
+        # elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
+        #     pass
         else:
-            print(f"Failed to retrieve file size for {img_url}")
-            return None
-    except InvalidSchema as e:
-        return None
-    finally:
-        return
+            super().handle_tag(tag, attrs, start)
 
-class ContentScrapingStrategy(ABC):
+    def handle_data(self, data, entity_char=False):
+        """Override handle_data to capture content within preserved tags."""
+        if self.preserve_depth > 0:
+            self.preserved_content.append(data)
+            return
+        super().handle_data(data, entity_char)
+
+class ContentScrappingStrategy(ABC):
     @abstractmethod
     def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
         pass
@@ -64,140 +114,21 @@ class ContentScrapingStrategy(ABC):
     async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
         pass
 
-class WebScrapingStrategy(ContentScrapingStrategy):
-    def __init__(self, logger=None):
-        self.logger = logger
-
-    def _log(self, level, message, tag="SCRAPE", **kwargs):
-        """Helper method to safely use logger."""
-        if self.logger:
-            log_method = getattr(self.logger, level)
-            log_method(message=message, tag=tag, **kwargs)
-                
+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 _generate_markdown_content(self, 
-                                 cleaned_html: str,
-                                 html: str,
-                                 url: str,
-                                 success: bool,
-                                 **kwargs) -> Dict[str, Any]:
-        """Generate markdown content using either new strategy or legacy method.
-        
-        Args:
-            cleaned_html: Sanitized HTML content
-            html: Original HTML content
-            url: Base URL of the page
-            success: Whether scraping was successful
-            **kwargs: Additional options including:
-                - markdown_generator: Optional[MarkdownGenerationStrategy]
-                - html2text: Dict[str, Any] options for HTML2Text
-                - content_filter: Optional[RelevantContentFilter]
-                - fit_markdown: bool
-                - fit_markdown_user_query: Optional[str]
-                - fit_markdown_bm25_threshold: float
-        
-        Returns:
-            Dict containing markdown content in various formats
-        """
-        markdown_generator: Optional[MarkdownGenerationStrategy] = kwargs.get('markdown_generator', DefaultMarkdownGenerator())
-        
-        if markdown_generator:
-            try:
-                if kwargs.get('fit_markdown', False) and not markdown_generator.content_filter:
-                        markdown_generator.content_filter = BM25ContentFilter(
-                            user_query=kwargs.get('fit_markdown_user_query', None),
-                            bm25_threshold=kwargs.get('fit_markdown_bm25_threshold', 1.0)
-                        )
-                
-                markdown_result: MarkdownGenerationResult = markdown_generator.generate_markdown(
-                    cleaned_html=cleaned_html,
-                    base_url=url,
-                    html2text_options=kwargs.get('html2text', {})
-                )
-                
-                help_message = """"""
-                
-                return {
-                    'markdown': markdown_result.raw_markdown,  
-                    'fit_markdown': markdown_result.fit_markdown,
-                    'fit_html': markdown_result.fit_html, 
-                    'markdown_v2': markdown_result
-                }
-            except Exception as e:
-                self._log('error',
-                    message="Error using new markdown generation strategy: {error}",
-                    tag="SCRAPE",
-                    params={"error": str(e)}
-                )
-                markdown_generator = None
-                return {
-                    'markdown': f"Error using new markdown generation strategy: {str(e)}",
-                    'fit_markdown': "Set flag 'fit_markdown' to True to get cleaned HTML content.",
-                    'fit_html': "Set flag 'fit_markdown' to True to get cleaned HTML content.",
-                    'markdown_v2': None                    
-                }
-
-        # Legacy method
-        h = CustomHTML2Text()
-        h.update_params(**kwargs.get('html2text', {}))            
-        markdown = h.handle(cleaned_html)
-        markdown = markdown.replace('    ```', '```')
-        
-        fit_markdown = "Set flag 'fit_markdown' to True to get cleaned HTML content."
-        fit_html = "Set flag 'fit_markdown' to True to get cleaned HTML content."
-        
-        if kwargs.get('content_filter', None) or kwargs.get('fit_markdown', False):
-            content_filter = kwargs.get('content_filter', None)
-            if not content_filter:
-                content_filter = BM25ContentFilter(
-                    user_query=kwargs.get('fit_markdown_user_query', None),
-                    bm25_threshold=kwargs.get('fit_markdown_bm25_threshold', 1.0)
-                )
-            fit_html = content_filter.filter_content(html)
-            fit_html = '\n'.join('<div>{}</div>'.format(s) for s in fit_html)
-            fit_markdown = h.handle(fit_html)
-
-        markdown_v2 = MarkdownGenerationResult(
-            raw_markdown=markdown,
-            markdown_with_citations=markdown,
-            references_markdown=markdown,
-            fit_markdown=fit_markdown
-        )
-        
-        return {
-            'markdown': markdown,
-            'fit_markdown': fit_markdown,
-            'fit_html': fit_html,
-            'markdown_v2' : markdown_v2
-        }
-
-
     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')
-        soup = BeautifulSoup(html, 'lxml')
+        soup = BeautifulSoup(html, 'html.parser')
         body = soup.body
         
-        try:
-            meta = extract_metadata("", soup)
-        except Exception as e:
-            self._log('error', 
-                message="Error extracting metadata: {error}",
-                tag="SCRAPE",
-                params={"error": str(e)}
-            )            
-            # print('Error extracting metadata:', str(e))
-            meta = {}
-        
         
         image_description_min_word_threshold = kwargs.get('image_description_min_word_threshold', IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD)
 
@@ -240,9 +171,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                             return text_content
                 return None
 
-        def process_image_old(img, url, index, total_images):
-                   
-            
+        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', '')
@@ -258,6 +187,32 @@ class WebScrapingStrategy(ContentScrapingStrategy):
 
             #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')
@@ -291,14 +246,14 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                     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 <= kwargs.get('image_score_threshold', IMAGE_SCORE_THRESHOLD):
+            if score <= IMAGE_SCORE_THRESHOLD:
                 return None
-
-            base_result = {
+            return {
                 'src': img.get('src', ''),
                 'data-src': img.get('data-src', ''),
                 'alt': img.get('alt', ''),
@@ -307,113 +262,6 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                 'type': 'image'
             }
 
-            sources = []
-            srcset = img.get('srcset', '')
-            if srcset:
-                sources = parse_srcset(srcset)
-                if sources:
-                    return [dict(base_result, src=source['url'], width=source['width']) 
-                        for source in sources]
-
-            return [base_result]  # Always return a list
-
-        def process_image(img, url, index, total_images):
-            parse_srcset = lambda s: [{'url': u.strip().split()[0], 'width': u.strip().split()[-1].rstrip('w') 
-                          if ' ' in u else None} 
-                         for u in [f"http{p}" for p in s.split("http") if p]]
-            
-            # Constants for checks
-            classes_to_check = frozenset(['button', 'icon', 'logo'])
-            tags_to_check = frozenset(['button', 'input'])
-            
-            # Pre-fetch commonly used attributes
-            style = img.get('style', '')
-            alt = img.get('alt', '')
-            src = img.get('src', '')
-            data_src = img.get('data-src', '')
-            width = img.get('width')
-            height = img.get('height')
-            parent = img.parent
-            parent_classes = parent.get('class', [])
-
-            # Quick validation checks
-            if ('display:none' in style or
-                parent.name in tags_to_check or
-                any(c in cls for c in parent_classes for cls in classes_to_check) or
-                any(c in src for c in classes_to_check) or
-                any(c in alt for c in classes_to_check)):
-                return None
-
-            # Quick score calculation
-            score = 0
-            if width and width.isdigit():
-                width_val = int(width)
-                score += 1 if width_val > 150 else 0
-            if height and height.isdigit():
-                height_val = int(height)
-                score += 1 if height_val > 150 else 0
-            if alt:
-                score += 1
-            score += index/total_images < 0.5
-            
-            image_format = ''
-            if "data:image/" in src:
-                image_format = src.split(',')[0].split(';')[0].split('/')[1].split(';')[0]
-            else:
-                image_format = os.path.splitext(src)[1].lower().strip('.').split('?')[0]
-            
-            if image_format in ('jpg', 'png', 'webp', 'avif'):
-                score += 1
-
-            if score <= kwargs.get('image_score_threshold', IMAGE_SCORE_THRESHOLD):
-                return None
-
-            # Use set for deduplication
-            unique_urls = set()
-            image_variants = []
-            
-            # Generate a unique group ID for this set of variants
-            group_id = index 
-            
-            # Base image info template
-            base_info = {
-                'alt': alt,
-                'desc': find_closest_parent_with_useful_text(img),
-                'score': score,
-                'type': 'image',
-                'group_id': group_id # Group ID for this set of variants
-            }
-
-            # Inline function for adding variants
-            def add_variant(src, width=None):
-                if src and not src.startswith('data:') and src not in unique_urls:
-                    unique_urls.add(src)
-                    image_variants.append({**base_info, 'src': src, 'width': width})
-
-            # Process all sources
-            add_variant(src)
-            add_variant(data_src)
-            
-            # Handle srcset and data-srcset in one pass
-            for attr in ('srcset', 'data-srcset'):
-                if value := img.get(attr):
-                    for source in parse_srcset(value):
-                        add_variant(source['url'], source['width'])
-
-            # Quick picture element check
-            if picture := img.find_parent('picture'):
-                for source in picture.find_all('source'):
-                    if srcset := source.get('srcset'):
-                        for src in parse_srcset(srcset):
-                            add_variant(src['url'], src['width'])
-
-            # Framework-specific attributes in one pass
-            for attr, value in img.attrs.items():
-                if attr.startswith('data-') and ('src' in attr or 'srcset' in attr) and 'http' in value:
-                    add_variant(value)
-
-            return image_variants if image_variants else None
-
         def remove_unwanted_attributes(element, important_attrs, keep_data_attributes=False):
             attrs_to_remove = []
             for attr in element.attrs:
@@ -446,6 +294,7 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                 
                 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'):
@@ -565,12 +414,9 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                 try:
                     remove_unwanted_attributes(element, IMPORTANT_ATTRS, kwargs.get('keep_data_attributes', False))
                 except Exception as e:
-                    # print('Error removing unwanted attributes:', str(e))
-                    self._log('error',
-                        message="Error removing unwanted attributes: {error}",
-                        tag="SCRAPE",
-                        params={"error": str(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):
@@ -591,30 +437,30 @@ class WebScrapingStrategy(ContentScrapingStrategy):
 
                 return keep_element
             except Exception as e:
-                # print('Error processing element:', str(e))
-                self._log('error',
-                    message="Error processing element: {error}",
-                    tag="SCRAPE",
-                    params={"error": str(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')
         
-        # For test we use for loop instead of thread
-        media['images'] = [
-            img for result in (process_image(img, url, i, len(imgs)) 
-                            for i, img in enumerate(imgs))
-            if result is not None
-            for img in result
-        ]
+        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):
@@ -632,9 +478,8 @@ class WebScrapingStrategy(ContentScrapingStrategy):
                 # Replace base64 data with empty string
                 img['src'] = base64_pattern.sub('', src)
                 
-        str_body = ""
         try:
-            str_body = body.encode_contents().decode('utf-8')
+            str(body)
         except Exception as e:
             # Reset body to the original HTML
             success = False
@@ -659,26 +504,35 @@ class WebScrapingStrategy(ContentScrapingStrategy):
             
             # Append the error div to the body
             body.body.append(error_div)
-            str_body = body.encode_contents().decode('utf-8')
             
             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.")
-            self._log('error',
-                message="After processing the crawled HTML and removing irrelevant tags, nothing was left in the page. Check the markdown for further details.",
-                tag="SCRAPE"
-            )
 
-        cleaned_html = str_body.replace('\n\n', '\n').replace('  ', ' ')
 
-        markdown_content = self._generate_markdown_content(
-            cleaned_html=cleaned_html,
-            html=html,
-            url=url,
-            success=success,
-            **kwargs
-        )
-        
+        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_content,
+            'markdown': markdown,
+            'fit_markdown': fit_markdown,
+            'fit_html': fit_html,
             'cleaned_html': cleaned_html,
             'success': success,
             'media': media,

crawl4ai/crawler_strategy.py

@@ -283,7 +283,7 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                 print(f"[LOG] ✅ Crawled {url} successfully!")
             
             return html
-        except InvalidArgumentException as e:
+        except InvalidArgumentException:
             if not hasattr(e, 'msg'):
                 e.msg = sanitize_input_encode(str(e))
             raise InvalidArgumentException(f"Failed to crawl {url}: {e.msg}")

crawl4ai/markdown_generation_strategy.py

@@ -1,124 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional, Dict, Any, Tuple
-from .models import MarkdownGenerationResult
-from .utils import CustomHTML2Text
-from .content_filter_strategy import RelevantContentFilter, BM25ContentFilter
-import re
-from urllib.parse import urljoin
-
-# Pre-compile the regex pattern
-LINK_PATTERN = re.compile(r'!?\[([^\]]+)\]\(([^)]+?)(?:\s+"([^"]*)")?\)')
-
-class MarkdownGenerationStrategy(ABC):
-    """Abstract base class for markdown generation strategies."""
-    def __init__(self, content_filter: Optional[RelevantContentFilter] = None):
-        self.content_filter = content_filter
-    
-    @abstractmethod
-    def generate_markdown(self, 
-                         cleaned_html: str, 
-                         base_url: str = "",
-                         html2text_options: Optional[Dict[str, Any]] = None,
-                         content_filter: Optional[RelevantContentFilter] = None,
-                         citations: bool = True,
-                         **kwargs) -> MarkdownGenerationResult:
-        """Generate markdown from cleaned HTML."""
-        pass
-
-class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
-    """Default implementation of markdown generation strategy."""
-    def __init__(self, content_filter: Optional[RelevantContentFilter] = None):
-        super().__init__(content_filter)
-    
-    def convert_links_to_citations(self, markdown: str, base_url: str = "") -> Tuple[str, str]:
-        link_map = {}
-        url_cache = {}  # Cache for URL joins
-        parts = []
-        last_end = 0
-        counter = 1
-        
-        for match in LINK_PATTERN.finditer(markdown):
-            parts.append(markdown[last_end:match.start()])
-            text, url, title = match.groups()
-            
-            # Use cached URL if available, otherwise compute and cache
-            if base_url and not url.startswith(('http://', 'https://', 'mailto:')):
-                if url not in url_cache:
-                    url_cache[url] = fast_urljoin(base_url, url)
-                url = url_cache[url]
-                
-            if url not in link_map:
-                desc = []
-                if title: desc.append(title)
-                if text and text != title: desc.append(text)
-                link_map[url] = (counter, ": " + " - ".join(desc) if desc else "")
-                counter += 1
-                
-            num = link_map[url][0]
-            parts.append(f"{text}⟨{num}⟩" if not match.group(0).startswith('!') else f"![{text}⟨{num}⟩]")
-            last_end = match.end()
-        
-        parts.append(markdown[last_end:])
-        converted_text = ''.join(parts)
-        
-        # Pre-build reference strings
-        references = ["\n\n## References\n\n"]
-        references.extend(
-            f"⟨{num}⟩ {url}{desc}\n" 
-            for url, (num, desc) in sorted(link_map.items(), key=lambda x: x[1][0])
-        )
-        
-        return converted_text, ''.join(references)
-
-    def generate_markdown(self, 
-                         cleaned_html: str, 
-                         base_url: str = "",
-                         html2text_options: Optional[Dict[str, Any]] = None,
-                         content_filter: Optional[RelevantContentFilter] = None,
-                         citations: bool = True,
-                         **kwargs) -> MarkdownGenerationResult:
-        """Generate markdown with citations from cleaned HTML."""
-        # Initialize HTML2Text with options
-        h = CustomHTML2Text()
-        if html2text_options:
-            h.update_params(**html2text_options)
-
-        # Generate raw markdown
-        raw_markdown = h.handle(cleaned_html)
-        raw_markdown = raw_markdown.replace('    ```', '```')
-
-        # Convert links to citations
-        markdown_with_citations: str = ""
-        references_markdown: str = ""
-        if citations:
-            markdown_with_citations, references_markdown = self.convert_links_to_citations(
-                raw_markdown, base_url
-            )
-
-        # Generate fit markdown if content filter is provided
-        fit_markdown: Optional[str] = ""
-        filtered_html: Optional[str] = ""
-        if content_filter or self.content_filter:
-            content_filter = content_filter or self.content_filter
-            filtered_html = content_filter.filter_content(cleaned_html)
-            filtered_html = '\n'.join('<div>{}</div>'.format(s) for s in filtered_html)
-            fit_markdown = h.handle(filtered_html)
-
-        return MarkdownGenerationResult(
-            raw_markdown=raw_markdown,
-            markdown_with_citations=markdown_with_citations,
-            references_markdown=references_markdown,
-            fit_markdown=fit_markdown,
-            fit_html=filtered_html,
-        )
-
-def fast_urljoin(base: str, url: str) -> str:
-    """Fast URL joining for common cases."""
-    if url.startswith(('http://', 'https://', 'mailto:', '//')):
-        return url
-    if url.startswith('/'):
-        # Handle absolute paths
-        if base.endswith('/'):
-            return base[:-1] + url
-        return base + url
-    return urljoin(base, url)

crawl4ai/migrations.py

@@ -1,152 +0,0 @@
-import os
-import asyncio
-import logging
-from pathlib import Path
-import aiosqlite
-from typing import Optional
-import xxhash
-import aiofiles
-import shutil
-import time
-from datetime import datetime
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-class DatabaseMigration:
-    def __init__(self, db_path: str):
-        self.db_path = db_path
-        self.content_paths = self._ensure_content_dirs(os.path.dirname(db_path))
-        
-    def _ensure_content_dirs(self, base_path: str) -> dict:
-        dirs = {
-            'html': 'html_content',
-            'cleaned': 'cleaned_html',
-            'markdown': 'markdown_content', 
-            'extracted': 'extracted_content',
-            'screenshots': 'screenshots'
-        }
-        content_paths = {}
-        for key, dirname in dirs.items():
-            path = os.path.join(base_path, dirname)
-            os.makedirs(path, exist_ok=True)
-            content_paths[key] = path
-        return content_paths
-
-    def _generate_content_hash(self, content: str) -> str:
-        x = xxhash.xxh64()
-        x.update(content.encode())
-        content_hash = x.hexdigest()
-        return content_hash
-        # return hashlib.sha256(content.encode()).hexdigest()
-
-    async def _store_content(self, content: str, content_type: str) -> str:
-        if not content:
-            return ""
-        
-        content_hash = self._generate_content_hash(content)
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-        
-        if not os.path.exists(file_path):
-            async with aiofiles.open(file_path, 'w', encoding='utf-8') as f:
-                await f.write(content)
-                
-        return content_hash
-
-    async def migrate_database(self):
-        """Migrate existing database to file-based storage"""
-        logger.info("Starting database migration...")
-        
-        try:
-            async with aiosqlite.connect(self.db_path) as db:
-                # Get all rows
-                async with db.execute(
-                    '''SELECT url, html, cleaned_html, markdown, 
-                       extracted_content, screenshot FROM crawled_data'''
-                ) as cursor:
-                    rows = await cursor.fetchall()
-
-                migrated_count = 0
-                for row in rows:
-                    url, html, cleaned_html, markdown, extracted_content, screenshot = row
-                    
-                    # Store content in files and get hashes
-                    html_hash = await self._store_content(html, 'html')
-                    cleaned_hash = await self._store_content(cleaned_html, 'cleaned')
-                    markdown_hash = await self._store_content(markdown, 'markdown')
-                    extracted_hash = await self._store_content(extracted_content, 'extracted')
-                    screenshot_hash = await self._store_content(screenshot, 'screenshots')
-
-                    # Update database with hashes
-                    await db.execute('''
-                        UPDATE crawled_data 
-                        SET html = ?, 
-                            cleaned_html = ?,
-                            markdown = ?,
-                            extracted_content = ?,
-                            screenshot = ?
-                        WHERE url = ?
-                    ''', (html_hash, cleaned_hash, markdown_hash, 
-                         extracted_hash, screenshot_hash, url))
-                    
-                    migrated_count += 1
-                    if migrated_count % 100 == 0:
-                        logger.info(f"Migrated {migrated_count} records...")
-
-                await db.commit()
-                logger.info(f"Migration completed. {migrated_count} records processed.")
-
-        except Exception as e:
-            logger.error(f"Migration failed: {e}")
-            raise
-
-async def backup_database(db_path: str) -> str:
-    """Create backup of existing database"""
-    if not os.path.exists(db_path):
-        logger.info("No existing database found. Skipping backup.")
-        return None
-        
-    # Create backup with timestamp
-    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
-    backup_path = f"{db_path}.backup_{timestamp}"
-    
-    try:
-        # Wait for any potential write operations to finish
-        await asyncio.sleep(1)
-        
-        # Create backup
-        shutil.copy2(db_path, backup_path)
-        logger.info(f"Database backup created at: {backup_path}")
-        return backup_path
-    except Exception as e:
-        logger.error(f"Backup failed: {e}")
-        raise
-    
-async def run_migration(db_path: Optional[str] = None):
-    """Run database migration"""
-    if db_path is None:
-        db_path = os.path.join(Path.home(), ".crawl4ai", "crawl4ai.db")
-    
-    if not os.path.exists(db_path):
-        logger.info("No existing database found. Skipping migration.")
-        return
-        
-    # Create backup first
-    backup_path = await backup_database(db_path)
-    if not backup_path:
-        return
-    
-    migration = DatabaseMigration(db_path)
-    await migration.migrate_database()
-    
-def main():
-    """CLI entry point for migration"""
-    import argparse
-    parser = argparse.ArgumentParser(description='Migrate Crawl4AI database to file-based storage')
-    parser.add_argument('--db-path', help='Custom database path')
-    args = parser.parse_args()
-    
-    asyncio.run(run_migration(args.db_path))
-
-if __name__ == "__main__":
-    main()

crawl4ai/models.py

@@ -1,19 +1,10 @@
 from pydantic import BaseModel, HttpUrl
-from typing import List, Dict, Optional, Callable, Awaitable, Union
-
-
+from typing import List, Dict, Optional
 
 class UrlModel(BaseModel):
     url: HttpUrl
     forced: bool = False
 
-class MarkdownGenerationResult(BaseModel):
-    raw_markdown: str
-    markdown_with_citations: str
-    references_markdown: str
-    fit_markdown: Optional[str] = None
-    fit_html: Optional[str] = None
-
 class CrawlResult(BaseModel):
     url: str
     html: str
@@ -21,10 +12,8 @@ class CrawlResult(BaseModel):
     cleaned_html: Optional[str] = None
     media: Dict[str, List[Dict]] = {}
     links: Dict[str, List[Dict]] = {}
-    downloaded_files: Optional[List[str]] = None
     screenshot: Optional[str] = None
-    markdown: Optional[Union[str, MarkdownGenerationResult]] = None
-    markdown_v2: Optional[MarkdownGenerationResult] = None
+    markdown: Optional[str] = None
     fit_markdown: Optional[str] = None
     fit_html: Optional[str] = None
     extracted_content: Optional[str] = None
@@ -32,17 +21,4 @@ class CrawlResult(BaseModel):
     error_message: Optional[str] = None
     session_id: Optional[str] = None
     response_headers: Optional[dict] = None
-    status_code: Optional[int] = None
-    
-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
-    downloaded_files: Optional[List[str]] = None
-
-    class Config:
-        arbitrary_types_allowed = True
-
-
+    status_code: Optional[int] = None

crawl4ai/tools.py

@@ -1,34 +0,0 @@
-import time
-import cProfile
-import pstats
-from functools import wraps
-
-def profile_and_time(func):
-    @wraps(func)
-    def wrapper(self, *args, **kwargs):
-        # Start timer
-        start_time = time.perf_counter()
-        
-        # Setup profiler
-        profiler = cProfile.Profile()
-        profiler.enable()
-        
-        # Run function
-        result = func(self, *args, **kwargs)
-        
-        # Stop profiler
-        profiler.disable()
-        
-        # Calculate elapsed time
-        elapsed_time = time.perf_counter() - start_time
-        
-        # Print timing
-        print(f"[PROFILER] Scraping completed in {elapsed_time:.2f} seconds")
-        
-        # Print profiling stats
-        stats = pstats.Stats(profiler)
-        stats.sort_stats('cumulative')  # Sort by cumulative time
-        stats.print_stats(20)  # Print top 20 time-consuming functions
-        
-        return result
-    return wrapper

crawl4ai/utils.py

@@ -14,157 +14,10 @@ from typing import Dict, Any
 from urllib.parse import urljoin
 import requests
 from requests.exceptions import InvalidSchema
-import hashlib
-from typing import Optional, Tuple, Dict, Any
-import xxhash
-from colorama import Fore, Style, init
-import textwrap
 
-from .html2text import HTML2Text
-class CustomHTML2Text(HTML2Text):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.inside_pre = False
-        self.inside_code = False
-        self.preserve_tags = set()  # Set of tags to preserve
-        self.current_preserved_tag = None
-        self.preserved_content = []
-        self.preserve_depth = 0
-        
-        # Configuration options
-        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 update_params(self, **kwargs):
-        """Update parameters and set preserved tags."""
-        for key, value in kwargs.items():
-            if key == 'preserve_tags':
-                self.preserve_tags = set(value)
-            else:
-                setattr(self, key, value)
-
-    def handle_tag(self, tag, attrs, start):
-        # Handle preserved tags
-        if tag in self.preserve_tags:
-            if start:
-                if self.preserve_depth == 0:
-                    self.current_preserved_tag = tag
-                    self.preserved_content = []
-                    # Format opening tag with attributes
-                    attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
-                    self.preserved_content.append(f'<{tag}{attr_str}>')
-                self.preserve_depth += 1
-                return
-            else:
-                self.preserve_depth -= 1
-                if self.preserve_depth == 0:
-                    self.preserved_content.append(f'</{tag}>')
-                    # Output the preserved HTML block with proper spacing
-                    preserved_html = ''.join(self.preserved_content)
-                    self.o('\n' + preserved_html + '\n')
-                    self.current_preserved_tag = None
-                return
-
-        # If we're inside a preserved tag, collect all content
-        if self.preserve_depth > 0:
-            if start:
-                # Format nested tags with attributes
-                attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
-                self.preserved_content.append(f'<{tag}{attr_str}>')
-            else:
-                self.preserved_content.append(f'</{tag}>')
-            return
-
-        # Handle pre tags
-        if tag == 'pre':
-            if start:
-                self.o('```\n')
-                self.inside_pre = True
-            else:
-                self.o('\n```')
-                self.inside_pre = False
-        # elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
-        #     pass
-        else:
-            super().handle_tag(tag, attrs, start)
-
-    def handle_data(self, data, entity_char=False):
-        """Override handle_data to capture content within preserved tags."""
-        if self.preserve_depth > 0:
-            self.preserved_content.append(data)
-            return
-        super().handle_data(data, entity_char)
 class InvalidCSSSelectorError(Exception):
     pass
 
-
-def create_box_message(
-   message: str, 
-   type: str = "info", 
-   width: int = 80, 
-   add_newlines: bool = True,
-   double_line: bool = False
-) -> str:
-   init()
-   
-   # Define border and text colors for different types
-   styles = {
-       "warning": (Fore.YELLOW, Fore.LIGHTYELLOW_EX, "⚠"),
-       "info": (Fore.BLUE, Fore.LIGHTBLUE_EX, "ℹ"), 
-       "success": (Fore.GREEN, Fore.LIGHTGREEN_EX, "✓"),
-       "error": (Fore.RED, Fore.LIGHTRED_EX, "×"),
-   }
-   
-   border_color, text_color, prefix = styles.get(type.lower(), styles["info"])
-   
-   # Define box characters based on line style
-   box_chars = {
-       "single": ("─", "│", "┌", "┐", "└", "┘"),
-       "double": ("═", "║", "╔", "╗", "╚", "╝")
-   }
-   line_style = "double" if double_line else "single"
-   h_line, v_line, tl, tr, bl, br = box_chars[line_style]
-   
-   # Process lines with lighter text color
-   formatted_lines = []
-   raw_lines = message.split('\n')
-   
-   if raw_lines:
-       first_line = f"{prefix} {raw_lines[0].strip()}"
-       wrapped_first = textwrap.fill(first_line, width=width-4)
-       formatted_lines.extend(wrapped_first.split('\n'))
-       
-       for line in raw_lines[1:]:
-           if line.strip():
-               wrapped = textwrap.fill(f"  {line.strip()}", width=width-4)
-               formatted_lines.extend(wrapped.split('\n'))
-           else:
-               formatted_lines.append("")
-   
-   # Create the box with colored borders and lighter text
-   horizontal_line = h_line * (width - 1)
-   box = [
-       f"{border_color}{tl}{horizontal_line}{tr}",
-       *[f"{border_color}{v_line}{text_color} {line:<{width-2}}{border_color}{v_line}" for line in formatted_lines],
-       f"{border_color}{bl}{horizontal_line}{br}{Style.RESET_ALL}"
-   ]
-   
-   result = "\n".join(box)
-   if add_newlines:
-       result = f"\n{result}\n"
-   
-   return result
-
 def calculate_semaphore_count():
     cpu_count = os.cpu_count()
     memory_gb = get_system_memory() / (1024 ** 3)  # Convert to GB
@@ -289,17 +142,12 @@ def sanitize_html(html):
 def sanitize_input_encode(text: str) -> str:
     """Sanitize input to handle potential encoding issues."""
     try:
-        try:
-            if not text:
-                return ''
-            # Attempt to encode and decode as UTF-8 to handle potential encoding issues
-            return text.encode('utf-8', errors='ignore').decode('utf-8')
-        except UnicodeEncodeError as e:
-            print(f"Warning: Encoding issue detected. Some characters may be lost. Error: {e}")
-            # Fall back to ASCII if UTF-8 fails
-            return text.encode('ascii', errors='ignore').decode('ascii')
-    except Exception as e:
-        raise ValueError(f"Error sanitizing input: {str(e)}") from e
+        # Attempt to encode and decode as UTF-8 to handle potential encoding issues
+        return text.encode('utf-8', errors='ignore').decode('utf-8')
+    except UnicodeEncodeError as e:
+        print(f"Warning: Encoding issue detected. Some characters may be lost. Error: {e}")
+        # Fall back to ASCII if UTF-8 fails
+        return text.encode('ascii', errors='ignore').decode('ascii')
 
 def escape_json_string(s):
     """
@@ -888,53 +736,45 @@ def get_content_of_website_optimized(url: str, html: str, word_count_threshold:
         'metadata': meta
     }
 
-def extract_metadata(html, soup=None):
+def extract_metadata(html, soup = None):
     metadata = {}
     
-    if not html and not soup:
-        return {}
-    
-    if not soup:
-        soup = BeautifulSoup(html, 'lxml')
-    
-    head = soup.head
-    if not head:
+    if not html:
         return metadata
     
+    # Parse HTML content with BeautifulSoup
+    if not soup:
+        soup = BeautifulSoup(html, 'html.parser')
+
     # Title
-    title_tag = head.find('title')
-    metadata['title'] = title_tag.string.strip() if title_tag and title_tag.string else None
+    title_tag = soup.find('title')
+    metadata['title'] = title_tag.string if title_tag else None
 
     # Meta description
-    description_tag = head.find('meta', attrs={'name': 'description'})
-    metadata['description'] = description_tag.get('content', '').strip() if description_tag else None
+    description_tag = soup.find('meta', attrs={'name': 'description'})
+    metadata['description'] = description_tag['content'] if description_tag else None
 
     # Meta keywords
-    keywords_tag = head.find('meta', attrs={'name': 'keywords'})
-    metadata['keywords'] = keywords_tag.get('content', '').strip() if keywords_tag else None
+    keywords_tag = soup.find('meta', attrs={'name': 'keywords'})
+    metadata['keywords'] = keywords_tag['content'] if keywords_tag else None
 
     # Meta author
-    author_tag = head.find('meta', attrs={'name': 'author'})
-    metadata['author'] = author_tag.get('content', '').strip() if author_tag else None
+    author_tag = soup.find('meta', attrs={'name': 'author'})
+    metadata['author'] = author_tag['content'] if author_tag else None
 
     # Open Graph metadata
-    og_tags = head.find_all('meta', attrs={'property': re.compile(r'^og:')})
+    og_tags = soup.find_all('meta', attrs={'property': lambda value: value and value.startswith('og:')})
     for tag in og_tags:
-        property_name = tag.get('property', '').strip()
-        content = tag.get('content', '').strip()
-        if property_name and content:
-            metadata[property_name] = content
+        property_name = tag['property']
+        metadata[property_name] = tag['content']
 
     # Twitter Card metadata
-    twitter_tags = head.find_all('meta', attrs={'name': re.compile(r'^twitter:')})
+    twitter_tags = soup.find_all('meta', attrs={'name': lambda value: value and value.startswith('twitter:')})
     for tag in twitter_tags:
-        property_name = tag.get('name', '').strip()
-        content = tag.get('content', '').strip()
-        if property_name and content:
-            metadata[property_name] = content
-    
-    return metadata
+        property_name = tag['name']
+        metadata[property_name] = tag['content']
 
+    return metadata
 
 def extract_xml_tags(string):
     tags = re.findall(r'<(\w+)>', string)
@@ -1140,54 +980,9 @@ def wrap_text(draw, text, font, max_width):
     return '\n'.join(lines)
 
 def format_html(html_string):
-    soup = BeautifulSoup(html_string, 'lxml.parser')
+    soup = BeautifulSoup(html_string, 'html.parser')
     return soup.prettify()
 
-def fast_format_html(html_string):
-    """
-    A fast HTML formatter that uses string operations instead of parsing.
-    
-    Args:
-        html_string (str): The HTML string to format
-        
-    Returns:
-        str: The formatted HTML string
-    """
-    # Initialize variables
-    indent = 0
-    indent_str = "  "  # Two spaces for indentation
-    formatted = []
-    in_content = False
-    
-    # Split by < and > to separate tags and content
-    parts = html_string.replace('>', '>\n').replace('<', '\n<').split('\n')
-    
-    for part in parts:
-        if not part.strip():
-            continue
-            
-        # Handle closing tags
-        if part.startswith('</'):
-            indent -= 1
-            formatted.append(indent_str * indent + part)
-            
-        # Handle self-closing tags
-        elif part.startswith('<') and part.endswith('/>'):
-            formatted.append(indent_str * indent + part)
-            
-        # Handle opening tags
-        elif part.startswith('<'):
-            formatted.append(indent_str * indent + part)
-            indent += 1
-            
-        # Handle content between tags
-        else:
-            content = part.strip()
-            if content:
-                formatted.append(indent_str * indent + content)
-    
-    return '\n'.join(formatted)
-
 def normalize_url(href, base_url):
     """Normalize URLs to ensure consistent format"""
     from urllib.parse import urljoin, urlparse
@@ -1251,82 +1046,3 @@ def is_external_url(url, base_domain):
         return False
         
     return False
-
-def clean_tokens(tokens: list[str]) -> list[str]:
-    # Set of tokens to remove
-    noise = {'ccp', 'up', '↑', '▲', '⬆️', 'a', 'an', 'at', 'by', 'in', 'of', 'on', 'to', 'the'}
-
-    STOP_WORDS = {
-        'a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'for', 'from', 
-        'has', 'he', 'in', 'is', 'it', 'its', 'of', 'on', 'that', 'the', 
-        'to', 'was', 'were', 'will', 'with',
-        
-        # Pronouns
-        'i', 'you', 'he', 'she', 'it', 'we', 'they',
-        'me', 'him', 'her', 'us', 'them',
-        'my', 'your', 'his', 'her', 'its', 'our', 'their',
-        'mine', 'yours', 'hers', 'ours', 'theirs',
-        'myself', 'yourself', 'himself', 'herself', 'itself', 'ourselves', 'themselves',
-        
-        # Common verbs
-        'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being',
-        'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing',
-        
-        # Prepositions
-        'about', 'above', 'across', 'after', 'against', 'along', 'among', 'around',
-        'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'beyond',
-        'by', 'down', 'during', 'except', 'for', 'from', 'in', 'inside', 'into',
-        'near', 'of', 'off', 'on', 'out', 'outside', 'over', 'past', 'through',
-        'to', 'toward', 'under', 'underneath', 'until', 'up', 'upon', 'with', 'within',
-        
-        # Conjunctions
-        'and', 'but', 'or', 'nor', 'for', 'yet', 'so',
-        'although', 'because', 'since', 'unless',
-        
-        # Articles
-        'a', 'an', 'the',
-        
-        # Other common words
-        'this', 'that', 'these', 'those',
-        'what', 'which', 'who', 'whom', 'whose',
-        'when', 'where', 'why', 'how',
-        'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such',
-        'can', 'cannot', "can't", 'could', "couldn't",
-        'may', 'might', 'must', "mustn't",
-        'shall', 'should', "shouldn't",
-        'will', "won't", 'would', "wouldn't",
-        'not', "n't", 'no', 'nor', 'none'
-    }   
-   
-    # Single comprehension, more efficient than multiple passes
-    return [token for token in tokens 
-            if len(token) > 2 
-            and token not in noise 
-            and token not in STOP_WORDS
-            and not token.startswith('↑')
-            and not token.startswith('▲')
-            and not token.startswith('⬆')]
-
-
-def generate_content_hash(content: str) -> str:
-    """Generate a unique hash for content"""
-    return xxhash.xxh64(content.encode()).hexdigest()
-    # return hashlib.sha256(content.encode()).hexdigest()
-
-def ensure_content_dirs(base_path: str) -> Dict[str, str]:
-    """Create content directories if they don't exist"""
-    dirs = {
-        'html': 'html_content',
-        'cleaned': 'cleaned_html',
-        'markdown': 'markdown_content', 
-        'extracted': 'extracted_content',
-        'screenshots': 'screenshots'
-    }
-    
-    content_paths = {}
-    for key, dirname in dirs.items():
-        path = os.path.join(base_path, dirname)
-        os.makedirs(path, exist_ok=True)
-        content_paths[key] = path
-        
-    return content_paths

crawl4ai/version_manager.py

@@ -1,30 +0,0 @@
-# version_manager.py
-import os
-from pathlib import Path
-from packaging import version
-from . import __version__
-
-class VersionManager:
-    def __init__(self):
-        self.home_dir = Path.home() / ".crawl4ai"
-        self.version_file = self.home_dir / "version.txt"
-        
-    def get_installed_version(self):
-        """Get the version recorded in home directory"""
-        if not self.version_file.exists():
-            return None
-        try:
-            return version.parse(self.version_file.read_text().strip())
-        except:
-            return None
-            
-    def update_version(self):
-        """Update the version file to current library version"""
-        self.version_file.write_text(__version__.__version__)
-        
-    def needs_update(self):
-        """Check if database needs update based on version"""
-        installed = self.get_installed_version()
-        current = version.parse(__version__.__version__)
-        return installed is None or installed < current
-

crawl4ai/web_crawler.py

@@ -10,7 +10,6 @@ from .extraction_strategy import *
 from .crawler_strategy import *
 from typing import List
 from concurrent.futures import ThreadPoolExecutor
-from .content_scraping_strategy import WebScrapingStrategy
 from .config import *
 import warnings
 import json
@@ -182,21 +181,7 @@ class WebCrawler:
             # Extract content from HTML
             try:
                 t1 = time.time()
-                scrapping_strategy = WebScrapingStrategy()
-                extra_params = {k: v for k, v in kwargs.items() if k not in ["only_text", "image_description_min_word_threshold"]}
-                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
-                    ),
-                    **extra_params,
-                )
-                
-                # result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
+                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:.2f} seconds")
                 

docker-compose.yml

@@ -1,62 +0,0 @@
-services:
-  crawl4ai:
-    build:
-      context: .
-      dockerfile: Dockerfile
-      args:
-        PYTHON_VERSION: "3.10"
-        INSTALL_TYPE: ${INSTALL_TYPE:-basic}
-        ENABLE_GPU: false
-    profiles: ["local"]
-    ports:
-      - "11235:11235"
-      - "8000:8000"
-      - "9222:9222"
-      - "8080:8080"
-    environment:
-      - CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-}
-      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
-      - CLAUDE_API_KEY=${CLAUDE_API_KEY:-}
-    volumes:
-      - /dev/shm:/dev/shm
-    deploy:
-      resources:
-        limits:
-          memory: 4G
-        reservations:
-          memory: 1G
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11235/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 40s
-
-  crawl4ai-hub:
-    image: unclecode/crawl4ai:basic
-    profiles: ["hub"]
-    ports:
-      - "11235:11235"
-      - "8000:8000"
-      - "9222:9222"
-      - "8080:8080"
-    environment:
-      - CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-}
-      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
-      - CLAUDE_API_KEY=${CLAUDE_API_KEY:-}
-    volumes:
-      - /dev/shm:/dev/shm
-    deploy:
-      resources:
-        limits:
-          memory: 4G
-        reservations:
-          memory: 1G
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11235/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 40s

docs/examples/docker_example.py

@@ -7,16 +7,12 @@ import os
 from typing import Dict, Any
 
 class Crawl4AiTester:
-    def __init__(self, base_url: str = "http://localhost:11235", api_token: str = None):
+    def __init__(self, base_url: str = "http://localhost:11235"):
         self.base_url = base_url
-        self.api_token = api_token or os.getenv('CRAWL4AI_API_TOKEN') or "test_api_code"  # Check environment variable as fallback
-        self.headers = {'Authorization': f'Bearer {self.api_token}'} if self.api_token else {}
         
     def submit_and_wait(self, request_data: Dict[str, Any], timeout: int = 300) -> Dict[str, Any]:
         # Submit crawl job
-        response = requests.post(f"{self.base_url}/crawl", json=request_data, headers=self.headers)
-        if response.status_code == 403:
-            raise Exception("API token is invalid or missing")
+        response = requests.post(f"{self.base_url}/crawl", json=request_data)
         task_id = response.json()["task_id"]
         print(f"Task ID: {task_id}")
         
@@ -26,7 +22,7 @@ class Crawl4AiTester:
             if time.time() - start_time > timeout:
                 raise TimeoutError(f"Task {task_id} did not complete within {timeout} seconds")
                 
-            result = requests.get(f"{self.base_url}/task/{task_id}", headers=self.headers)
+            result = requests.get(f"{self.base_url}/task/{task_id}")
             status = result.json()
             
             if status["status"] == "failed":
@@ -37,30 +33,9 @@ class Crawl4AiTester:
                 return status
                 
             time.sleep(2)
-            
-    def submit_sync(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
-        response = requests.post(f"{self.base_url}/crawl_sync", json=request_data, headers=self.headers, timeout=60)
-        if response.status_code == 408:
-            raise TimeoutError("Task did not complete within server timeout")
-        response.raise_for_status()
-        return response.json()
-    
-    def crawl_direct(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Directly crawl without using task queue"""
-        response = requests.post(
-            f"{self.base_url}/crawl_direct", 
-            json=request_data, 
-            headers=self.headers
-        )
-        response.raise_for_status()
-        return response.json()
 
 def test_docker_deployment(version="basic"):
-    tester = Crawl4AiTester(
-        base_url="http://localhost:11235" ,
-        # base_url="https://api.crawl4ai.com" # just for example
-        # api_token="test" # just for example
-    )
+    tester = Crawl4AiTester()
     print(f"Testing Crawl4AI Docker {version} version")
     
     # Health check with timeout and retry
@@ -78,10 +53,7 @@ def test_docker_deployment(version="basic"):
             time.sleep(5)
     
     # Test cases based on version
-    # test_basic_crawl(tester)
-    # test_basic_crawl(tester)
-    # test_basic_crawl_sync(tester)
-    test_basic_crawl_direct(tester)
+    test_basic_crawl(tester)
     
     # if version in ["full", "transformer"]:
     #     test_cosine_extraction(tester)
@@ -98,8 +70,7 @@ def test_basic_crawl(tester: Crawl4AiTester):
     print("\n=== Testing Basic Crawl ===")
     request = {
         "urls": "https://www.nbcnews.com/business",
-        "priority": 10, 
-        "session_id": "test"
+        "priority": 10
     }
     
     result = tester.submit_and_wait(request)
@@ -107,34 +78,6 @@ def test_basic_crawl(tester: Crawl4AiTester):
     assert result["result"]["success"]
     assert len(result["result"]["markdown"]) > 0
 
-def test_basic_crawl_sync(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl (Sync) ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        "session_id": "test"
-    }
-    
-    result = tester.submit_sync(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result['status'] == 'completed'
-    assert result['result']['success']
-    assert len(result['result']['markdown']) > 0
-    
-def test_basic_crawl_direct(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl (Direct) ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        # "session_id": "test"
-        "cache_mode": "bypass"  # or "enabled", "disabled", "read_only", "write_only"
-    }
-    
-    result = tester.crawl_direct(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result['result']['success']
-    assert len(result['result']['markdown']) > 0
-    
 def test_js_execution(tester: Crawl4AiTester):
     print("\n=== Testing JS Execution ===")
     request = {

docs/examples/quickstart_async.py

@@ -13,9 +13,7 @@ import re
 from typing import Dict, List
 from bs4 import BeautifulSoup
 from pydantic import BaseModel, Field
-from crawl4ai import AsyncWebCrawler, CacheMode
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
-from crawl4ai.content_filter_strategy import BM25ContentFilter
+from crawl4ai import AsyncWebCrawler
 from crawl4ai.extraction_strategy import (
     JsonCssExtractionStrategy,
     LLMExtractionStrategy,
@@ -53,7 +51,7 @@ async def simple_example_with_running_js_code():
             url="https://www.nbcnews.com/business",
             js_code=js_code,
             # wait_for=wait_for,
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
         )
         print(result.markdown[:500])  # Print first 500 characters
 
@@ -63,7 +61,7 @@ async def simple_example_with_css_selector():
         result = await crawler.arun(
             url="https://www.nbcnews.com/business",
             css_selector=".wide-tease-item__description",
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
         )
         print(result.markdown[:500])  # Print first 500 characters
 
@@ -73,12 +71,12 @@ async def use_proxy():
         "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 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:
@@ -134,7 +132,7 @@ async def extract_structured_data_using_llm(provider: str, api_token: str = None
                 {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
                 extra_args=extra_args
             ),
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
         )
         print(result.extracted_content)
 
@@ -168,7 +166,7 @@ async def extract_structured_data_using_css_extractor():
         result = await crawler.arun(
             url="https://www.coinbase.com/explore",
             extraction_strategy=extraction_strategy,
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
         )
 
         assert result.success, "Failed to crawl the page"
@@ -215,7 +213,7 @@ async def crawl_dynamic_content_pages_method_1():
                 session_id=session_id,
                 css_selector="li.Box-sc-g0xbh4-0",
                 js=js_next_page if page > 0 else None,
-                cache_mode=CacheMode.BYPASS,
+                bypass_cache=True,
                 js_only=page > 0,
                 headless=False,
             )
@@ -284,7 +282,7 @@ async def crawl_dynamic_content_pages_method_2():
                 extraction_strategy=extraction_strategy,
                 js_code=js_next_page_and_wait if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS,
+                bypass_cache=True,
                 headless=False,
             )
 
@@ -345,7 +343,7 @@ async def crawl_dynamic_content_pages_method_3():
                 js_code=js_next_page if page > 0 else None,
                 wait_for=wait_for if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS,
+                bypass_cache=True,
                 headless=False,
             )
 
@@ -386,7 +384,7 @@ async def crawl_with_user_simultion():
         url = "YOUR-URL-HERE"
         result = await crawler.arun(
             url=url,            
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
             magic = True, # Automatically detects and removes overlays, popups, and other elements that block content
             # 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
@@ -410,7 +408,7 @@ async def speed_comparison():
     params={'formats': ['markdown', 'html']}
     )
     end = time.time()
-    print("Firecrawl:")
+    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')}")
@@ -422,7 +420,7 @@ async def speed_comparison():
         result = await crawler.arun(
             url="https://www.nbcnews.com/business",
             word_count_threshold=0,
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
             verbose=False,
         )
         end = time.time()
@@ -432,25 +430,6 @@ async def speed_comparison():
         print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
         print()
 
-        # Crawl4AI with advanced content filtering
-        start = time.time()
-        result = await crawler.arun(
-            url="https://www.nbcnews.com/business",
-            word_count_threshold=0,
-            markdown_generator=DefaultMarkdownGenerator(
-                content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
-            ),
-            cache_mode=CacheMode.BYPASS,
-            verbose=False,
-        )
-        end = time.time()
-        print("Crawl4AI (Markdown Plus):")
-        print(f"Time taken: {end - start:.2f} seconds")
-        print(f"Content length: {len(result.markdown_v2.raw_markdown)} characters")
-        print(f"Fit Markdown: {len(result.markdown_v2.fit_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(
@@ -459,17 +438,13 @@ async def speed_comparison():
                 "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
             ],
             word_count_threshold=0,
-            cache_mode=CacheMode.BYPASS,
-            markdown_generator=DefaultMarkdownGenerator(
-                content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
-            ),
+            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"Fit Markdown: {len(result.markdown_v2.fit_markdown)} characters")
         print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
 
     print("\nNote on Speed Comparison:")
@@ -508,7 +483,7 @@ async def generate_knowledge_graph():
         url = "https://paulgraham.com/love.html"
         result = await crawler.arun(
             url=url,
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
             extraction_strategy=extraction_strategy,
             # magic=True
         )
@@ -521,7 +496,7 @@ async def fit_markdown_remove_overlay():
         url = "https://janineintheworld.com/places-to-visit-in-central-mexico"
         result = await crawler.arun(
             url=url,
-            cache_mode=CacheMode.BYPASS,
+            bypass_cache=True,
             word_count_threshold = 10,
             remove_overlay_elements=True,
             screenshot = True
@@ -542,10 +517,10 @@ async def main():
     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("ollama/llama3.2")    
+    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 = {

docs/examples/v0.3.74.overview.py

@@ -1,277 +0,0 @@
-import os, sys
-# append the parent directory to the sys.path
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-parent_parent_dir = os.path.dirname(parent_dir)
-sys.path.append(parent_parent_dir)
-__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-__data__ = os.path.join(__location__, "__data")
-import asyncio
-from pathlib import Path
-import aiohttp
-import json
-from crawl4ai import AsyncWebCrawler, CacheMode
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-
-# 1. File Download Processing Example
-async def download_example():
-    """Example of downloading files from Python.org"""
-    # downloads_path = os.path.join(os.getcwd(), "downloads")
-    downloads_path = os.path.join(Path.home(), ".crawl4ai", "downloads")
-    os.makedirs(downloads_path, exist_ok=True)
-    
-    print(f"Downloads will be saved to: {downloads_path}")
-    
-    async with AsyncWebCrawler(
-        accept_downloads=True,
-        downloads_path=downloads_path,
-        verbose=True
-    ) as crawler:
-        result = await crawler.arun(
-            url="https://www.python.org/downloads/",
-            js_code="""
-            // Find and click the first Windows installer link
-            const downloadLink = document.querySelector('a[href$=".exe"]');
-            if (downloadLink) {
-                console.log('Found download link:', downloadLink.href);
-                downloadLink.click();
-            } else {
-                console.log('No .exe download link found');
-            }
-            """,
-            delay_before_return_html=1,  # Wait 5 seconds to ensure download starts
-            cache_mode=CacheMode.BYPASS
-        )
-        
-        if result.downloaded_files:
-            print("\nDownload successful!")
-            print("Downloaded files:")
-            for file_path in result.downloaded_files:
-                print(f"- {file_path}")
-                print(f"  File size: {os.path.getsize(file_path) / (1024*1024):.2f} MB")
-        else:
-            print("\nNo files were downloaded")
-
-# 2. Local File and Raw HTML Processing Example
-async def local_and_raw_html_example():
-    """Example of processing local files and raw HTML"""
-    # Create a sample HTML file
-    sample_file = os.path.join(__data__, "sample.html")
-    with open(sample_file, "w") as f:
-        f.write("""
-        <html><body>
-            <h1>Test Content</h1>
-            <p>This is a test paragraph.</p>
-        </body></html>
-        """)
-    
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        # Process local file
-        local_result = await crawler.arun(
-            url=f"file://{os.path.abspath(sample_file)}"
-        )
-        
-        # Process raw HTML
-        raw_html = """
-        <html><body>
-            <h1>Raw HTML Test</h1>
-            <p>This is a test of raw HTML processing.</p>
-        </body></html>
-        """
-        raw_result = await crawler.arun(
-            url=f"raw:{raw_html}"
-        )
-        
-        # Clean up
-        os.remove(sample_file)
-        
-        print("Local file content:", local_result.markdown)
-        print("\nRaw HTML content:", raw_result.markdown)
-
-# 3. Enhanced Markdown Generation Example
-async def markdown_generation_example():
-    """Example of enhanced markdown generation with citations and LLM-friendly features"""
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        # Create a content filter (optional)
-        content_filter = BM25ContentFilter(
-            # user_query="History and cultivation",
-            bm25_threshold=1.0
-        )
-        
-        result = await crawler.arun(
-            url="https://en.wikipedia.org/wiki/Apple",
-            css_selector="main div#bodyContent",
-            content_filter=content_filter,
-            cache_mode=CacheMode.BYPASS
-        )
-        
-        from crawl4ai import AsyncWebCrawler
-        from crawl4ai.content_filter_strategy import BM25ContentFilter
-        
-        result = await crawler.arun(
-            url="https://en.wikipedia.org/wiki/Apple",
-            css_selector="main div#bodyContent",
-            content_filter=BM25ContentFilter()
-        )
-        print(result.markdown_v2.fit_markdown)
-        
-        print("\nMarkdown Generation Results:")
-        print(f"1. Original markdown length: {len(result.markdown)}")
-        print(f"2. New markdown versions (markdown_v2):")
-        print(f"   - Raw markdown length: {len(result.markdown_v2.raw_markdown)}")
-        print(f"   - Citations markdown length: {len(result.markdown_v2.markdown_with_citations)}")
-        print(f"   - References section length: {len(result.markdown_v2.references_markdown)}")
-        if result.markdown_v2.fit_markdown:
-            print(f"   - Filtered markdown length: {len(result.markdown_v2.fit_markdown)}")
-        
-        # Save examples to files
-        output_dir = os.path.join(__data__, "markdown_examples")
-        os.makedirs(output_dir, exist_ok=True)
-        
-        # Save different versions
-        with open(os.path.join(output_dir, "1_raw_markdown.md"), "w") as f:
-            f.write(result.markdown_v2.raw_markdown)
-            
-        with open(os.path.join(output_dir, "2_citations_markdown.md"), "w") as f:
-            f.write(result.markdown_v2.markdown_with_citations)
-            
-        with open(os.path.join(output_dir, "3_references.md"), "w") as f:
-            f.write(result.markdown_v2.references_markdown)
-            
-        if result.markdown_v2.fit_markdown:
-            with open(os.path.join(output_dir, "4_filtered_markdown.md"), "w") as f:
-                f.write(result.markdown_v2.fit_markdown)
-                
-        print(f"\nMarkdown examples saved to: {output_dir}")
-        
-        # Show a sample of citations and references
-        print("\nSample of markdown with citations:")
-        print(result.markdown_v2.markdown_with_citations[:500] + "...\n")
-        print("Sample of references:")
-        print('\n'.join(result.markdown_v2.references_markdown.split('\n')[:10]) + "...")
-
-# 4. Browser Management Example
-async def browser_management_example():
-    """Example of using enhanced browser management features"""
-    # Use the specified user directory path
-    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
-    os.makedirs(user_data_dir, exist_ok=True)
-    
-    print(f"Browser profile will be saved to: {user_data_dir}")
-    
-    async with AsyncWebCrawler(
-        use_managed_browser=True,
-        user_data_dir=user_data_dir,
-        headless=False,
-        verbose=True
-    ) as crawler:
-
-        result = await crawler.arun(
-            url="https://crawl4ai.com",
-            # session_id="persistent_session_1",
-            cache_mode=CacheMode.BYPASS
-        )        
-        # Use GitHub as an example - it's a good test for browser management
-        # because it requires proper browser handling
-        result = await crawler.arun(
-            url="https://github.com/trending",
-            # session_id="persistent_session_1",
-            cache_mode=CacheMode.BYPASS
-        )
-        
-        print("\nBrowser session result:", result.success)
-        if result.success:
-            print("Page title:", result.metadata.get('title', 'No title found'))
-
-# 5. API Usage Example
-async def api_example():
-    """Example of using the new API endpoints"""
-    api_token = os.getenv('CRAWL4AI_API_TOKEN') or "test_api_code"
-    headers = {'Authorization': f'Bearer {api_token}'}    
-    async with aiohttp.ClientSession() as session:
-        # Submit crawl job
-        crawl_request = {
-            "urls": ["https://news.ycombinator.com"],  # Hacker News as an example
-            "extraction_config": {
-                "type": "json_css",
-                "params": {
-                    "schema": {
-                        "name": "Hacker News Articles",
-                        "baseSelector": ".athing",
-                        "fields": [
-                            {
-                                "name": "title",
-                                "selector": ".title a",
-                                "type": "text"
-                            },
-                            {
-                                "name": "score",
-                                "selector": ".score",
-                                "type": "text"
-                            },
-                            {
-                                "name": "url",
-                                "selector": ".title a",
-                                "type": "attribute",
-                                "attribute": "href"
-                            }
-                        ]
-                    }
-                }
-            },
-            "crawler_params": {
-                "headless": True,
-                # "use_managed_browser": True
-            },
-            "cache_mode": "bypass",
-            # "screenshot": True,
-            # "magic": True
-        }
-        
-        async with session.post(
-            "http://localhost:11235/crawl",
-            json=crawl_request,
-            headers=headers
-        ) as response:
-            task_data = await response.json()
-            task_id = task_data["task_id"]
-            
-            # Check task status
-            while True:
-                async with session.get(
-                    f"http://localhost:11235/task/{task_id}",
-                    headers=headers
-                ) as status_response:
-                    result = await status_response.json()
-                    print(f"Task status: {result['status']}")
-                    
-                    if result["status"] == "completed":
-                        print("Task completed!")
-                        print("Results:")
-                        news = json.loads(result["results"][0]['extracted_content'])
-                        print(json.dumps(news[:4], indent=2))
-                        break
-                    else:
-                        await asyncio.sleep(1)
-
-# Main execution
-async def main():
-    # print("Running Crawl4AI feature examples...")
-    
-    # print("\n1. Running Download Example:")
-    # await download_example()
-    
-    # print("\n2. Running Markdown Generation Example:")
-    # await markdown_generation_example()
-    
-    # # print("\n3. Running Local and Raw HTML Example:")
-    # await local_and_raw_html_example()
-    
-    # # print("\n4. Running Browser Management Example:")
-    await browser_management_example()
-    
-    # print("\n5. Running API Example:")
-    await api_example()
-
-if __name__ == "__main__":
-    asyncio.run(main())

docs/md_v2/advanced/hooks-auth.md

@@ -18,7 +18,7 @@ Let's see how we can customize the AsyncWebCrawler using hooks! In this example,
 import asyncio
 from crawl4ai import AsyncWebCrawler
 from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
-from playwright.async_api import Page, Browser, BrowserContext
+from playwright.async_api import Page, Browser
 
 async def on_browser_created(browser: Browser):
     print("[HOOK] on_browser_created")
@@ -71,11 +71,7 @@ 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!")
     
-    initial_cookies = [
-        {"name": "sessionId", "value": "abc123", "domain": ".example.com"},
-        {"name": "userId", "value": "12345", "domain": ".example.com"}
-    ]
-    crawler_strategy = AsyncPlaywrightCrawlerStrategy(verbose=True, cookies=initial_cookies)
+    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)

docs/md_v2/advanced/managed_browser.md

@@ -1,84 +0,0 @@
-# Content Filtering in Crawl4AI
-
-This guide explains how to use content filtering strategies in Crawl4AI to extract the most relevant information from crawled web pages.  You'll learn how to use the built-in `BM25ContentFilter` and how to create your own custom content filtering strategies.
-
-## Relevance Content Filter
-
-The `RelevanceContentFilter` is an abstract class that provides a common interface for content filtering strategies. Specific filtering algorithms, like `BM25ContentFilter`, inherit from this class and implement the `filter_content` method. This method takes the HTML content as input and returns a list of filtered text blocks.
-
-## BM25 Algorithm
-
-The `BM25ContentFilter` uses the BM25 algorithm, a ranking function used in information retrieval to estimate the relevance of documents to a given search query. In Crawl4AI, this algorithm helps to identify and extract text chunks that are most relevant to the page's metadata or a user-specified query.
-
-### Usage
-
-To use the `BM25ContentFilter`, initialize it and then pass it as the `extraction_strategy` parameter to the `arun` method of the crawler.
-
-```python
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-
-async def filter_content(url, query=None):
-    async with AsyncWebCrawler() as crawler:
-        content_filter = BM25ContentFilter(user_query=query)
-        result = await crawler.arun(url=url, extraction_strategy=content_filter, fit_markdown=True) # Set fit_markdown flag to True to trigger BM25 filtering
-        if result.success:
-            print(f"Filtered Content (JSON):\n{result.extracted_content}")
-            print(f"\nFiltered Markdown:\n{result.fit_markdown}") # New field in CrawlResult object
-            print(f"\nFiltered HTML:\n{result.fit_html}") # New field in CrawlResult object. Note that raw HTML may have tags re-organized due to internal parsing.
-        else:
-            print("Error:", result.error_message)
-
-# Example usage:
-asyncio.run(filter_content("https://en.wikipedia.org/wiki/Apple", "fruit nutrition health")) # with query
-asyncio.run(filter_content("https://en.wikipedia.org/wiki/Apple")) # without query, metadata will be used as the query.
-
-```
-
-### Parameters
-
-- **`user_query`**:  (Optional) A string representing the search query. If not provided, the filter extracts relevant metadata (title, description, keywords) from the page and uses that as the query.
-- **`bm25_threshold`**: (Optional, default 1.0)  A float value that controls the threshold for relevance.  Higher values result in stricter filtering, returning only the most relevant text chunks. Lower values result in more lenient filtering.
-
-
-## Fit Markdown Flag
-
-Setting the `fit_markdown` flag to `True` in the `arun` method activates the BM25 content filtering during the crawl. The `fit_markdown` parameter instructs the scraper to extract and clean the HTML, primarily to prepare for a Large Language Model that cannot process large amounts of data. Setting this flag not only improves the quality of the extracted content but also adds the filtered content to two new attributes in the returned  `CrawlResult` object: `fit_markdown` and `fit_html`.
-
-
-## Custom Content Filtering Strategies
-
-You can create your own custom filtering strategies by inheriting from the `RelevantContentFilter` class and implementing the `filter_content` method.  This allows you to tailor the filtering logic to your specific needs.
-
-```python
-from crawl4ai.content_filter_strategy import RelevantContentFilter
-from bs4 import BeautifulSoup, Tag
-from typing import List
-
-class MyCustomFilter(RelevantContentFilter):
-    def filter_content(self, html: str) -> List[str]:
-        soup = BeautifulSoup(html, 'lxml')
-        # Implement custom filtering logic here
-        # Example: extract all paragraphs within divs with class "article-body"
-        filtered_paragraphs = []
-        for tag in soup.select("div.article-body p"):
-            if isinstance(tag, Tag):
-                filtered_paragraphs.append(str(tag)) # Add the cleaned HTML element.  
-        return filtered_paragraphs
-
-
-
-async def custom_filter_demo(url: str):
-    async with AsyncWebCrawler() as crawler:
-        custom_filter = MyCustomFilter()
-        result = await crawler.arun(url, extraction_strategy=custom_filter)
-        if result.success:
-            print(result.extracted_content)
-
-```
-
-This example demonstrates extracting paragraphs from a specific div class.  You can customize this logic to implement different filtering strategies, use regular expressions, analyze text density, or apply other relevant techniques.
-
-## Conclusion
-
-Content filtering strategies provide a powerful way to refine the output of your crawls. By using `BM25ContentFilter` or creating custom strategies, you can focus on the most pertinent information and improve the efficiency of your data processing pipeline.

docs/md_v2/advanced/session-management-advanced.md

@@ -30,7 +30,7 @@ Let's start with a basic example of session-based crawling:
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
+from crawl4ai import AsyncWebCrawler
 
 async def basic_session_crawl():
     async with AsyncWebCrawler(verbose=True) as crawler:
@@ -43,7 +43,7 @@ async def basic_session_crawl():
                 session_id=session_id,
                 js_code="document.querySelector('.load-more-button').click();" if page > 0 else None,
                 css_selector=".content-item",
-                cache_mode=CacheMode.BYPASS
+                bypass_cache=True
             )
             
             print(f"Page {page + 1}: Found {result.extracted_content.count('.content-item')} items")
@@ -102,7 +102,7 @@ async def advanced_session_crawl_with_hooks():
                 session_id=session_id,
                 css_selector="li.commit-item",
                 js_code=js_next_page if page > 0 else None,
-                cache_mode=CacheMode.BYPASS,
+                bypass_cache=True,
                 js_only=page > 0
             )
 
@@ -174,7 +174,7 @@ async def integrated_js_and_wait_crawl():
                 extraction_strategy=extraction_strategy,
                 js_code=js_next_page_and_wait if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS
+                bypass_cache=True
             )
 
             commits = json.loads(result.extracted_content)
@@ -241,7 +241,7 @@ async def wait_for_parameter_crawl():
                 js_code=js_next_page if page > 0 else None,
                 wait_for=wait_for if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS
+                bypass_cache=True
             )
 
             commits = json.loads(result.extracted_content)

docs/md_v2/advanced/session-management.md

@@ -75,7 +75,7 @@ async def crawl_dynamic_content():
                 js_code=js_next_page if page > 0 else None,
                 wait_for=wait_for if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS
+                bypass_cache=True
             )
 
             if result.success:

docs/md_v2/api/arun.md

@@ -8,26 +8,11 @@ The following parameters can be passed to the `arun()` method. They are organize
 await crawler.arun(
     url="https://example.com",   # Required: URL to crawl
     verbose=True,               # Enable detailed logging
-    cache_mode=CacheMode.ENABLED,  # Control cache behavior
+    bypass_cache=False,         # Skip cache for this request
     warmup=True                # Whether to run warmup check
 )
 ```
 
-## Cache Control
-
-```python
-from crawl4ai import CacheMode
-
-await crawler.arun(
-    cache_mode=CacheMode.ENABLED,    # Normal caching (read/write)
-    # Other cache modes:
-    # cache_mode=CacheMode.DISABLED   # No caching at all
-    # cache_mode=CacheMode.READ_ONLY  # Only read from cache
-    # cache_mode=CacheMode.WRITE_ONLY # Only write to cache
-    # cache_mode=CacheMode.BYPASS     # Skip cache for this operation
-)
-```
-
 ## Content Processing Parameters
 
 ### Text Processing
@@ -177,13 +162,14 @@ await crawler.arun(
 
 ## Parameter Interactions and Notes
 
-1. **Cache and Performance Setup**
+1. **Magic Mode Combinations**
    ```python
-   # Optimal caching for repeated crawls
+   # Full anti-detection setup
    await crawler.arun(
-       cache_mode=CacheMode.ENABLED,
-       word_count_threshold=10,
-       process_iframes=False
+       magic=True,
+       headless=False,
+       simulate_user=True,
+       override_navigator=True
    )
    ```
 
@@ -193,8 +179,7 @@ await crawler.arun(
    await crawler.arun(
        js_code="window.scrollTo(0, document.body.scrollHeight);",
        wait_for="css:.lazy-content",
-       delay_before_return_html=2.0,
-       cache_mode=CacheMode.WRITE_ONLY  # Cache results after dynamic load
+       delay_before_return_html=2.0
    )
    ```
 
@@ -207,8 +192,7 @@ await crawler.arun(
        extraction_strategy=my_strategy,
        chunking_strategy=my_chunking,
        process_iframes=True,
-       remove_overlay_elements=True,
-       cache_mode=CacheMode.ENABLED
+       remove_overlay_elements=True
    )
    ```
 
@@ -217,7 +201,7 @@ await crawler.arun(
 1. **Performance Optimization**
    ```python
    await crawler.arun(
-       cache_mode=CacheMode.ENABLED,  # Use full caching
+       bypass_cache=False,           # Use cache when possible
        word_count_threshold=10,      # Filter out noise
        process_iframes=False         # Skip iframes if not needed
    )
@@ -228,8 +212,7 @@ await crawler.arun(
    await crawler.arun(
        magic=True,                   # Enable anti-detection
        delay_before_return_html=1.0, # Wait for dynamic content
-       page_timeout=60000,          # Longer timeout for slow pages
-       cache_mode=CacheMode.WRITE_ONLY  # Cache results after successful crawl
+       page_timeout=60000           # Longer timeout for slow pages
    )
    ```
 
@@ -238,7 +221,6 @@ await crawler.arun(
    await crawler.arun(
        remove_overlay_elements=True,  # Remove popups
        excluded_tags=['nav', 'aside'],# Remove unnecessary elements
-       keep_data_attributes=False,    # Remove data attributes
-       cache_mode=CacheMode.ENABLED   # Use cache for faster processing
+       keep_data_attributes=False     # Remove data attributes
    )
    ```

docs/md_v2/api/crawl-result.md

@@ -20,7 +20,6 @@ class CrawlResult(BaseModel):
     fit_html: Optional[str] = None          # Most relevant HTML content
     markdown: Optional[str] = None          # HTML converted to markdown
     fit_markdown: Optional[str] = None      # Most relevant markdown content
-    downloaded_files: Optional[List[str]] = None  # Downloaded files
     
     # Extracted Data
     extracted_content: Optional[str] = None  # Content from extraction strategy

docs/md_v2/api/parameters.md

@@ -32,5 +32,4 @@
 | async_webcrawler.py | warmup | `kwargs.get("warmup", True)` | AsyncWebCrawler | Initialize crawler with warmup request |
 | async_webcrawler.py | session_id | `kwargs.get("session_id", None)` | AsyncWebCrawler | Session identifier for browser reuse |
 | async_webcrawler.py | only_text | `kwargs.get("only_text", False)` | AsyncWebCrawler | Extract only text content |
-| async_webcrawler.py | bypass_cache | `kwargs.get("bypass_cache", False)` | AsyncWebCrawler | Skip cache and force fresh crawl |
-| async_webcrawler.py | cache_mode | `kwargs.get("cache_mode", CacheMode.ENABLE)` | AsyncWebCrawler | Cache handling mode for request |
+| async_webcrawler.py | bypass_cache | `kwargs.get("bypass_cache", False)` | AsyncWebCrawler | Skip cache and force fresh crawl |

docs/md_v2/basic/cache-modes.md

@@ -1,79 +0,0 @@
-# Crawl4AI Cache System and Migration Guide
-
-## Overview
-Starting from version X.X.X, Crawl4AI introduces a new caching system that replaces the old boolean flags with a more intuitive `CacheMode` enum. This change simplifies cache control and makes the behavior more predictable.
-
-## Old vs New Approach
-
-### Old Way (Deprecated)
-The old system used multiple boolean flags:
-- `bypass_cache`: Skip cache entirely
-- `disable_cache`: Disable all caching
-- `no_cache_read`: Don't read from cache
-- `no_cache_write`: Don't write to cache
-
-### New Way (Recommended)
-The new system uses a single `CacheMode` enum:
-- `CacheMode.ENABLED`: Normal caching (read/write)
-- `CacheMode.DISABLED`: No caching at all
-- `CacheMode.READ_ONLY`: Only read from cache
-- `CacheMode.WRITE_ONLY`: Only write to cache
-- `CacheMode.BYPASS`: Skip cache for this operation
-
-## Migration Example
-
-### Old Code (Deprecated)
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-
-async def use_proxy():
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(
-            url="https://www.nbcnews.com/business",
-            bypass_cache=True  # Old way
-        )
-        print(len(result.markdown))
-
-async def main():
-    await use_proxy()
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-### New Code (Recommended)
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode  # Import CacheMode
-
-async def use_proxy():
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(
-            url="https://www.nbcnews.com/business",
-            cache_mode=CacheMode.BYPASS  # New way
-        )
-        print(len(result.markdown))
-
-async def main():
-    await use_proxy()
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-## Common Migration Patterns
-
-Old Flag | New Mode
----------|----------
-`bypass_cache=True` | `cache_mode=CacheMode.BYPASS`
-`disable_cache=True` | `cache_mode=CacheMode.DISABLED`
-`no_cache_read=True` | `cache_mode=CacheMode.WRITE_ONLY`
-`no_cache_write=True` | `cache_mode=CacheMode.READ_ONLY`
-
-## Suppressing Deprecation Warnings
-If you need time to migrate, you can temporarily suppress deprecation warnings:
-```python
-# In your config.py
-SHOW_DEPRECATION_WARNINGS = False
-```

docs/md_v2/basic/content_filtering.md

@@ -1,84 +0,0 @@
-# Content Filtering in Crawl4AI
-
-This guide explains how to use content filtering strategies in Crawl4AI to extract the most relevant information from crawled web pages.  You'll learn how to use the built-in `BM25ContentFilter` and how to create your own custom content filtering strategies.
-
-## Relevance Content Filter
-
-The `RelevanceContentFilter` is an abstract class that provides a common interface for content filtering strategies. Specific filtering algorithms, like `BM25ContentFilter`, inherit from this class and implement the `filter_content` method. This method takes the HTML content as input and returns a list of filtered text blocks.
-
-## BM25 Algorithm
-
-The `BM25ContentFilter` uses the BM25 algorithm, a ranking function used in information retrieval to estimate the relevance of documents to a given search query. In Crawl4AI, this algorithm helps to identify and extract text chunks that are most relevant to the page's metadata or a user-specified query.
-
-### Usage
-
-To use the `BM25ContentFilter`, initialize it and then pass it as the `extraction_strategy` parameter to the `arun` method of the crawler.
-
-```python
-from crawl4ai import AsyncWebCrawler
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-
-async def filter_content(url, query=None):
-    async with AsyncWebCrawler() as crawler:
-        content_filter = BM25ContentFilter(user_query=query)
-        result = await crawler.arun(url=url, content_filter=content_filter, fit_markdown=True) # Set fit_markdown flag to True to trigger BM25 filtering
-        if result.success:
-            print(f"Filtered Content (JSON):\n{result.extracted_content}")
-            print(f"\nFiltered Markdown:\n{result.fit_markdown}") # New field in CrawlResult object
-            print(f"\nFiltered HTML:\n{result.fit_html}") # New field in CrawlResult object. Note that raw HTML may have tags re-organized due to internal parsing.
-        else:
-            print("Error:", result.error_message)
-
-# Example usage:
-asyncio.run(filter_content("https://en.wikipedia.org/wiki/Apple", "fruit nutrition health")) # with query
-asyncio.run(filter_content("https://en.wikipedia.org/wiki/Apple")) # without query, metadata will be used as the query.
-
-```
-
-### Parameters
-
-- **`user_query`**:  (Optional) A string representing the search query. If not provided, the filter extracts relevant metadata (title, description, keywords) from the page and uses that as the query.
-- **`bm25_threshold`**: (Optional, default 1.0)  A float value that controls the threshold for relevance.  Higher values result in stricter filtering, returning only the most relevant text chunks. Lower values result in more lenient filtering.
-
-
-## Fit Markdown Flag
-
-Setting the `fit_markdown` flag to `True` in the `arun` method activates the BM25 content filtering during the crawl. The `fit_markdown` parameter instructs the scraper to extract and clean the HTML, primarily to prepare for a Large Language Model that cannot process large amounts of data. Setting this flag not only improves the quality of the extracted content but also adds the filtered content to two new attributes in the returned  `CrawlResult` object: `fit_markdown` and `fit_html`.
-
-
-## Custom Content Filtering Strategies
-
-You can create your own custom filtering strategies by inheriting from the `RelevantContentFilter` class and implementing the `filter_content` method.  This allows you to tailor the filtering logic to your specific needs.
-
-```python
-from crawl4ai.content_filter_strategy import RelevantContentFilter
-from bs4 import BeautifulSoup, Tag
-from typing import List
-
-class MyCustomFilter(RelevantContentFilter):
-    def filter_content(self, html: str) -> List[str]:
-        soup = BeautifulSoup(html, 'lxml')
-        # Implement custom filtering logic here
-        # Example: extract all paragraphs within divs with class "article-body"
-        filtered_paragraphs = []
-        for tag in soup.select("div.article-body p"):
-            if isinstance(tag, Tag):
-                filtered_paragraphs.append(str(tag)) # Add the cleaned HTML element.  
-        return filtered_paragraphs
-
-
-
-async def custom_filter_demo(url: str):
-    async with AsyncWebCrawler() as crawler:
-        custom_filter = MyCustomFilter()
-        result = await crawler.arun(url, content_filter=custom_filter)
-        if result.success:
-            print(result.extracted_content)
-
-```
-
-This example demonstrates extracting paragraphs from a specific div class.  You can customize this logic to implement different filtering strategies, use regular expressions, analyze text density, or apply other relevant techniques.
-
-## Conclusion
-
-Content filtering strategies provide a powerful way to refine the output of your crawls. By using `BM25ContentFilter` or creating custom strategies, you can focus on the most pertinent information and improve the efficiency of your data processing pipeline.

docs/md_v2/basic/docker-deploymeny.md

@@ -7,325 +7,66 @@ Crawl4AI provides official Docker images for easy deployment and scalability. Th
 Pull and run the basic version:
 
 ```bash
-# Basic run without security
 docker pull unclecode/crawl4ai:basic
 docker run -p 11235:11235 unclecode/crawl4ai:basic
-
-# Run with API security enabled
-docker run -p 11235:11235 -e CRAWL4AI_API_TOKEN=your_secret_token unclecode/crawl4ai:basic
 ```
 
-## Running with Docker Compose 🐳
-
-### Use Docker Compose (From Local Dockerfile or Docker Hub)
-
-Crawl4AI provides flexibility to use Docker Compose for managing your containerized services. You can either build the image locally from the provided `Dockerfile` or use the pre-built image from Docker Hub.
-
-### **Option 1: Using Docker Compose to Build Locally**
-If you want to build the image locally, use the provided `docker-compose.local.yml` file.
-
-```bash
-docker-compose -f docker-compose.local.yml up -d
-```
-
-This will:
-1. Build the Docker image from the provided `Dockerfile`.
-2. Start the container and expose it on `http://localhost:11235`.
-
----
-
-### **Option 2: Using Docker Compose with Pre-Built Image from Hub**
-If you prefer using the pre-built image on Docker Hub, use the `docker-compose.hub.yml` file.
-
-```bash
-docker-compose -f docker-compose.hub.yml up -d
-```
-
-This will:
-1. Pull the pre-built image `unclecode/crawl4ai:basic` (or `all`, depending on your configuration).
-2. Start the container and expose it on `http://localhost:11235`.
-
----
-
-### **Stopping the Running Services**
-
-To stop the services started via Docker Compose, you can use:
-
-```bash
-docker-compose -f docker-compose.local.yml down
-# OR
-docker-compose -f docker-compose.hub.yml down
-```
-
-If the containers don’t stop and the application is still running, check the running containers:
-
-```bash
-docker ps
-```
-
-Find the `CONTAINER ID` of the running service and stop it forcefully:
-
-```bash
-docker stop <CONTAINER_ID>
-```
-
----
-
-### **Debugging with Docker Compose**
-
-- **Check Logs**: To view the container logs:
-  ```bash
-  docker-compose -f docker-compose.local.yml logs -f
-  ```
-
-- **Remove Orphaned Containers**: If the service is still running unexpectedly:
-  ```bash
-  docker-compose -f docker-compose.local.yml down --remove-orphans
-  ```
-
-- **Manually Remove Network**: If the network is still in use:
-  ```bash
-  docker network ls
-  docker network rm crawl4ai_default
-  ```
-
----
-
-### Why Use Docker Compose?
-
-Docker Compose is the recommended way to deploy Crawl4AI because:
-1. It simplifies multi-container setups.
-2. Allows you to define environment variables, resources, and ports in a single file.
-3. Makes it easier to switch between local development and production-ready images.
-
-For example, your `docker-compose.yml` could include API keys, token settings, and memory limits, making deployment quick and consistent.
-
-
-
-
-## API Security 🔒
-
-### Understanding CRAWL4AI_API_TOKEN
-
-The `CRAWL4AI_API_TOKEN` provides optional security for your Crawl4AI instance:
-
-- If `CRAWL4AI_API_TOKEN` is set: All API endpoints (except `/health`) require authentication
-- If `CRAWL4AI_API_TOKEN` is not set: The API is publicly accessible
-
-```bash
-# Secured Instance
-docker run -p 11235:11235 -e CRAWL4AI_API_TOKEN=your_secret_token unclecode/crawl4ai:all
-
-# Unsecured Instance
-docker run -p 11235:11235 unclecode/crawl4ai:all
-```
-
-### Making API Calls
-
-For secured instances, include the token in all requests:
-
+Test the deployment:
 ```python
 import requests
 
-# Setup headers if token is being used
-api_token = "your_secret_token"  # Same token set in CRAWL4AI_API_TOKEN
-headers = {"Authorization": f"Bearer {api_token}"} if api_token else {}
+# Test health endpoint
+health = requests.get("http://localhost:11235/health")
+print("Health check:", health.json())
 
-# Making authenticated requests
+# Test basic crawl
 response = requests.post(
     "http://localhost:11235/crawl",
-    headers=headers,
     json={
-        "urls": "https://example.com",
+        "urls": "https://www.nbcnews.com/business",
         "priority": 10
     }
 )
-
-# Checking task status
 task_id = response.json()["task_id"]
-status = requests.get(
-    f"http://localhost:11235/task/{task_id}",
-    headers=headers
-)
+print("Task ID:", task_id)
 ```
 
-### Using with Docker Compose
+## Available Images 🏷️
 
-In your `docker-compose.yml`:
-```yaml
-services:
-  crawl4ai:
-    image: unclecode/crawl4ai:all
-    environment:
-      - CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-}  # Optional
-    # ... other configuration
-```
-
-Then either:
-1. Set in `.env` file:
-```env
-CRAWL4AI_API_TOKEN=your_secret_token
-```
-
-2. Or set via command line:
-```bash
-CRAWL4AI_API_TOKEN=your_secret_token docker-compose up
-```
-
-> **Security Note**: If you enable the API token, make sure to keep it secure and never commit it to version control. The token will be required for all API endpoints except the health check endpoint (`/health`).
+- `unclecode/crawl4ai:basic` - Basic web crawling capabilities
+- `unclecode/crawl4ai:all` - Full installation with all features
+- `unclecode/crawl4ai:gpu` - GPU-enabled version for ML features
 
 ## Configuration Options 🔧
 
 ### Environment Variables
 
-You can configure the service using environment variables:
-
 ```bash
-# Basic configuration
 docker run -p 11235:11235 \
     -e MAX_CONCURRENT_TASKS=5 \
+    -e OPENAI_API_KEY=your_key \
     unclecode/crawl4ai:all
+```
 
-# With security and LLM support
+### Volume Mounting
+
+Mount a directory for persistent data:
+```bash
 docker run -p 11235:11235 \
-    -e CRAWL4AI_API_TOKEN=your_secret_token \
-    -e OPENAI_API_KEY=sk-... \
-    -e ANTHROPIC_API_KEY=sk-ant-... \
+    -v $(pwd)/data:/app/data \
     unclecode/crawl4ai:all
 ```
 
-### Using Docker Compose (Recommended) 🐳
+### Resource Limits
 
-Create a `docker-compose.yml`:
-
-```yaml
-version: '3.8'
-
-services:
-  crawl4ai:
-    image: unclecode/crawl4ai:all
-    ports:
-      - "11235:11235"
-    environment:
-      - CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-}  # Optional API security
-      - MAX_CONCURRENT_TASKS=5
-      # LLM Provider Keys
-      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
-      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
-    volumes:
-      - /dev/shm:/dev/shm
-    deploy:
-      resources:
-        limits:
-          memory: 4G
-        reservations:
-          memory: 1G
-```
-
-You can run it in two ways:
-
-1. Using environment variables directly:
+Control container resources:
 ```bash
-CRAWL4AI_API_TOKEN=secret123 OPENAI_API_KEY=sk-... docker-compose up
+docker run -p 11235:11235 \
+    --memory=4g \
+    --cpus=2 \
+    unclecode/crawl4ai:all
 ```
 
-2. Using a `.env` file (recommended):
-Create a `.env` file in the same directory:
-```env
-# API Security (optional)
-CRAWL4AI_API_TOKEN=your_secret_token
-
-# LLM Provider Keys
-OPENAI_API_KEY=sk-...
-ANTHROPIC_API_KEY=sk-ant-...
-
-# Other Configuration
-MAX_CONCURRENT_TASKS=5
-```
-
-Then simply run:
-```bash
-docker-compose up
-```
-
-### Testing the Deployment 🧪
-
-```python
-import requests
-
-# For unsecured instances
-def test_unsecured():
-    # Health check
-    health = requests.get("http://localhost:11235/health")
-    print("Health check:", health.json())
-
-    # Basic crawl
-    response = requests.post(
-        "http://localhost:11235/crawl",
-        json={
-            "urls": "https://www.nbcnews.com/business",
-            "priority": 10
-        }
-    )
-    task_id = response.json()["task_id"]
-    print("Task ID:", task_id)
-
-# For secured instances
-def test_secured(api_token):
-    headers = {"Authorization": f"Bearer {api_token}"}
-    
-    # Basic crawl with authentication
-    response = requests.post(
-        "http://localhost:11235/crawl",
-        headers=headers,
-        json={
-            "urls": "https://www.nbcnews.com/business",
-            "priority": 10
-        }
-    )
-    task_id = response.json()["task_id"]
-    print("Task ID:", task_id)
-```
-
-### LLM Extraction Example 🤖
-
-When you've configured your LLM provider keys (via environment variables or `.env`), you can use LLM extraction:
-
-```python
-request = {
-    "urls": "https://example.com",
-    "extraction_config": {
-        "type": "llm",
-        "params": {
-            "provider": "openai/gpt-4",
-            "instruction": "Extract main topics from the page"
-        }
-    }
-}
-
-# Make the request (add headers if using API security)
-response = requests.post("http://localhost:11235/crawl", json=request)
-```
-
-> **Note**: Remember to add `.env` to your `.gitignore` to keep your API keys secure!
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 ## Usage Examples 📝
 
 ### Basic Crawling

docs/md_v2/basic/file-download.md

@@ -1,148 +0,0 @@
-# Download Handling in Crawl4AI
-
-This guide explains how to use Crawl4AI to handle file downloads during crawling.  You'll learn how to trigger downloads, specify download locations, and access downloaded files.
-
-## Enabling Downloads
-
-By default, Crawl4AI does not download files. To enable downloads, set the `accept_downloads` parameter to `True` in either the `AsyncWebCrawler` constructor or the `arun` method.
-
-```python
-from crawl4ai import AsyncWebCrawler
-
-async def main():
-    async with AsyncWebCrawler(accept_downloads=True) as crawler:  # Globally enable downloads
-        # ... your crawling logic ...
-
-asyncio.run(main())
-```
-
-Or, enable it for a specific crawl:
-
-```python
-async def main():
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(url="...", accept_downloads=True)
-        # ...
-```
-
-## Specifying Download Location
-
-You can specify the download directory using the `downloads_path` parameter. If not provided, Crawl4AI creates a "downloads" directory inside the `.crawl4ai` folder in your home directory.
-
-```python
-import os
-from pathlib import Path
-
-# ... inside your crawl function:
-
-downloads_path = os.path.join(os.getcwd(), "my_downloads")  # Custom download path
-os.makedirs(downloads_path, exist_ok=True)
-
-result = await crawler.arun(url="...", downloads_path=downloads_path, accept_downloads=True)
-
-# ...
-```
-
-If you are setting it globally, provide the path to the AsyncWebCrawler:
-```python
-async def crawl_with_downloads(url: str, download_path: str):
-    async with AsyncWebCrawler(
-        accept_downloads=True,
-        downloads_path=download_path, # or set it on arun
-        verbose=True
-    ) as crawler:
-        result = await crawler.arun(url=url) # you still need to enable downloads per call.
-        # ...
-```
-
-
-
-## Triggering Downloads
-
-Downloads are typically triggered by user interactions on a web page (e.g., clicking a download button).  You can simulate these actions with the `js_code` parameter, injecting JavaScript code to be executed within the browser context.  The `wait_for` parameter might also be crucial to allowing sufficient time for downloads to initiate before the crawler proceeds.
-
-```python
-result = await crawler.arun(
-    url="https://www.python.org/downloads/",
-    js_code="""
-        // Find and click the first Windows installer link
-        const downloadLink = document.querySelector('a[href$=".exe"]');
-        if (downloadLink) {
-            downloadLink.click();
-        }
-    """,
-    wait_for=5  # Wait for 5 seconds for the download to start
-)
-```
-
-## Accessing Downloaded Files
-
-Downloaded file paths are stored in the `downloaded_files` attribute of the returned  `CrawlResult`  object.  This is a list of strings, with each string representing the absolute path to a downloaded file.
-
-```python
-if result.downloaded_files:
-    print("Downloaded files:")
-    for file_path in result.downloaded_files:
-        print(f"- {file_path}")
-        # Perform operations with downloaded files, e.g., check file size
-        file_size = os.path.getsize(file_path)
-        print(f"- File size: {file_size} bytes")
-else:
-    print("No files downloaded.")
-```
-
-
-##  Example: Downloading Multiple Files
-
-```python
-import asyncio
-import os
-from pathlib import Path
-from crawl4ai import AsyncWebCrawler
-
-async def download_multiple_files(url: str, download_path: str):
-
-    async with AsyncWebCrawler(
-        accept_downloads=True,
-        downloads_path=download_path,
-        verbose=True
-    ) as crawler:
-        result = await crawler.arun(
-            url=url,
-            js_code="""
-            // Trigger multiple downloads (example)
-            const downloadLinks = document.querySelectorAll('a[download]'); // Or a more specific selector
-            for (const link of downloadLinks) {
-                link.click();
-                await new Promise(r => setTimeout(r, 2000)); // Add a small delay between clicks if needed
-            }
-            """,
-            wait_for=10 # Adjust the timeout to match the expected time for all downloads to start
-        )
-
-        if result.downloaded_files:
-            print("Downloaded files:")
-            for file in result.downloaded_files:
-                print(f"- {file}")
-        else:
-            print("No files downloaded.")
-            
-
-# Example usage
-download_path = os.path.join(Path.home(), ".crawl4ai", "downloads")
-os.makedirs(download_path, exist_ok=True) # Create directory if it doesn't exist
-
-
-asyncio.run(download_multiple_files("https://www.python.org/downloads/windows/", download_path))
-```
-
-## Important Considerations
-
-- **Browser Context:** Downloads are managed within the browser context.  Ensure your `js_code` correctly targets the download triggers on the specific web page.
-- **Waiting:**  Use `wait_for` to manage the timing of the crawl process if immediate download might not occur.
-- **Error Handling:** Implement proper error handling to gracefully manage failed downloads or incorrect file paths.
-- **Security:** Downloaded files should be scanned for potential security threats before use.
-
-
-
-This guide provides a foundation for handling downloads with Crawl4AI. You can adapt these techniques to manage downloads in various scenarios and integrate them into more complex crawling workflows.

docs/md_v2/basic/installation.md

@@ -58,51 +58,6 @@ crawl4ai-download-models
 
 This is optional but will boost the performance and speed of the crawler. You only need to do this once after installation.
 
-## Playwright Installation Note for Ubuntu
-
-If you encounter issues with Playwright installation on Ubuntu, you may need to install additional dependencies:
-
-```bash
-sudo apt-get install -y \
-    libwoff1 \
-    libopus0 \
-    libwebp7 \
-    libwebpdemux2 \
-    libenchant-2-2 \
-    libgudev-1.0-0 \
-    libsecret-1-0 \
-    libhyphen0 \
-    libgdk-pixbuf2.0-0 \
-    libegl1 \
-    libnotify4 \
-    libxslt1.1 \
-    libevent-2.1-7 \
-    libgles2 \
-    libxcomposite1 \
-    libatk1.0-0 \
-    libatk-bridge2.0-0 \
-    libepoxy0 \
-    libgtk-3-0 \
-    libharfbuzz-icu0 \
-    libgstreamer-gl1.0-0 \
-    libgstreamer-plugins-bad1.0-0 \
-    gstreamer1.0-plugins-good \
-    gstreamer1.0-plugins-bad \
-    libxt6 \
-    libxaw7 \
-    xvfb \
-    fonts-noto-color-emoji \
-    libfontconfig \
-    libfreetype6 \
-    xfonts-cyrillic \
-    xfonts-scalable \
-    fonts-liberation \
-    fonts-ipafont-gothic \
-    fonts-wqy-zenhei \
-    fonts-tlwg-loma-otf \
-    fonts-freefont-ttf
-```
-
 ## Option 2: Using Docker (Coming Soon)
 
 Docker support for Crawl4AI is currently in progress and will be available soon. This will allow you to run Crawl4AI in a containerized environment, ensuring consistency across different systems.

docs/md_v2/basic/prefix-based-input.md

@@ -1,235 +0,0 @@
-# Prefix-Based Input Handling in Crawl4AI
-
-This guide will walk you through using the Crawl4AI library to crawl web pages, local HTML files, and raw HTML strings. We'll demonstrate these capabilities using a Wikipedia page as an example.
-
-## Table of Contents
-- [Prefix-Based Input Handling in Crawl4AI](#prefix-based-input-handling-in-crawl4ai)
-  - [Table of Contents](#table-of-contents)
-    - [Crawling a Web URL](#crawling-a-web-url)
-    - [Crawling a Local HTML File](#crawling-a-local-html-file)
-    - [Crawling Raw HTML Content](#crawling-raw-html-content)
-  - [Complete Example](#complete-example)
-    - [**How It Works**](#how-it-works)
-    - [**Running the Example**](#running-the-example)
-  - [Conclusion](#conclusion)
-
----
-
-
-### Crawling a Web URL
-
-To crawl a live web page, provide the URL starting with `http://` or `https://`.
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-
-async def crawl_web():
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(url="https://en.wikipedia.org/wiki/apple", bypass_cache=True)
-        if result.success:
-            print("Markdown Content:")
-            print(result.markdown)
-        else:
-            print(f"Failed to crawl: {result.error_message}")
-
-asyncio.run(crawl_web())
-```
-
-### Crawling a Local HTML File
-
-To crawl a local HTML file, prefix the file path with `file://`.
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-
-async def crawl_local_file():
-    local_file_path = "/path/to/apple.html"  # Replace with your file path
-    file_url = f"file://{local_file_path}"
-    
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(url=file_url, bypass_cache=True)
-        if result.success:
-            print("Markdown Content from Local File:")
-            print(result.markdown)
-        else:
-            print(f"Failed to crawl local file: {result.error_message}")
-
-asyncio.run(crawl_local_file())
-```
-
-### Crawling Raw HTML Content
-
-To crawl raw HTML content, prefix the HTML string with `raw:`.
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler
-
-async def crawl_raw_html():
-    raw_html = "<html><body><h1>Hello, World!</h1></body></html>"
-    raw_html_url = f"raw:{raw_html}"
-    
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        result = await crawler.arun(url=raw_html_url, bypass_cache=True)
-        if result.success:
-            print("Markdown Content from Raw HTML:")
-            print(result.markdown)
-        else:
-            print(f"Failed to crawl raw HTML: {result.error_message}")
-
-asyncio.run(crawl_raw_html())
-```
-
----
-
-## Complete Example
-
-Below is a comprehensive script that:
-1. **Crawls the Wikipedia page for "Apple".**
-2. **Saves the HTML content to a local file (`apple.html`).**
-3. **Crawls the local HTML file and verifies the markdown length matches the original crawl.**
-4. **Crawls the raw HTML content from the saved file and verifies consistency.**
-
-```python
-import os
-import sys
-import asyncio
-from pathlib import Path
-
-# Adjust the parent directory to include the crawl4ai module
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-
-from crawl4ai import AsyncWebCrawler
-
-async def main():
-    # Define the URL to crawl
-    wikipedia_url = "https://en.wikipedia.org/wiki/apple"
-    
-    # Define the path to save the HTML file
-    # Save the file in the same directory as the script
-    script_dir = Path(__file__).parent
-    html_file_path = script_dir / "apple.html"
-    
-    async with AsyncWebCrawler(verbose=True) as crawler:
-        print("\n=== Step 1: Crawling the Wikipedia URL ===")
-        # Crawl the Wikipedia URL
-        result = await crawler.arun(url=wikipedia_url, bypass_cache=True)
-        
-        # Check if crawling was successful
-        if not result.success:
-            print(f"Failed to crawl {wikipedia_url}: {result.error_message}")
-            return
-        
-        # Save the HTML content to a local file
-        with open(html_file_path, 'w', encoding='utf-8') as f:
-            f.write(result.html)
-        print(f"Saved HTML content to {html_file_path}")
-        
-        # Store the length of the generated markdown
-        web_crawl_length = len(result.markdown)
-        print(f"Length of markdown from web crawl: {web_crawl_length}\n")
-        
-        print("=== Step 2: Crawling from the Local HTML File ===")
-        # Construct the file URL with 'file://' prefix
-        file_url = f"file://{html_file_path.resolve()}"
-        
-        # Crawl the local HTML file
-        local_result = await crawler.arun(url=file_url, bypass_cache=True)
-        
-        # Check if crawling was successful
-        if not local_result.success:
-            print(f"Failed to crawl local file {file_url}: {local_result.error_message}")
-            return
-        
-        # Store the length of the generated markdown from local file
-        local_crawl_length = len(local_result.markdown)
-        print(f"Length of markdown from local file crawl: {local_crawl_length}")
-        
-        # Compare the lengths
-        assert web_crawl_length == local_crawl_length, (
-            f"Markdown length mismatch: Web crawl ({web_crawl_length}) != Local file crawl ({local_crawl_length})"
-        )
-        print("✅ Markdown length matches between web crawl and local file crawl.\n")
-        
-        print("=== Step 3: Crawling Using Raw HTML Content ===")
-        # Read the HTML content from the saved file
-        with open(html_file_path, 'r', encoding='utf-8') as f:
-            raw_html_content = f.read()
-        
-        # Prefix the raw HTML content with 'raw:'
-        raw_html_url = f"raw:{raw_html_content}"
-        
-        # Crawl using the raw HTML content
-        raw_result = await crawler.arun(url=raw_html_url, bypass_cache=True)
-        
-        # Check if crawling was successful
-        if not raw_result.success:
-            print(f"Failed to crawl raw HTML content: {raw_result.error_message}")
-            return
-        
-        # Store the length of the generated markdown from raw HTML
-        raw_crawl_length = len(raw_result.markdown)
-        print(f"Length of markdown from raw HTML crawl: {raw_crawl_length}")
-        
-        # Compare the lengths
-        assert web_crawl_length == raw_crawl_length, (
-            f"Markdown length mismatch: Web crawl ({web_crawl_length}) != Raw HTML crawl ({raw_crawl_length})"
-        )
-        print("✅ Markdown length matches between web crawl and raw HTML crawl.\n")
-        
-        print("All tests passed successfully!")
-        
-    # Clean up by removing the saved HTML file
-    if html_file_path.exists():
-        os.remove(html_file_path)
-        print(f"Removed the saved HTML file: {html_file_path}")
-
-# Run the main function
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-### **How It Works**
-
-1. **Step 1: Crawl the Web URL**
-   - Crawls `https://en.wikipedia.org/wiki/apple`.
-   - Saves the HTML content to `apple.html`.
-   - Records the length of the generated markdown.
-
-2. **Step 2: Crawl from the Local HTML File**
-   - Uses the `file://` prefix to crawl `apple.html`.
-   - Ensures the markdown length matches the original web crawl.
-
-3. **Step 3: Crawl Using Raw HTML Content**
-   - Reads the HTML from `apple.html`.
-   - Prefixes it with `raw:` and crawls.
-   - Verifies the markdown length matches the previous results.
-
-4. **Cleanup**
-   - Deletes the `apple.html` file after testing.
-
-### **Running the Example**
-
-1. **Save the Script:**
-   - Save the above code as `test_crawl4ai.py` in your project directory.
-
-2. **Execute the Script:**
-   - Run the script using:
-     ```bash
-     python test_crawl4ai.py
-     ```
-
-3. **Observe the Output:**
-   - The script will print logs detailing each step.
-   - Assertions ensure consistency across different crawling methods.
-   - Upon success, it confirms that all markdown lengths match.
-
----
-
-## Conclusion
-
-With the new prefix-based input handling in **Crawl4AI**, you can effortlessly crawl web URLs, local HTML files, and raw HTML strings using a unified `url` parameter. This enhancement simplifies the API usage and provides greater flexibility for diverse crawling scenarios.
-

docs/md_v2/basic/quickstart.md

@@ -8,7 +8,7 @@ First, let's import the necessary modules and create an instance of `AsyncWebCra
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, CasheMode
+from crawl4ai import AsyncWebCrawler
 
 async def main():
     async with AsyncWebCrawler(verbose=True) as crawler:
@@ -42,7 +42,7 @@ async def capture_and_save_screenshot(url: str, output_path: str):
         result = await crawler.arun(
             url=url,
             screenshot=True,
-            cache_mode=CacheMode.BYPASS
+            bypass_cache=True
         )
         
         if result.success and result.screenshot:
@@ -62,15 +62,15 @@ Crawl4AI supports multiple browser engines. Here's how to use different browsers
 ```python
 # Use Firefox
 async with AsyncWebCrawler(browser_type="firefox", verbose=True, headless=True) as crawler:
-    result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
+    result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
 
 # Use WebKit
 async with AsyncWebCrawler(browser_type="webkit", verbose=True, headless=True) as crawler:
-    result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
+    result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
 
 # Use Chromium (default)
 async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
-    result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
+    result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
 ```
 
 ### User Simulation 🎭
@@ -81,7 +81,7 @@ Simulate real user behavior to avoid detection:
 async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
     result = await crawler.arun(
         url="YOUR-URL-HERE",
-        cache_mode=CacheMode.BYPASS,
+        bypass_cache=True,
         simulate_user=True,  # Causes random mouse movements and clicks
         override_navigator=True  # Makes the browser appear more like a real user
     )
@@ -99,7 +99,7 @@ async def main():
         print(f"First crawl result: {result1.markdown[:100]}...")
 
         # Force to crawl again
-        result2 = await crawler.arun(url="https://www.nbcnews.com/business", cache_mode=CacheMode.BYPASS)
+        result2 = await crawler.arun(url="https://www.nbcnews.com/business", bypass_cache=True)
         print(f"Second crawl result: {result2.markdown[:100]}...")
 
 asyncio.run(main())
@@ -189,7 +189,7 @@ extraction_strategy = LLMExtractionStrategy(
 async with AsyncWebCrawler() as crawler:
     result = await crawler.arun(
         url="https://paulgraham.com/love.html",
-        cache_mode=CacheMode.BYPASS,
+        bypass_cache=True,
         extraction_strategy=extraction_strategy
     )
 ```
@@ -239,7 +239,7 @@ async def crawl_dynamic_content():
                 js_code=js_next_page if page > 0 else None,
                 wait_for=wait_for if page > 0 else None,
                 js_only=page > 0,
-                cache_mode=CacheMode.BYPASS,
+                bypass_cache=True,
                 headless=False,
             )
 
@@ -254,7 +254,7 @@ Remove overlay elements and fit content appropriately:
 async with AsyncWebCrawler(headless=False) as crawler:
     result = await crawler.arun(
         url="your-url-here",
-        cache_mode=CacheMode.BYPASS,
+        bypass_cache=True,
         word_count_threshold=10,
         remove_overlay_elements=True,
         screenshot=True
@@ -282,7 +282,7 @@ async with AsyncWebCrawler() as crawler:
     result = await crawler.arun(
         url="https://www.nbcnews.com/business",
         word_count_threshold=0,
-        cache_mode=CacheMode.BYPASS,
+        bypass_cache=True,
         verbose=False,
     )
     end = time.time()

docs/md_v2/basic/simple-crawling.md

@@ -12,9 +12,7 @@ from crawl4ai import AsyncWebCrawler
 
 async def main():
     async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://example.com"
-        )
+        result = await crawler.arun(url="https://example.com")
         print(result.markdown)  # Print clean markdown content
 
 if __name__ == "__main__":
@@ -26,7 +24,7 @@ if __name__ == "__main__":
 The `arun()` method returns a `CrawlResult` object with several useful properties. Here's a quick overview (see [CrawlResult](../api/crawl-result.md) for complete details):
 
 ```python
-result = await crawler.arun(url="https://example.com", fit_markdown=True)
+result = await crawler.arun(url="https://example.com")
 
 # Different content formats
 print(result.html)         # Raw HTML
@@ -83,7 +81,7 @@ Here's a more comprehensive example showing common usage patterns:
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, CacheMode
+from crawl4ai import AsyncWebCrawler
 
 async def main():
     async with AsyncWebCrawler(verbose=True) as crawler:
@@ -99,7 +97,7 @@ async def main():
             remove_overlay_elements=True,
             
             # Cache control
-            cache_mode=CacheMode.ENABLE  # Use cache if available
+            bypass_cache=False  # Use cache if available
         )
         
         if result.success:

docs/md_v2/tutorial/episode_11_2_Extraction_Strategies_LLM.md

@@ -52,7 +52,7 @@ Here’s a comprehensive outline for the **LLM Extraction Strategy** video, cove
                      extraction_type="schema",
                      instruction="Extract model names and fees for input and output tokens from the page."
                  ),
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```
@@ -98,7 +98,7 @@ Here’s a comprehensive outline for the **LLM Extraction Strategy** video, cove
              result = await crawler.arun(
                  url="https://example.com/some-article",
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```

docs/md_v2/tutorial/episode_11_3_Extraction_Strategies_Cosine.md

@@ -55,7 +55,7 @@ Here’s a structured outline for the **Cosine Similarity Strategy** video, cove
              result = await crawler.arun(
                  url=url,
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```
@@ -103,7 +103,7 @@ Here’s a structured outline for the **Cosine Similarity Strategy** video, cove
              result = await crawler.arun(
                  url=url,
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```

docs/md_v2/tutorial/tutorial.md

@@ -26,7 +26,7 @@ Here's a condensed outline of the **Installation and Setup** video content:
    - Walk through a simple test script to confirm the setup:
       ```python
       import asyncio
-      from crawl4ai import AsyncWebCrawler, CacheMode
+      from crawl4ai import AsyncWebCrawler
       
       async def main():
           async with AsyncWebCrawler(verbose=True) as crawler:
@@ -1093,7 +1093,7 @@ Here’s a comprehensive outline for the **LLM Extraction Strategy** video, cove
                      extraction_type="schema",
                      instruction="Extract model names and fees for input and output tokens from the page."
                  ),
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```
@@ -1139,7 +1139,7 @@ Here’s a comprehensive outline for the **LLM Extraction Strategy** video, cove
              result = await crawler.arun(
                  url="https://example.com/some-article",
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```
@@ -1248,7 +1248,7 @@ Here’s a structured outline for the **Cosine Similarity Strategy** video, cove
              result = await crawler.arun(
                  url=url,
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```
@@ -1296,7 +1296,7 @@ Here’s a structured outline for the **Cosine Similarity Strategy** video, cove
              result = await crawler.arun(
                  url=url,
                  extraction_strategy=extraction_strategy,
-                 cache_mode=CacheMode.BYPASS
+                 bypass_cache=True
              )
              print(result.extracted_content)
      ```

main.py

@@ -10,8 +10,6 @@ from fastapi.exceptions import RequestValidationError
 from starlette.middleware.base import BaseHTTPMiddleware
 from starlette.responses import FileResponse
 from fastapi.responses import RedirectResponse
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
-from fastapi import Depends, Security
 
 from pydantic import BaseModel, HttpUrl, Field
 from typing import Optional, List, Dict, Any, Union
@@ -25,8 +23,7 @@ import logging
 from enum import Enum
 from dataclasses import dataclass
 import json
-from crawl4ai import AsyncWebCrawler, CrawlResult, CacheMode
-from crawl4ai.config import MIN_WORD_THRESHOLD
+from crawl4ai import AsyncWebCrawler, CrawlResult
 from crawl4ai.extraction_strategy import (
     LLMExtractionStrategy,
     CosineStrategy,
@@ -54,31 +51,18 @@ class ExtractionConfig(BaseModel):
     type: CrawlerType
     params: Dict[str, Any] = {}
 
-class ChunkingStrategy(BaseModel):
-    type: str
-    params: Dict[str, Any] = {}
-
-class ContentFilter(BaseModel):
-    type: str = "bm25"
-    params: Dict[str, Any] = {}
-
 class CrawlRequest(BaseModel):
     urls: Union[HttpUrl, List[HttpUrl]]
-    word_count_threshold: int = MIN_WORD_THRESHOLD
     extraction_config: Optional[ExtractionConfig] = None
-    chunking_strategy: Optional[ChunkingStrategy] = None
-    content_filter: Optional[ContentFilter] = None
+    crawler_params: Dict[str, Any] = {}
+    priority: int = Field(default=5, ge=1, le=10)
+    ttl: Optional[int] = 3600
     js_code: Optional[List[str]] = None
     wait_for: Optional[str] = None
     css_selector: Optional[str] = None
     screenshot: bool = False
     magic: bool = False
     extra: Optional[Dict[str, Any]] = {}
-    session_id: Optional[str] = None
-    cache_mode: Optional[CacheMode] = CacheMode.ENABLED
-    priority: int = Field(default=5, ge=1, le=10)
-    ttl: Optional[int] = 3600    
-    crawler_params: Dict[str, Any] = {}
 
 @dataclass
 class TaskInfo:
@@ -292,15 +276,12 @@ class CrawlerService:
                     if isinstance(request.urls, list):
                         results = await crawler.arun_many(
                             urls=[str(url) for url in request.urls],
-                            word_count_threshold=MIN_WORD_THRESHOLD,
                             extraction_strategy=extraction_strategy,
                             js_code=request.js_code,
                             wait_for=request.wait_for,
                             css_selector=request.css_selector,
                             screenshot=request.screenshot,
                             magic=request.magic,
-                            session_id=request.session_id,
-                            cache_mode=request.cache_mode,
                             **request.extra,
                         )
                     else:
@@ -312,8 +293,6 @@ class CrawlerService:
                             css_selector=request.css_selector,
                             screenshot=request.screenshot,
                             magic=request.magic,
-                            session_id=request.session_id,
-                            cache_mode=request.cache_mode,
                             **request.extra,
                         )
 
@@ -342,27 +321,7 @@ app.add_middleware(
 
 # Mount the pages directory as a static directory
 app.mount("/pages", StaticFiles(directory=__location__ + "/pages"), name="pages")
-
-# API token security
-security = HTTPBearer()
-CRAWL4AI_API_TOKEN = os.getenv("CRAWL4AI_API_TOKEN") or "test_api_code"
-
-async def verify_token(credentials: HTTPAuthorizationCredentials = Security(security)):
-    if not CRAWL4AI_API_TOKEN:
-        return credentials  # No token verification if CRAWL4AI_API_TOKEN is not set
-    if credentials.credentials != CRAWL4AI_API_TOKEN:
-        raise HTTPException(status_code=401, detail="Invalid token")
-    return credentials
-
-# Helper function to conditionally apply security
-def secure_endpoint():
-    return Depends(verify_token) if CRAWL4AI_API_TOKEN else None
-
-# Check if site directory exists
-if os.path.exists(__location__ + "/site"):
-    # Mount the site directory as a static directory
-    app.mount("/mkdocs", StaticFiles(directory="site", html=True), name="mkdocs")
-
+app.mount("/mkdocs", StaticFiles(directory="site", html=True), name="mkdocs")
 site_templates = Jinja2Templates(directory=__location__ + "/site")
 templates = Jinja2Templates(directory=__location__ + "/pages")
 
@@ -378,18 +337,15 @@ async def shutdown_event():
 
 @app.get("/")
 def read_root():
-    if os.path.exists(__location__ + "/site"):
-        return RedirectResponse(url="/mkdocs")
-    # Return a json response
-    return {"message": "Crawl4AI API service is running"}
+    return RedirectResponse(url="/mkdocs")
 
 
-@app.post("/crawl", dependencies=[Depends(verify_token)])
+@app.post("/crawl")
 async def crawl(request: CrawlRequest) -> Dict[str, str]:
     task_id = await crawler_service.submit_task(request)
     return {"task_id": task_id}
 
-@app.get("/task/{task_id}", dependencies=[Depends(verify_token)])
+@app.get("/task/{task_id}")
 async def get_task_status(task_id: str):
     task_info = crawler_service.task_manager.get_task(task_id)
     if not task_info:
@@ -411,71 +367,6 @@ async def get_task_status(task_id: str):
 
     return response
 
-@app.post("/crawl_sync", dependencies=[Depends(verify_token)])
-async def crawl_sync(request: CrawlRequest) -> Dict[str, Any]:
-    task_id = await crawler_service.submit_task(request)
-    
-    # Wait up to 60 seconds for task completion
-    for _ in range(60):
-        task_info = crawler_service.task_manager.get_task(task_id)
-        if not task_info:
-            raise HTTPException(status_code=404, detail="Task not found")
-            
-        if task_info.status == TaskStatus.COMPLETED:
-            # Return same format as /task/{task_id} endpoint
-            if isinstance(task_info.result, list):
-                return {"status": task_info.status, "results": [result.dict() for result in task_info.result]}
-            return {"status": task_info.status, "result": task_info.result.dict()}
-            
-        if task_info.status == TaskStatus.FAILED:
-            raise HTTPException(status_code=500, detail=task_info.error)
-            
-        await asyncio.sleep(1)
-    
-    # If we get here, task didn't complete within timeout
-    raise HTTPException(status_code=408, detail="Task timed out")
-
-@app.post("/crawl_direct", dependencies=[Depends(verify_token)])
-async def crawl_direct(request: CrawlRequest) -> Dict[str, Any]:
-    try:
-        crawler = await crawler_service.crawler_pool.acquire(**request.crawler_params)
-        extraction_strategy = crawler_service._create_extraction_strategy(request.extraction_config)
-        
-        try:
-            if isinstance(request.urls, list):
-                results = await crawler.arun_many(
-                    urls=[str(url) for url in request.urls],
-                    extraction_strategy=extraction_strategy,
-                    js_code=request.js_code,
-                    wait_for=request.wait_for,
-                    css_selector=request.css_selector,
-                    screenshot=request.screenshot,
-                    magic=request.magic,
-                    cache_mode=request.cache_mode,
-                    session_id=request.session_id,
-                    **request.extra,
-                )
-                return {"results": [result.dict() for result in results]}
-            else:
-                result = await crawler.arun(
-                    url=str(request.urls),
-                    extraction_strategy=extraction_strategy,
-                    js_code=request.js_code,
-                    wait_for=request.wait_for,
-                    css_selector=request.css_selector,
-                    screenshot=request.screenshot,
-                    magic=request.magic,
-                    cache_mode=request.cache_mode,
-                    session_id=request.session_id,
-                    **request.extra,
-                )
-                return {"result": result.dict()}
-        finally:
-            await crawler_service.crawler_pool.release(crawler)
-    except Exception as e:
-        logger.error(f"Error in direct crawl: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-    
 @app.get("/health")
 async def health_check():
     available_slots = await crawler_service.resource_monitor.get_available_slots()

mkdocs.yml

@@ -17,7 +17,6 @@ nav:
     - 'Browser Configuration': 'basic/browser-config.md'
     - 'Page Interaction': 'basic/page-interaction.md'
     - 'Content Selection': 'basic/content-selection.md'
-    - 'Cache Modes': 'basic/cache-modes.md'
 
   - Advanced:
     - 'Content Processing': 'advanced/content-processing.md'

pages/app.css

@@ -0,0 +1,131 @@
+:root {
+    --ifm-font-size-base: 100%;
+    --ifm-line-height-base: 1.65;
+    --ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans, sans-serif,
+        BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+}
+html {
+    -webkit-font-smoothing: antialiased;
+    -webkit-text-size-adjust: 100%;
+    text-size-adjust: 100%;
+    font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
+}
+body {
+    background-color: #1a202c;
+    color: #fff;
+}
+.tab-content {
+    max-height: 400px;
+    overflow: auto;
+}
+pre {
+    white-space: pre-wrap;
+    font-size: 14px;
+}
+pre code {
+    width: 100%;
+}
+
+/* Custom styling for docs-item class and Markdown generated elements */
+.docs-item {
+    background-color: #2d3748; /* bg-gray-800 */
+    padding: 1rem; /* p-4 */
+    border-radius: 0.375rem; /* rounded */
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* shadow-md */
+    margin-bottom: 1rem; /* space between items */
+    line-height: 1.5; /* leading-normal */
+}
+
+.docs-item h3,
+.docs-item h4 {
+    color: #ffffff; /* text-white */
+    font-size: 1.25rem; /* text-xl */
+    font-weight: 700; /* font-bold */
+    margin-bottom: 0.5rem; /* mb-2 */
+}
+.docs-item h4 {
+    font-size: 1rem; /* text-xl */
+}
+
+.docs-item p {
+    color: #e2e8f0; /* text-gray-300 */
+    margin-bottom: 0.5rem; /* mb-2 */
+}
+
+.docs-item code {
+    background-color: #1a202c; /* bg-gray-900 */
+    color: #e2e8f0; /* text-gray-300 */
+    padding: 0.25rem 0.5rem; /* px-2 py-1 */
+    border-radius: 0.25rem; /* rounded */
+    font-size: 0.875rem; /* text-sm */
+}
+
+.docs-item pre {
+    background-color: #1a202c; /* bg-gray-900 */
+    color: #e2e8f0; /* text-gray-300 */
+    padding: 0.5rem; /* p-2 */
+    border-radius: 0.375rem; /* rounded */
+    overflow: auto; /* overflow-auto */
+    margin-bottom: 0.5rem; /* mb-2 */
+}
+
+.docs-item div {
+    color: #e2e8f0; /* text-gray-300 */
+    font-size: 1rem; /* prose prose-sm */
+    line-height: 1.25rem; /* line-height for readability */
+}
+
+/* Adjustments to make prose class more suitable for dark mode */
+.prose {
+    max-width: none; /* max-w-none */
+}
+
+.prose p,
+.prose ul {
+    margin-bottom: 1rem; /* mb-4 */
+}
+
+.prose code {
+    /* background-color: #4a5568; */ /* bg-gray-700 */
+    color: #65a30d; /* text-white */
+    padding: 0.25rem 0.5rem; /* px-1 py-0.5 */
+    border-radius: 0.25rem; /* rounded */
+    display: inline-block; /* inline-block */
+}
+
+.prose pre {
+    background-color: #1a202c; /* bg-gray-900 */
+    color: #ffffff; /* text-white */
+    padding: 0.5rem; /* p-2 */
+    border-radius: 0.375rem; /* rounded */
+}
+
+.prose h3 {
+    color: #65a30d; /* text-white */
+    font-size: 1.25rem; /* text-xl */
+    font-weight: 700; /* font-bold */
+    margin-bottom: 0.5rem; /* mb-2 */
+}
+
+body {
+    background-color: #1a1a1a;
+    color: #b3ff00;
+}
+.sidebar {
+    color: #b3ff00;
+    border-right: 1px solid #333;
+}
+.sidebar a {
+    color: #b3ff00;
+    text-decoration: none;
+}
+.sidebar a:hover {
+    background-color: #555;
+}
+.content-section {
+    display: none;
+}
+.content-section.active {
+    display: block;
+}

pages/app.js

@@ -0,0 +1,356 @@
+// JavaScript to manage dynamic form changes and logic
+document.getElementById("extraction-strategy-select").addEventListener("change", function () {
+    const strategy = this.value;
+    const providerModelSelect = document.getElementById("provider-model-select");
+    const tokenInput = document.getElementById("token-input");
+    const instruction = document.getElementById("instruction");
+    const semantic_filter = document.getElementById("semantic_filter");
+    const instruction_div = document.getElementById("instruction_div");
+    const semantic_filter_div = document.getElementById("semantic_filter_div");
+    const llm_settings = document.getElementById("llm_settings");
+
+    if (strategy === "LLMExtractionStrategy") {
+        // providerModelSelect.disabled = false;
+        // tokenInput.disabled = false;
+        // semantic_filter.disabled = true;
+        // instruction.disabled = false;
+        llm_settings.classList.remove("hidden");
+        instruction_div.classList.remove("hidden");
+        semantic_filter_div.classList.add("hidden");
+    } else if (strategy === "NoExtractionStrategy") {
+        semantic_filter_div.classList.add("hidden");
+        instruction_div.classList.add("hidden");
+        llm_settings.classList.add("hidden");
+    } else {
+        // providerModelSelect.disabled = true;
+        // tokenInput.disabled = true;
+        // semantic_filter.disabled = false;
+        // instruction.disabled = true;
+        llm_settings.classList.add("hidden");
+        instruction_div.classList.add("hidden");
+        semantic_filter_div.classList.remove("hidden");
+    }
+
+
+});
+
+// Get the selected provider model and token from local storage
+const storedProviderModel = localStorage.getItem("provider_model");
+const storedToken = localStorage.getItem(storedProviderModel);
+
+if (storedProviderModel) {
+    document.getElementById("provider-model-select").value = storedProviderModel;
+}
+
+if (storedToken) {
+    document.getElementById("token-input").value = storedToken;
+}
+
+// Handle provider model dropdown change
+document.getElementById("provider-model-select").addEventListener("change", () => {
+    const selectedProviderModel = document.getElementById("provider-model-select").value;
+    const storedToken = localStorage.getItem(selectedProviderModel);
+
+    if (storedToken) {
+        document.getElementById("token-input").value = storedToken;
+    } else {
+        document.getElementById("token-input").value = "";
+    }
+});
+
+// Fetch total count from the database
+axios
+    .get("/total-count")
+    .then((response) => {
+        document.getElementById("total-count").textContent = response.data.count;
+    })
+    .catch((error) => console.error(error));
+
+// Handle crawl button click
+document.getElementById("crawl-btn").addEventListener("click", () => {
+    // validate input to have both URL and API token
+    // if selected extraction strategy is LLMExtractionStrategy, then API token is required
+    if (document.getElementById("extraction-strategy-select").value === "LLMExtractionStrategy") {
+        if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
+            alert("Please enter both URL(s) and API token.");
+            return;
+        }
+    }
+
+    const selectedProviderModel = document.getElementById("provider-model-select").value;
+    const apiToken = document.getElementById("token-input").value;
+    const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
+    const bypassCache = document.getElementById("bypass-cache-checkbox").checked;
+
+    // Save the selected provider model and token to local storage
+    localStorage.setItem("provider_model", selectedProviderModel);
+    localStorage.setItem(selectedProviderModel, apiToken);
+
+    const urlsInput = document.getElementById("url-input").value;
+    const urls = urlsInput.split(",").map((url) => url.trim());
+    const data = {
+        urls: urls,
+        include_raw_html: true,
+        bypass_cache: bypassCache,
+        extract_blocks: extractBlocks,
+        word_count_threshold: parseInt(document.getElementById("threshold").value),
+        extraction_strategy: document.getElementById("extraction-strategy-select").value,
+        extraction_strategy_args: {
+            provider: selectedProviderModel,
+            api_token: apiToken,
+            instruction: document.getElementById("instruction").value,
+            semantic_filter: document.getElementById("semantic_filter").value,
+        },
+        chunking_strategy: document.getElementById("chunking-strategy-select").value,
+        chunking_strategy_args: {},
+        css_selector: document.getElementById("css-selector").value,
+        screenshot: document.getElementById("screenshot-checkbox").checked,
+        // instruction: document.getElementById("instruction").value,
+        // semantic_filter: document.getElementById("semantic_filter").value,
+        verbose: true,
+    };
+
+    // import requests
+
+    // data = {
+    //   "urls": [
+    //     "https://www.nbcnews.com/business"
+    //   ],
+    //   "word_count_threshold": 10,
+    //   "extraction_strategy": "NoExtractionStrategy",
+    // }
+    
+    // response = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally 
+    // print(response.json())
+
+    // save api token to local storage
+    localStorage.setItem("api_token", document.getElementById("token-input").value);
+
+    document.getElementById("loading").classList.remove("hidden");
+    document.getElementById("result").style.visibility = "hidden";
+    document.getElementById("code_help").style.visibility = "hidden";
+
+    axios
+        .post("/crawl", data)
+        .then((response) => {
+            const result = response.data.results[0];
+            const parsedJson = JSON.parse(result.extracted_content);
+            document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
+            document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
+            document.getElementById("markdown-result").textContent = result.markdown;
+            document.getElementById("media-result").textContent = JSON.stringify( result.media, null, 2);
+            if (result.screenshot){
+                const imgElement = document.createElement("img");
+                // Set the src attribute with the base64 data
+                imgElement.src = `data:image/png;base64,${result.screenshot}`;
+                document.getElementById("screenshot-result").innerHTML = "";
+                document.getElementById("screenshot-result").appendChild(imgElement);
+            }
+            
+            // Update code examples dynamically
+            const extractionStrategy = data.extraction_strategy;
+            const isLLMExtraction = extractionStrategy === "LLMExtractionStrategy";
+
+            // REMOVE API TOKEN FROM CODE EXAMPLES
+            data.extraction_strategy_args.api_token = "your_api_token";
+
+            if (data.extraction_strategy === "NoExtractionStrategy") {
+                delete data.extraction_strategy_args;
+                delete data.extrac_blocks;
+            }
+
+            if (data.chunking_strategy === "RegexChunking") {
+                delete data.chunking_strategy_args;
+            }
+
+            delete data.verbose;
+
+            if (data.css_selector === "") {
+                delete data.css_selector;
+            }
+
+            if (!data.bypass_cache) {
+                delete data.bypass_cache;
+            }
+
+            if (!data.extract_blocks) {
+                delete data.extract_blocks;
+            }
+
+            if (!data.include_raw_html) {
+                delete data.include_raw_html;
+            }
+
+            document.getElementById(
+                "curl-code"
+            ).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
+                ...data,
+                api_token: isLLMExtraction ? "your_api_token" : undefined,
+            }, null, 2)}' https://crawl4ai.com/crawl`;
+
+            document.getElementById("python-code").textContent = `import requests\n\ndata = ${JSON.stringify(
+                { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
+                null,
+                2
+            )}\n\nresponse = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
+
+            document.getElementById(
+                "nodejs-code"
+            ).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
+                { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
+                null,
+                2
+            )};\n\naxios.post("https://crawl4ai.com/crawl", data) // OR local host if your run locally \n    .then(response => console.log(response.data))\n    .catch(error => console.error(error));`;
+
+            document.getElementById(
+                "library-code"
+            ).textContent = `from crawl4ai.web_crawler import WebCrawler\nfrom crawl4ai.extraction_strategy import *\nfrom crawl4ai.chunking_strategy import *\n\ncrawler = WebCrawler()\ncrawler.warmup()\n\nresult = crawler.run(\n    url='${
+                urls[0]
+            }',\n    word_count_threshold=${data.word_count_threshold},\n    extraction_strategy=${
+                isLLMExtraction
+                    ? `${extractionStrategy}(provider="${data.provider_model}", api_token="${data.api_token}")`
+                    : extractionStrategy + "()"
+            },\n    chunking_strategy=${data.chunking_strategy}(),\n    bypass_cache=${
+                data.bypass_cache
+            },\n    css_selector="${data.css_selector}"\n)\nprint(result)`;
+
+            // Highlight code syntax
+            hljs.highlightAll();
+
+            // Select JSON tab by default
+            document.querySelector('.tab-btn[data-tab="json"]').click();
+
+            document.getElementById("loading").classList.add("hidden");
+
+            document.getElementById("result").style.visibility = "visible";
+            document.getElementById("code_help").style.visibility = "visible";
+
+            // increment the total count
+            document.getElementById("total-count").textContent =
+                parseInt(document.getElementById("total-count").textContent) + 1;
+        })
+        .catch((error) => {
+            console.error(error);
+            document.getElementById("loading").classList.add("hidden");
+        });
+});
+
+// Handle tab clicks
+document.querySelectorAll(".tab-btn").forEach((btn) => {
+    btn.addEventListener("click", () => {
+        const tab = btn.dataset.tab;
+        document.querySelectorAll(".tab-btn").forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
+        btn.classList.add("bg-lime-700", "text-white");
+        document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
+        document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
+    });
+});
+
+// Handle code tab clicks
+document.querySelectorAll(".code-tab-btn").forEach((btn) => {
+    btn.addEventListener("click", () => {
+        const tab = btn.dataset.tab;
+        document.querySelectorAll(".code-tab-btn").forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
+        btn.classList.add("bg-lime-700", "text-white");
+        document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
+        document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
+    });
+});
+
+// Handle copy to clipboard button clicks
+
+async function copyToClipboard(text) {
+    if (navigator.clipboard && navigator.clipboard.writeText) {
+        return navigator.clipboard.writeText(text);
+    } else {
+        return fallbackCopyTextToClipboard(text);
+    }
+}
+
+function fallbackCopyTextToClipboard(text) {
+    return new Promise((resolve, reject) => {
+        const textArea = document.createElement("textarea");
+        textArea.value = text;
+
+        // Avoid scrolling to bottom
+        textArea.style.top = "0";
+        textArea.style.left = "0";
+        textArea.style.position = "fixed";
+
+        document.body.appendChild(textArea);
+        textArea.focus();
+        textArea.select();
+
+        try {
+            const successful = document.execCommand("copy");
+            if (successful) {
+                resolve();
+            } else {
+                reject();
+            }
+        } catch (err) {
+            reject(err);
+        }
+
+        document.body.removeChild(textArea);
+    });
+}
+
+document.querySelectorAll(".copy-btn").forEach((btn) => {
+    btn.addEventListener("click", () => {
+        const target = btn.dataset.target;
+        const code = document.getElementById(target).textContent;
+        //navigator.clipboard.writeText(code).then(() => {
+        copyToClipboard(code).then(() => {
+            btn.textContent = "Copied!";
+            setTimeout(() => {
+                btn.textContent = "Copy";
+            }, 2000);
+        });
+    });
+});
+
+document.addEventListener("DOMContentLoaded", async () => {
+    try {
+        const extractionResponse = await fetch("/strategies/extraction");
+        const extractionStrategies = await extractionResponse.json();
+
+        const chunkingResponse = await fetch("/strategies/chunking");
+        const chunkingStrategies = await chunkingResponse.json();
+
+        renderStrategies("extraction-strategies", extractionStrategies);
+        renderStrategies("chunking-strategies", chunkingStrategies);
+    } catch (error) {
+        console.error("Error fetching strategies:", error);
+    }
+});
+
+function renderStrategies(containerId, strategies) {
+    const container = document.getElementById(containerId);
+    container.innerHTML = ""; // Clear any existing content
+    strategies = JSON.parse(strategies);
+    Object.entries(strategies).forEach(([strategy, description]) => {
+        const strategyElement = document.createElement("div");
+        strategyElement.classList.add("bg-zinc-800", "p-4", "rounded", "shadow-md", "docs-item");
+
+        const strategyDescription = document.createElement("div");
+        strategyDescription.classList.add("text-gray-300", "prose", "prose-sm");
+        strategyDescription.innerHTML = marked.parse(description);
+
+        strategyElement.appendChild(strategyDescription);
+
+        container.appendChild(strategyElement);
+    });
+}
+document.querySelectorAll(".sidebar a").forEach((link) => {
+    link.addEventListener("click", function (event) {
+        event.preventDefault();
+        document.querySelectorAll(".content-section").forEach((section) => {
+            section.classList.remove("active");
+        });
+        const target = event.target.getAttribute("data-target");
+        document.getElementById(target).classList.add("active");
+    });
+});
+// Highlight code syntax
+hljs.highlightAll();

pages/index copy.html

@@ -0,0 +1,971 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Crawl4AI</title>
+
+        <link rel="preconnect" href="https://fonts.googleapis.com" />
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
+
+        <!-- <link href="https://cdn.jsdelivr.net/npm/tailwindcss@3.4.3/dist/tailwind.min.css" rel="stylesheet" /> -->
+        <script src="https://cdn.tailwindcss.com"></script>
+        <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
+        <link
+            rel="stylesheet"
+            href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/monokai.min.css"
+        />
+        <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
+
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
+        <style>
+            :root {
+                --ifm-font-size-base: 100%;
+                --ifm-line-height-base: 1.65;
+                --ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans,
+                    sans-serif, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji",
+                    "Segoe UI Emoji", "Segoe UI Symbol";
+            }
+            html {
+                -webkit-font-smoothing: antialiased;
+                -webkit-text-size-adjust: 100%;
+                text-size-adjust: 100%;
+                font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
+            }
+            body {
+                background-color: #1a202c;
+                color: #fff;
+            }
+            .tab-content {
+                max-height: 400px;
+                overflow: auto;
+            }
+            pre {
+                white-space: pre-wrap;
+                font-size: 14px;
+            }
+            pre code {
+                width: 100%;
+            }
+        </style>
+        <style>
+            /* Custom styling for docs-item class and Markdown generated elements */
+            .docs-item {
+                background-color: #2d3748; /* bg-gray-800 */
+                padding: 1rem; /* p-4 */
+                border-radius: 0.375rem; /* rounded */
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* shadow-md */
+                margin-bottom: 1rem; /* space between items */
+            }
+
+            .docs-item h3,
+            .docs-item h4 {
+                color: #ffffff; /* text-white */
+                font-size: 1.25rem; /* text-xl */
+                font-weight: 700; /* font-bold */
+                margin-bottom: 0.5rem; /* mb-2 */
+            }
+
+            .docs-item p {
+                color: #e2e8f0; /* text-gray-300 */
+                margin-bottom: 0.5rem; /* mb-2 */
+            }
+
+            .docs-item code {
+                background-color: #1a202c; /* bg-gray-900 */
+                color: #e2e8f0; /* text-gray-300 */
+                padding: 0.25rem 0.5rem; /* px-2 py-1 */
+                border-radius: 0.25rem; /* rounded */
+            }
+
+            .docs-item pre {
+                background-color: #1a202c; /* bg-gray-900 */
+                color: #e2e8f0; /* text-gray-300 */
+                padding: 0.5rem; /* p-2 */
+                border-radius: 0.375rem; /* rounded */
+                overflow: auto; /* overflow-auto */
+                margin-bottom: 0.5rem; /* mb-2 */
+            }
+
+            .docs-item div {
+                color: #e2e8f0; /* text-gray-300 */
+                font-size: 1rem; /* prose prose-sm */
+                line-height: 1.25rem; /* line-height for readability */
+            }
+
+            /* Adjustments to make prose class more suitable for dark mode */
+            .prose {
+                max-width: none; /* max-w-none */
+            }
+
+            .prose p,
+            .prose ul {
+                margin-bottom: 1rem; /* mb-4 */
+            }
+
+            .prose code {
+                /* background-color: #4a5568; */ /* bg-gray-700 */
+                color: #65a30d; /* text-white */
+                padding: 0.25rem 0.5rem; /* px-1 py-0.5 */
+                border-radius: 0.25rem; /* rounded */
+                display: inline-block; /* inline-block */
+            }
+
+            .prose pre {
+                background-color: #1a202c; /* bg-gray-900 */
+                color: #ffffff; /* text-white */
+                padding: 0.5rem; /* p-2 */
+                border-radius: 0.375rem; /* rounded */
+            }
+
+            .prose h3 {
+                color: #65a30d; /* text-white */
+                font-size: 1.25rem; /* text-xl */
+                font-weight: 700; /* font-bold */
+                margin-bottom: 0.5rem; /* mb-2 */
+            }
+        </style>
+    </head>
+    <body class="bg-black text-gray-200">
+        <header class="bg-zinc-950 text-white py-4 flex">
+            <div class="mx-auto px-4">
+                <h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts</h1>
+            </div>
+            <div class="mx-auto px-4 flex font-bold text-xl gap-2">
+                <span>📊 Total Website Processed</span>
+                <span id="total-count" class="text-lime-400">2</span>
+            </div>
+        </header>
+
+        <section class="try-it py-8 px-16 pb-20">
+            <div class="container mx-auto px-4">
+                <h2 class="text-2xl font-bold mb-4">Try It Now</h2>
+                <div class="grid grid-cols-1 lg:grid-cols-3 gap-4">
+                    <div class="space-y-4">
+                        <div class="flex flex-col">
+                            <label for="url-input" class="text-lime-500 font-bold text-xs">URL(s)</label>
+                            <input
+                                type="text"
+                                id="url-input"
+                                value="https://www.nbcnews.com/business"
+                                class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
+                                placeholder="Enter URL(s) separated by commas"
+                            />
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="threshold" class="text-lime-500 font-bold text-xs">Min Words Threshold</label>
+                            <select
+                                id="threshold"
+                                class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
+                            >
+                                <option value="5">5</option>
+                                <option value="10" selected>10</option>
+                                <option value="15">15</option>
+                                <option value="20">20</option>
+                                <option value="25">25</option>
+                            </select>
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="css-selector" class="text-lime-500 font-bold text-xs">CSS Selector</label>
+                            <input
+                                type="text"
+                                id="css-selector"
+                                class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
+                                placeholder="Enter CSS Selector"
+                            />
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="extraction-strategy-select" class="text-lime-500 font-bold text-xs"
+                                >Extraction Strategy</label
+                            >
+                            <select
+                                id="extraction-strategy-select"
+                                class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-lime-500"
+                            >
+                                <option value="CosineStrategy">CosineStrategy</option>
+                                <option value="LLMExtractionStrategy">LLMExtractionStrategy</option>
+                                <option value="NoExtractionStrategy">NoExtractionStrategy</option>
+                            </select>
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="chunking-strategy-select" class="text-lime-500 font-bold text-xs"
+                                >Chunking Strategy</label
+                            >
+                            <select
+                                id="chunking-strategy-select"
+                                class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-lime-500"
+                            >
+                                <option value="RegexChunking">RegexChunking</option>
+                                <option value="NlpSentenceChunking">NlpSentenceChunking</option>
+                                <option value="TopicSegmentationChunking">TopicSegmentationChunking</option>
+                                <option value="FixedLengthWordChunking">FixedLengthWordChunking</option>
+                                <option value="SlidingWindowChunking">SlidingWindowChunking</option>
+                            </select>
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="provider-model-select" class="text-lime-500 font-bold text-xs"
+                                >Provider Model</label
+                            >
+                            <select
+                                id="provider-model-select"
+                                class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
+                                disabled
+                            >
+                                <option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
+                                <option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
+                                <option value="openai/gpt-4-turbo">gpt-4-turbo</option>
+                                <option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
+                                <option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
+                                <option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
+                                <option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
+                            </select>
+                        </div>
+                        <div class="flex flex-col">
+                            <label for="token-input" class="text-lime-500 font-bold text-xs">API Token</label>
+                            <input
+                                type="password"
+                                id="token-input"
+                                class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
+                                placeholder="Enter Groq API token"
+                                disabled
+                            />
+                        </div>
+                        <div class="flex gap-3">
+                            <div class="flex items-center gap-2">
+                                <input type="checkbox" id="bypass-cache-checkbox" />
+                                <label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
+                            </div>
+                            <div class="flex items-center gap-2">
+                                <input type="checkbox" id="extract-blocks-checkbox" checked />
+                                <label for="extract-blocks-checkbox" class="text-lime-500 font-bold"
+                                    >Extract Blocks</label
+                                >
+                            </div>
+                            <button id="crawl-btn" class="bg-lime-600 text-black font-bold px-4 py-0 rounded">
+                                Crawl
+                            </button>
+                        </div>
+                    </div>
+
+                    <div id="result" class=" ">
+                        <div id="loading" class="hidden">
+                            <p class="text-white">Loading... Please wait.</p>
+                        </div>
+                        <div class="tab-buttons flex gap-2">
+                            <button
+                                class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="json"
+                            >
+                                JSON
+                            </button>
+                            <button
+                                class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="cleaned-html"
+                            >
+                                Cleaned HTML
+                            </button>
+                            <button
+                                class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="markdown"
+                            >
+                                Markdown
+                            </button>
+                        </div>
+                        <div class="tab-content code bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
+                            <pre class="h-full flex"><code id="json-result" class="language-json"></code></pre>
+                            <pre
+                                class="hidden h-full flex"
+                            ><code id="cleaned-html-result" class="language-html"></code></pre>
+                            <pre
+                                class="hidden h-full flex"
+                            ><code id="markdown-result" class="language-markdown"></code></pre>
+                        </div>
+                    </div>
+
+                    <div id="code_help" class=" ">
+                        <div class="tab-buttons flex gap-2">
+                            <button
+                                class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="curl"
+                            >
+                                cURL
+                            </button>
+                            <button
+                                class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="library"
+                            >
+                                Python Library
+                            </button>
+                            <button
+                                class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="python"
+                            >
+                                Python (Request)
+                            </button>
+                            <button
+                                class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                                data-tab="nodejs"
+                            >
+                                Node.js
+                            </button>
+                        </div>
+                        <div class="tab-content result bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
+                            <pre class="h-full flex relative">
+                                <code id="curl-code" class="language-bash"></code>
+                                <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
+                            </pre>
+                            <pre class="hidden h-full flex relative">
+                                <code id="python-code" class="language-python"></code>
+                                <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
+                            </pre>
+                            <pre class="hidden h-full flex relative">
+                                <code id="nodejs-code" class="language-javascript"></code>
+                                <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
+                            </pre>
+                            <pre class="hidden h-full flex relative">
+                                <code id="library-code" class="language-python"></code>
+                                <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="library-code">Copy</button>
+                            </pre>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </section>
+        <section class="bg-zinc-900 text-zinc-300 p-6 px-20">
+            <div class="grid grid-cols-2 gap-4 p-4 bg-zinc-900 text-lime-500">
+                <!-- Step 1 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🌟 <strong>Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling fun!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">
+                    First Step: Create an instance of WebCrawler and call the <code>warmup()</code> function.
+                </div>
+                <div>
+                    <pre><code class="language-python">crawler = WebCrawler()
+            crawler.warmup()</code></pre>
+                </div>
+
+                <!-- Step 2 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">First crawl (caches the result):</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Second crawl (Force to crawl again):</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)</code></pre>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Crawl result without raw HTML content:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", include_raw_html=False)</code></pre>
+                </div>
+
+                <!-- Step 3 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    📄
+                    <strong
+                        >The 'include_raw_html' parameter, when set to True, includes the raw HTML content in the
+                        response. By default, it is set to True.</strong
+                    >
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Set <code>always_by_pass_cache</code> to True:</div>
+                <div>
+                    <pre><code class="language-python">crawler.always_by_pass_cache = True</code></pre>
+                </div>
+
+                <!-- Step 4 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using RegexChunking:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                chunking_strategy=RegexChunking(patterns=["\n\n"])
+            )</code></pre>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using NlpSentenceChunking:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                chunking_strategy=NlpSentenceChunking()
+            )</code></pre>
+                </div>
+
+                <!-- Step 5 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using CosineStrategy:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                extraction_strategy=CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method="ward", top_k=3)
+            )</code></pre>
+                </div>
+
+                <!-- Step 6 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🤖 <strong>Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using LLMExtractionStrategy without instructions:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                extraction_strategy=LLMExtractionStrategy(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'))
+            )</code></pre>
+                </div>
+
+                <!-- Step 7 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    📜 <strong>Let's make it even more interesting: LLMExtractionStrategy with instructions!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using LLMExtractionStrategy with instructions:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                extraction_strategy=LLMExtractionStrategy(
+                    provider="openai/gpt-4o",
+                    api_token=os.getenv('OPENAI_API_KEY'),
+                    instruction="I am interested in only financial news"
+                )
+            )</code></pre>
+                </div>
+
+                <!-- Step 8 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🎯 <strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using CSS selector to extract H2 tags:</div>
+                <div>
+                    <pre><code class="language-python">result = crawler.run(
+                url="https://www.nbcnews.com/business",
+                css_selector="h2"
+            )</code></pre>
+                </div>
+
+                <!-- Step 9 -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🖱️ <strong>Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong>
+                </div>
+                <div class="bg-zinc-800 p-2 rounded">Using JavaScript to click 'Load More' button:</div>
+                <div>
+                    <pre><code class="language-python">js_code = """
+            const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+            loadMoreButton && loadMoreButton.click();
+            """
+            crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+            crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
+            result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
+                </div>
+
+                <!-- Conclusion -->
+                <div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
+                    🎉
+                    <strong
+                        >Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl
+                        the web like a pro! 🕸️</strong
+                    >
+                </div>
+            </div>
+        </section>
+        <section class="bg-zinc-900 text-zinc-300 p-6 px-20">
+            <h1 class="text-3xl font-bold mb-4">Installation 💻</h1>
+            <p class="mb-4">
+                There are two ways to use Crawl4AI: as a library in your Python projects or as a standalone local
+                server.
+            </p>
+
+            <p class="mb-4">
+                You can also try Crawl4AI in a Google Colab
+                <a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
+                    ><img
+                        src="https://colab.research.google.com/assets/colab-badge.svg"
+                        alt="Open In Colab"
+                        style="display: inline-block; width: 100px; height: 20px"
+                /></a>
+            </p>
+
+            <h2 class="text-2xl font-bold mb-2">Using Crawl4AI as a Library 📚</h2>
+            <p class="mb-4">To install Crawl4AI as a library, follow these steps:</p>
+
+            <ol class="list-decimal list-inside mb-4">
+                <li class="mb-2">
+                    Install the package from GitHub:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code>pip install git+https://github.com/unclecode/crawl4ai.git</code></pre>
+                </li>
+                <li class="mb-2">
+                    Alternatively, you can clone the repository and install the package locally:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code  class = "language-python bash">virtualenv venv
+source venv/bin/activate
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e .
+        </code></pre>
+                </li>
+                <li>
+                    Import the necessary modules in your Python script:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code class = "language-python hljs">from crawl4ai.web_crawler import WebCrawler
+from crawl4ai.chunking_strategy import *
+from crawl4ai.extraction_strategy import *
+import os
+
+crawler = WebCrawler()
+
+# Single page crawl
+single_url = UrlModel(url='https://www.nbcnews.com/business', forced=False)
+result = crawl4ai.fetch_page(
+    url='https://www.nbcnews.com/business',
+    word_count_threshold=5, # Minimum word count for a HTML tag to be considered as a worthy block
+    chunking_strategy= RegexChunking( patterns = ["\\n\\n"]), # Default is RegexChunking
+    extraction_strategy= CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3) # Default is CosineStrategy
+    # extraction_strategy= LLMExtractionStrategy(provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY')),
+    bypass_cache=False,
+    extract_blocks =True, # Whether to extract semantical blocks of text from the HTML
+    css_selector = "", # Eg: "div.article-body"
+    verbose=True,
+    include_raw_html=True, # Whether to include the raw HTML content in the response
+)
+print(result.model_dump())
+        </code></pre>
+                </li>
+            </ol>
+            <p class="mb-4">
+                For more information about how to run Crawl4AI as a local server, please refer to the
+                <a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
+            </p>
+            
+        </section>
+
+        <section class="bg-zinc-900 text-zinc-300 p-6 px-20">
+            <h1 class="text-3xl font-bold mb-4">📖 Parameters</h1>
+            <div class="overflow-x-auto">
+                <table class="min-w-full bg-zinc-800 border border-zinc-700">
+                    <thead>
+                        <tr>
+                            <th class="py-2 px-4 border-b border-zinc-700">Parameter</th>
+                            <th class="py-2 px-4 border-b border-zinc-700">Description</th>
+                            <th class="py-2 px-4 border-b border-zinc-700">Required</th>
+                            <th class="py-2 px-4 border-b border-zinc-700">Default Value</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">urls</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                A list of URLs to crawl and extract data from.
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">Yes</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">-</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">include_raw_html</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                Whether to include the raw HTML content in the response.
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">false</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">bypass_cache</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                Whether to force a fresh crawl even if the URL has been previously crawled.
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">false</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">extract_blocks</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                Whether to extract semantical blocks of text from the HTML.
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">true</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">word_count_threshold</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                The minimum number of words a block must contain to be considered meaningful (minimum
+                                value is 5).
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">5</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">extraction_strategy</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                The strategy to use for extracting content from the HTML (e.g., "CosineStrategy").
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">CosineStrategy</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">chunking_strategy</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                The strategy to use for chunking the text before processing (e.g., "RegexChunking").
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">RegexChunking</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b border-zinc-700">css_selector</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">
+                                The CSS selector to target specific parts of the HTML for extraction.
+                            </td>
+                            <td class="py-2 px-4 border-b border-zinc-700">No</td>
+                            <td class="py-2 px-4 border-b border-zinc-700">None</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4">verbose</td>
+                            <td class="py-2 px-4">Whether to enable verbose logging.</td>
+                            <td class="py-2 px-4">No</td>
+                            <td class="py-2 px-4">true</td>
+                        </tr>
+                    </tbody>
+                </table>
+            </div>
+        </section>
+
+        <section id="extraction" class="py-8 px-20">
+            <div class="overflow-x-auto mx-auto px-6">
+                <h2 class="text-2xl font-bold mb-4">Extraction Strategies</h2>
+                <div id="extraction-strategies" class="space-y-4"></div>
+            </div>
+        </section>
+
+        <section id="chunking" class="py-8 px-20">
+            <div class="overflow-x-auto mx-auto px-6">
+                <h2 class="text-2xl font-bold mb-4">Chunking Strategies</h2>
+                <div id="chunking-strategies" class="space-y-4"></div>
+            </div>
+        </section>
+
+        <section class="hero bg-zinc-900 py-8 px-20">
+            <div class="container mx-auto px-4">
+                <h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
+                <p class="text-lg mb-4">
+                    In recent times, we've witnessed a surge of startups emerging, riding the AI hype wave and charging
+                    for services that should rightfully be accessible to everyone. 🌍💸 One such example is scraping and
+                    crawling web pages and transforming them into a format suitable for Large Language Models (LLMs).
+                    🕸️🤖 We believe that building a business around this is not the right approach; instead, it should
+                    definitely be open-source. 🆓🌟 So, if you possess the skills to build such tools and share our
+                    philosophy, we invite you to join our "Robinhood" band and help set these products free for the
+                    benefit of all. 🤝💪
+                </p>
+            </div>
+        </section>
+
+        <section class="installation py-8 px-20">
+            <div class="container mx-auto px-4">
+                <h2 class="text-2xl font-bold mb-4">⚙️ Installation</h2>
+                <p class="mb-4">
+                    To install and run Crawl4AI as a library or a local server, please refer to the 📚
+                    <a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
+                </p>
+            </div>
+        </section>
+
+        <footer class="bg-zinc-900 text-white py-4">
+            <div class="container mx-auto px-4">
+                <div class="flex justify-between items-center">
+                    <p>© 2024 Crawl4AI. All rights reserved.</p>
+                    <div class="social-links">
+                        <a
+                            href="https://github.com/unclecode/crawl4ai"
+                            class="text-white hover:text-gray-300 mx-2"
+                            target="_blank"
+                            >😺 GitHub</a
+                        >
+                        <a
+                            href="https://twitter.com/unclecode"
+                            class="text-white hover:text-gray-300 mx-2"
+                            target="_blank"
+                            >🐦 Twitter</a
+                        >
+                    </div>
+                </div>
+            </div>
+        </footer>
+
+        <script>
+            // JavaScript to manage dynamic form changes and logic
+            document.getElementById("extraction-strategy-select").addEventListener("change", function () {
+                const strategy = this.value;
+                const providerModelSelect = document.getElementById("provider-model-select");
+                const tokenInput = document.getElementById("token-input");
+
+                if (strategy === "LLMExtractionStrategy") {
+                    providerModelSelect.disabled = false;
+                    tokenInput.disabled = false;
+                } else {
+                    providerModelSelect.disabled = true;
+                    tokenInput.disabled = true;
+                }
+            });
+
+            // Get the selected provider model and token from local storage
+            const storedProviderModel = localStorage.getItem("provider_model");
+            const storedToken = localStorage.getItem(storedProviderModel);
+
+            if (storedProviderModel) {
+                document.getElementById("provider-model-select").value = storedProviderModel;
+            }
+
+            if (storedToken) {
+                document.getElementById("token-input").value = storedToken;
+            }
+
+            // Handle provider model dropdown change
+            document.getElementById("provider-model-select").addEventListener("change", () => {
+                const selectedProviderModel = document.getElementById("provider-model-select").value;
+                const storedToken = localStorage.getItem(selectedProviderModel);
+
+                if (storedToken) {
+                    document.getElementById("token-input").value = storedToken;
+                } else {
+                    document.getElementById("token-input").value = "";
+                }
+            });
+
+            // Fetch total count from the database
+            axios
+                .get("/total-count")
+                .then((response) => {
+                    document.getElementById("total-count").textContent = response.data.count;
+                })
+                .catch((error) => console.error(error));
+
+            // Handle crawl button click
+            document.getElementById("crawl-btn").addEventListener("click", () => {
+                // validate input to have both URL and API token
+                if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
+                    alert("Please enter both URL(s) and API token.");
+                    return;
+                }
+
+                const selectedProviderModel = document.getElementById("provider-model-select").value;
+                const apiToken = document.getElementById("token-input").value;
+                const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
+                const bypassCache = document.getElementById("bypass-cache-checkbox").checked;
+
+                // Save the selected provider model and token to local storage
+                localStorage.setItem("provider_model", selectedProviderModel);
+                localStorage.setItem(selectedProviderModel, apiToken);
+
+                const urlsInput = document.getElementById("url-input").value;
+                const urls = urlsInput.split(",").map((url) => url.trim());
+                const data = {
+                    urls: urls,
+                    provider_model: selectedProviderModel,
+                    api_token: apiToken,
+                    include_raw_html: true,
+                    bypass_cache: bypassCache,
+                    extract_blocks: extractBlocks,
+                    word_count_threshold: parseInt(document.getElementById("threshold").value),
+                    extraction_strategy: document.getElementById("extraction-strategy-select").value,
+                    chunking_strategy: document.getElementById("chunking-strategy-select").value,
+                    css_selector: document.getElementById("css-selector").value,
+                    verbose: true,
+                };
+
+                // save api token to local storage
+                localStorage.setItem("api_token", document.getElementById("token-input").value);
+
+                document.getElementById("loading").classList.remove("hidden");
+                //document.getElementById("result").classList.add("hidden");
+                //document.getElementById("code_help").classList.add("hidden");
+
+                axios
+                    .post("/crawl", data)
+                    .then((response) => {
+                        const result = response.data.results[0];
+                        const parsedJson = JSON.parse(result.extracted_content);
+                        document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
+                        document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
+                        document.getElementById("markdown-result").textContent = result.markdown;
+
+                        // Update code examples dynamically
+                        const extractionStrategy = data.extraction_strategy;
+                        const isLLMExtraction = extractionStrategy === "LLMExtractionStrategy";
+
+                        document.getElementById(
+                            "curl-code"
+                        ).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
+                            ...data,
+                            api_token: isLLMExtraction ? "your_api_token" : undefined,
+                        })}' http://crawl4ai.uccode.io/crawl`;
+
+                        document.getElementById(
+                            "python-code"
+                        ).textContent = `import requests\n\ndata = ${JSON.stringify(
+                            { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
+                            null,
+                            2
+                        )}\n\nresponse = requests.post("http://crawl4ai.uccode.io/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
+
+                        document.getElementById(
+                            "nodejs-code"
+                        ).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
+                            { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
+                            null,
+                            2
+                        )};\n\naxios.post("http://crawl4ai.uccode.io/crawl", data) // OR local host if your run locally \n    .then(response => console.log(response.data))\n    .catch(error => console.error(error));`;
+
+                        document.getElementById(
+                            "library-code"
+                        ).textContent = `from crawl4ai.web_crawler import WebCrawler\nfrom crawl4ai.extraction_strategy import *\nfrom crawl4ai.chunking_strategy import *\n\ncrawler = WebCrawler()\ncrawler.warmup()\n\nresult = crawler.run(\n    url='${
+                            urls[0]
+                        }',\n    word_count_threshold=${data.word_count_threshold},\n    extraction_strategy=${
+                            isLLMExtraction
+                                ? `${extractionStrategy}(provider="${data.provider_model}", api_token="${data.api_token}")`
+                                : extractionStrategy + "()"
+                        },\n    chunking_strategy=${data.chunking_strategy}(),\n    bypass_cache=${
+                            data.bypass_cache
+                        },\n    css_selector="${data.css_selector}"\n)\nprint(result)`;
+
+                        // Highlight code syntax
+                        hljs.highlightAll();
+
+                        // Select JSON tab by default
+                        document.querySelector('.tab-btn[data-tab="json"]').click();
+
+                        document.getElementById("loading").classList.add("hidden");
+                        document.getElementById("result").classList.remove("hidden");
+                        document.getElementById("code_help").classList.remove("hidden");
+
+                        // increment the total count
+                        document.getElementById("total-count").textContent =
+                            parseInt(document.getElementById("total-count").textContent) + 1;
+                    })
+                    .catch((error) => {
+                        console.error(error);
+                        document.getElementById("loading").classList.add("hidden");
+                    });
+            });
+
+            // Handle tab clicks
+            document.querySelectorAll(".tab-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const tab = btn.dataset.tab;
+                    document
+                        .querySelectorAll(".tab-btn")
+                        .forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
+                    btn.classList.add("bg-lime-700", "text-white");
+                    document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
+                    document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
+                });
+            });
+
+            // Handle code tab clicks
+            document.querySelectorAll(".code-tab-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const tab = btn.dataset.tab;
+                    document
+                        .querySelectorAll(".code-tab-btn")
+                        .forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
+                    btn.classList.add("bg-lime-700", "text-white");
+                    document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
+                    document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
+                });
+            });
+
+            // Handle copy to clipboard button clicks
+
+            async function copyToClipboard(text) {
+                if (navigator.clipboard && navigator.clipboard.writeText) {
+                    return navigator.clipboard.writeText(text);
+                } else {
+                    return fallbackCopyTextToClipboard(text);
+                }
+            }
+
+            function fallbackCopyTextToClipboard(text) {
+                return new Promise((resolve, reject) => {
+                    const textArea = document.createElement("textarea");
+                    textArea.value = text;
+
+                    // Avoid scrolling to bottom
+                    textArea.style.top = "0";
+                    textArea.style.left = "0";
+                    textArea.style.position = "fixed";
+
+                    document.body.appendChild(textArea);
+                    textArea.focus();
+                    textArea.select();
+
+                    try {
+                        const successful = document.execCommand("copy");
+                        if (successful) {
+                            resolve();
+                        } else {
+                            reject();
+                        }
+                    } catch (err) {
+                        reject(err);
+                    }
+
+                    document.body.removeChild(textArea);
+                });
+            }
+
+            document.querySelectorAll(".copy-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const target = btn.dataset.target;
+                    const code = document.getElementById(target).textContent;
+                    //navigator.clipboard.writeText(code).then(() => {
+                    copyToClipboard(code).then(() => {
+                        btn.textContent = "Copied!";
+                        setTimeout(() => {
+                            btn.textContent = "Copy";
+                        }, 2000);
+                    });
+                });
+            });
+
+            document.addEventListener("DOMContentLoaded", async () => {
+                try {
+                    const extractionResponse = await fetch("/strategies/extraction");
+                    const extractionStrategies = await extractionResponse.json();
+
+                    const chunkingResponse = await fetch("/strategies/chunking");
+                    const chunkingStrategies = await chunkingResponse.json();
+
+                    renderStrategies("extraction-strategies", extractionStrategies);
+                    renderStrategies("chunking-strategies", chunkingStrategies);
+                } catch (error) {
+                    console.error("Error fetching strategies:", error);
+                }
+            });
+
+            function renderStrategies(containerId, strategies) {
+                const container = document.getElementById(containerId);
+                container.innerHTML = ""; // Clear any existing content
+                strategies = JSON.parse(strategies);
+                Object.entries(strategies).forEach(([strategy, description]) => {
+                    const strategyElement = document.createElement("div");
+                    strategyElement.classList.add("bg-zinc-800", "p-4", "rounded", "shadow-md", "docs-item");
+
+                    const strategyDescription = document.createElement("div");
+                    strategyDescription.classList.add("text-gray-300", "prose", "prose-sm");
+                    strategyDescription.innerHTML = marked.parse(description);
+
+                    strategyElement.appendChild(strategyDescription);
+
+                    container.appendChild(strategyElement);
+                });
+            }
+
+            // Highlight code syntax
+            hljs.highlightAll();
+        </script>
+    </body>
+</html>

pages/index.html

@@ -0,0 +1,73 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Crawl4AI</title>
+
+        <link rel="preconnect" href="https://fonts.googleapis.com" />
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
+
+        <!-- <link href="https://cdn.jsdelivr.net/npm/tailwindcss@3.4.3/dist/tailwind.min.css" rel="stylesheet" /> -->
+        <script src="https://cdn.tailwindcss.com"></script>
+        <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
+        <link rel="stylesheet" href="/pages/app.css" />
+        <link
+            rel="stylesheet"
+            href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/monokai.min.css"
+        />
+        <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
+
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
+    </head>
+    <body class="bg-black text-gray-200">
+        <header class="bg-zinc-950 text-lime-500 py-4 flex">
+            
+            <div class="mx-auto px-4">
+                <h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts</h1>
+            </div>
+            <div class="mx-auto px-4 flex font-bold text-xl gap-2">
+                <span>📊 Total Website Processed</span>
+                <span id="total-count" class="text-lime-400">2</span>
+            </div>
+        </header>
+        
+            {{ try_it | safe }}
+
+            <div class="mx-auto p-4 bg-zinc-950 text-lime-500 min-h-screen">
+                <div class="container mx-auto">
+                <div class="flex h-full px-20">
+                    <div class="sidebar w-1/4 p-4">
+                        <h2 class="text-lg font-bold mb-4">Outline</h2>
+                        <ul>
+                            <li class="mb-2"><a href="#" data-target="installation">Installation</a></li>
+                            <li class="mb-2"><a href="#" data-target="how-to-guide">How to Guide</a></li>
+                            <li class="mb-2"><a href="#" data-target="chunking-strategies">Chunking Strategies</a></li>
+                            <li class="mb-2">
+                                <a href="#" data-target="extraction-strategies">Extraction Strategies</a>
+                            </li>
+                        </ul>
+                    </div>
+
+                    <!-- Main Content -->
+                    <div class="w-3/4 p-4">
+                        {{installation | safe}} {{how_to_guide | safe}}
+
+                        <section id="chunking-strategies" class="content-section">
+                            <h1 class="text-2xl font-bold">Chunking Strategies</h1>
+                            <p>Content for chunking strategies...</p>
+                        </section>
+                        <section id="extraction-strategies" class="content-section">
+                            <h1 class="text-2xl font-bold">Extraction Strategies</h1>
+                            <p>Content for extraction strategies...</p>
+                        </section>
+                    </div>
+                </div>
+            </div>
+            </div>
+        
+        {{ footer | safe }}
+        <script script src="/pages/app.js"></script>
+    </body>
+</html>

pages/index_pooling.html

@@ -0,0 +1,425 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Crawl4AI</title>
+
+        <link rel="preconnect" href="https://fonts.googleapis.com" />
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
+
+        <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet" />
+        <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
+        <link
+            rel="stylesheet"
+            href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/vs2015.min.css"
+        />
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
+        <style>
+            :root {
+                --ifm-font-size-base: 100%;
+                --ifm-line-height-base: 1.65;
+                --ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans,
+                    sans-serif, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji",
+                    "Segoe UI Emoji", "Segoe UI Symbol";
+            }
+            html {
+                -webkit-font-smoothing: antialiased;
+                -webkit-text-size-adjust: 100%;
+                text-size-adjust: 100%;
+                font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
+            }
+            body {
+                background-color: #1a202c;
+                color: #fff;
+            }
+            .tab-content {
+                max-height: 400px;
+                overflow: auto;
+            }
+            pre {
+                white-space: pre-wrap;
+                font-size: 14px;
+            }
+            pre code {
+                width: 100%;
+            }
+        </style>
+    </head>
+    <body>
+        <header class="bg-gray-900 text-white py-4">
+            <div class="container mx-auto px-4">
+                <h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Open-source LLM Friendly Web scraper</h1>
+            </div>
+        </header>
+
+        <section class="try-it py-8 pb-20">
+            <div class="container mx-auto px-4">
+                <h2 class="text-2xl font-bold mb-4">Try It Now</h2>
+                <div class="mb-4 flex w-full gap-2">
+                    <input
+                        type="text"
+                        id="url-input"
+                        value="https://kidocode.com"
+                        class="border border-gray-600 rounded px-4 py-2 flex-grow bg-gray-800 text-white"
+                        placeholder="Enter URL(s) separated by commas"
+                    />
+                    <select
+                        id="provider-model-select"
+                        class="border border-gray-600 rounded px-4 py-2 bg-gray-800 text-white"
+                    >
+                        <!-- Add your option values here -->
+                        <option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
+                        <option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
+                        <option value="openai/gpt-4-turbo">gpt-4-turbo</option>
+                        <option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
+                        <option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
+                        <option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
+                        <option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
+                    </select>
+                    <input
+                        type="password"
+                        id="token-input"
+                        class="border border-gray-600 rounded px-4 py-2 flex-grow bg-gray-800 text-white"
+                        placeholder="Enter Groq API token"
+                    />
+                    <div class="flex items-center justify-center">
+                        <input type="checkbox" id="extract-blocks-checkbox" class="mr-2" checked>
+                        <label for="extract-blocks-checkbox" class="text-white">Extract Blocks</label>
+                    </div>
+                    <button id="crawl-btn" class="bg-blue-600 text-white px-4 py-2 rounded">Crawl</button>
+                </div>
+                <div class="grid grid-cols-1 md:grid-cols-2 gap-8">
+                    <div id="loading" class="hidden mt-4">
+                        <p>Loading...</p>
+                    </div>
+                    <div id="result" class="tab-container flex-1 h-full flex-col">
+                        <div class="tab-buttons flex gap-2">
+                            <button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="json">JSON</button>
+                            <button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="cleaned-html">
+                                Cleaned HTML
+                            </button>
+                            <button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="markdown">
+                                Markdown
+                            </button>
+                        </div>
+                        <div class="tab-content code bg-gray-800 p-2 rounded  h-full  flex-1 border border-gray-600">
+                            <pre class="h-full flex"><code id="json-result" class="language-json "></code></pre>
+                            <pre
+                                class="hidden h-full flex"
+                            ><code id="cleaned-html-result" class="language-html "></code></pre>
+                            <pre
+                                class="hidden h-full flex"
+                            ><code id="markdown-result" class="language-markdown "></code></pre>
+                        </div>
+                    </div>
+                    <div id="code_help" class="tab-container flex-1 h-full">
+                        <div class="tab-buttons flex gap-2">
+                            <button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="curl">cURL</button>
+                            <button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="python">
+                                Python
+                            </button>
+                            <button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="nodejs">
+                                Node.js
+                            </button>
+                        </div>
+                        <div class="tab-content result bg-gray-800 p-2 rounded h-full  flex-1 border border-gray-600">
+                            <pre class="h-full flex relative">
+                                    <code id="curl-code" class="language-bash"></code>
+                                    <button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
+                                </pre>
+                            <pre class="hidden h-full flex relative">
+                                    <code id="python-code" class="language-python"></code>
+                                    <button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
+                                </pre>
+                            <pre class="hidden h-full flex relative">
+                                    <code id="nodejs-code" class="language-javascript"></code>
+                                    <button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
+                                </pre>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </section>
+
+        <section class="hero bg-gray-900 py-8">
+            <div class="container mx-auto px-4">
+                <h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
+                <p class="text-lg mb-4">
+                    In recent times, we've seen numerous startups emerging, riding the AI hype wave and charging for
+                    services that should rightfully be accessible to everyone. 🌍💸 One for example is to scrap and crawl 
+                    a web page, and transform it o a form suitable for LLM. We don't think one should build a business
+                    out of this, but definilty should be opened source. So if you possess the skills to build such things 
+                    and you have such philosphy you should join our "Robinhood" band and help set
+                    these products free. 🆓🤝
+                </p>
+            </div>
+        </section>
+
+        <section class="installation py-8">
+            <div class="container mx-auto px-4">
+                <h2 class="text-2xl font-bold mb-4">⚙️ Installation</h2>
+                <p class="mb-4">
+                    To install and run Crawl4AI locally or on your own service, the best way is to use Docker. 🐳 Follow
+                    these steps:
+                </p>
+                <ol class="list-decimal list-inside mb-4">
+                    <li>
+                        Clone the GitHub repository: 📥
+                        <code>git clone https://github.com/unclecode/crawl4ai.git</code>
+                    </li>
+                    <li>Navigate to the project directory: 📂 <code>cd crawl4ai</code></li>
+                    <li>
+                        Build the Docker image: 🛠️ <code>docker build -t crawl4ai .</code> On Mac, follow: 🍎
+                        <code>docker build --platform linux/amd64 -t crawl4ai .</code>
+                    </li>
+                    <li>Run the Docker container: ▶️ <code>docker run -p 8000:80 crawl4ai</code></li>
+                </ol>
+                <p>
+                    For more detailed instructions and advanced configuration options, please refer to the 📚
+                    <a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
+                </p>
+            </div>
+        </section>
+
+        <footer class="bg-gray-900 text-white py-4">
+            <div class="container mx-auto px-4">
+                <div class="flex justify-between items-center">
+                    <p>© 2024 Crawl4AI. All rights reserved.</p>
+                    <div class="social-links">
+                        <a
+                            href="https://github.com/unclecode/crawl4ai"
+                            class="text-white hover:text-gray-300 mx-2"
+                            target="_blank"
+                            >😺 GitHub</a
+                        >
+                        <a
+                            href="https://twitter.com/unclecode"
+                            class="text-white hover:text-gray-300 mx-2"
+                            target="_blank"
+                            >🐦 Twitter</a
+                        >
+                        <a
+                            href="https://discord.gg/your-invite-link"
+                            class="text-white hover:text-gray-300 mx-2"
+                            target="_blank"
+                            >💬 Discord</a
+                        >
+                    </div>
+                </div>
+            </div>
+        </footer>
+
+        <script>
+            // Get the selected provider model and token from local storage
+            const storedProviderModel = localStorage.getItem("provider_model");
+            const storedToken = localStorage.getItem(storedProviderModel);
+
+            if (storedProviderModel) {
+                document.getElementById("provider-model-select").value = storedProviderModel;
+            }
+
+            if (storedToken) {
+                document.getElementById("token-input").value = storedToken;
+            }
+
+            // Handle provider model dropdown change
+            document.getElementById("provider-model-select").addEventListener("change", () => {
+                const selectedProviderModel = document.getElementById("provider-model-select").value;
+                const storedToken = localStorage.getItem(selectedProviderModel);
+
+                if (storedToken) {
+                    document.getElementById("token-input").value = storedToken;
+                } else {
+                    document.getElementById("token-input").value = "";
+                }
+            });
+
+            // Fetch total count from the database
+            axios
+                .get("/total-count")
+                .then((response) => {
+                    document.getElementById("total-count").textContent = response.data.count;
+                })
+                .catch((error) => console.error(error));
+
+            // Handle crawl button click
+            document.getElementById("crawl-btn").addEventListener("click", () => {
+                // validate input to have both URL and API token
+                if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
+                    alert("Please enter both URL(s) and API token.");
+                    return;
+                }
+
+                const selectedProviderModel = document.getElementById("provider-model-select").value;
+                const apiToken = document.getElementById("token-input").value;
+                const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
+
+
+                // Save the selected provider model and token to local storage
+                localStorage.setItem("provider_model", selectedProviderModel);
+                localStorage.setItem(selectedProviderModel, apiToken);
+
+                const urlsInput = document.getElementById("url-input").value;
+                const urls = urlsInput.split(",").map((url) => url.trim());
+                const data = {
+                    urls: urls,
+                    provider_model: selectedProviderModel,
+                    api_token: apiToken,
+                    include_raw_html: true,
+                    forced: false,
+                    extract_blocks: extractBlocks,
+                };
+
+                // save api token to local storage
+                localStorage.setItem("api_token", document.getElementById("token-input").value);
+
+                document.getElementById("loading").classList.remove("hidden");
+                document.getElementById("result").classList.add("hidden");
+                document.getElementById("code_help").classList.add("hidden");
+
+                axios
+                    .post("/crawl", data)
+                    .then((response) => {
+                        const result = response.data.results[0];
+                        const parsedJson = JSON.parse(result.extracted_content);
+                        document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
+                        document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
+                        document.getElementById("markdown-result").textContent = result.markdown;
+
+                        // Update code examples dynamically
+                        // Update code examples dynamically
+                        document.getElementById(
+                            "curl-code"
+                        ).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
+                            ...data,
+                            api_token: "your_api_token",
+                        })}' http://localhost:8000/crawl`;
+
+                        document.getElementById(
+                            "python-code"
+                        ).textContent = `import requests\n\ndata = ${JSON.stringify(
+                            { ...data, api_token: "your_api_token" },
+                            null,
+                            2
+                        )}\n\nresponse = requests.post("http://localhost:8000/crawl", json=data)\nprint(response.json())`;
+
+                        document.getElementById(
+                            "nodejs-code"
+                        ).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
+                            { ...data, api_token: "your_api_token" },
+                            null,
+                            2
+                        )};\n\naxios.post("http://localhost:8000/crawl", data)\n    .then(response => console.log(response.data))\n    .catch(error => console.error(error));`;
+                        // Highlight code syntax
+                        hljs.highlightAll();
+
+                        // Select JSON tab by default
+                        document.querySelector('.tab-btn[data-tab="json"]').click();
+
+                        document.getElementById("loading").classList.add("hidden");
+                        document.getElementById("result").classList.remove("hidden");
+                        document.getElementById("code_help").classList.remove("hidden");
+                    })
+                    .catch((error) => {
+                        console.error(error);
+                        document.getElementById("loading").classList.add("hidden");
+                    });
+            });
+
+            // Handle tab clicks
+            document.querySelectorAll(".tab-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const tab = btn.dataset.tab;
+                    document
+                        .querySelectorAll(".tab-btn")
+                        .forEach((b) => b.classList.remove("bg-blue-600", "text-white"));
+                    btn.classList.add("bg-blue-600", "text-white");
+                    document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
+                    document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
+                });
+            });
+
+            // Handle code tab clicks
+            document.querySelectorAll(".code-tab-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const tab = btn.dataset.tab;
+                    document
+                        .querySelectorAll(".code-tab-btn")
+                        .forEach((b) => b.classList.remove("bg-blue-600", "text-white"));
+                    btn.classList.add("bg-blue-600", "text-white");
+                    document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
+                    document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
+                });
+            });
+
+            // Handle copy to clipboard button clicks
+            document.querySelectorAll(".copy-btn").forEach((btn) => {
+                btn.addEventListener("click", () => {
+                    const target = btn.dataset.target;
+                    const code = document.getElementById(target).textContent;
+                    navigator.clipboard.writeText(code).then(() => {
+                        btn.textContent = "Copied!";
+                        setTimeout(() => {
+                            btn.textContent = "Copy";
+                        }, 2000);
+                    });
+                });
+            });
+
+
+            document.getElementById("crawl-btn").addEventListener("click", () => {
+                const urlsInput = document.getElementById("url-input").value;
+                const urls = urlsInput.split(",").map(url => url.trim());
+                const apiToken = document.getElementById("token-input").value;
+                const selectedProviderModel = document.getElementById("provider-model-select").value;
+                const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
+            
+                const data = {
+                    urls: urls,
+                    provider_model: selectedProviderModel,
+                    api_token: apiToken,
+                    include_raw_html: true,
+                    forced: false,
+                    extract_blocks: extractBlocks
+                };
+            
+                localStorage.setItem("api_token", apiToken);
+            
+                document.getElementById("loading").classList.remove("hidden");
+                document.getElementById("result").classList.add("hidden");
+                document.getElementById("code_help").classList.add("hidden");
+            
+                axios.post("/crawl", data)
+                    .then(response => {
+                        const taskId = response.data.task_id;
+                        pollTaskStatus(taskId);
+                    })
+                    .catch(error => {
+                        console.error('Error during fetch:', error);
+                        document.getElementById("loading").classList.add("hidden");
+                    });
+            });
+            
+            function pollTaskStatus(taskId) {
+                axios.get(`/task/${taskId}`)
+                    .then(response => {
+                        const task = response.data;
+                        if (task.status === 'done') {
+                            displayResults(task.results[0]);
+                        } else if (task.status === 'pending') {
+                            setTimeout(() => pollTaskStatus(taskId), 2000);  // Poll every 2 seconds
+                        } else {
+                            console.error('Task failed:', task.error);
+                            document.getElementById("loading").classList.add("hidden");
+                        }
+                    })
+                    .catch(error => {
+                        console.error('Error polling task status:', error);
+                        document.getElementById("loading").classList.add("hidden");
+                    });
+            }
+        </script>
+    </body>
+</html>

pages/partial/footer.html

@@ -0,0 +1,36 @@
+<section class="hero bg-zinc-900 py-8 px-20 text-zinc-400">
+    <div class="container mx-auto px-4">
+        <h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
+        <p class="text-lg mb-4">
+            In recent times, we've witnessed a surge of startups emerging, riding the AI hype wave and charging
+            for services that should rightfully be accessible to everyone. 🌍💸 One such example is scraping and
+            crawling web pages and transforming them into a format suitable for Large Language Models (LLMs).
+            🕸️🤖 We believe that building a business around this is not the right approach; instead, it should
+            definitely be open-source. 🆓🌟 So, if you possess the skills to build such tools and share our
+            philosophy, we invite you to join our "Robinhood" band and help set these products free for the
+            benefit of all. 🤝💪
+        </p>
+    </div>
+</section>
+
+<footer class="bg-zinc-900 text-zinc-400 py-4">
+    <div class="container mx-auto px-4">
+        <div class="flex justify-between items-center">
+            <p>© 2024 Crawl4AI. All rights reserved.</p>
+            <div class="social-links">
+                <a
+                    href="https://github.com/unclecode/crawl4ai"
+                    class="text-zinc-400 hover:text-gray-300 mx-2"
+                    target="_blank"
+                    >😺 GitHub</a
+                >
+                <a
+                    href="https://twitter.com/unclecode"
+                    class="text-zinc-400 hover:text-gray-300 mx-2"
+                    target="_blank"
+                    >🐦 Twitter</a
+                >
+            </div>
+        </div>
+    </div>
+</footer>

pages/partial/how_to_guide.html

@@ -0,0 +1,174 @@
+<section id="how-to-guide" class="content-section">
+    <h1 class="text-2xl font-bold">How to Guide</h1>
+    <div class="flex flex-col gap-4 p-4 bg-zinc-900 text-lime-500">
+        <!-- Step 1 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🌟
+            <strong
+                >Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling
+                fun!</strong
+            >
+        </div>
+        <div class="">
+            First Step: Create an instance of WebCrawler and call the
+            <code>warmup()</code> function.
+        </div>
+        <div>
+            <pre><code class="language-python">crawler = WebCrawler()
+crawler.warmup()</code></pre>
+        </div>
+
+        <!-- Step 2 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
+        </div>
+        <div class="">First crawl (caches the result):</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
+        </div>
+        <div class="">Second crawl (Force to crawl again):</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)</code></pre>
+            <div class="bg-red-900 p-2 text-zinc-50">
+                ⚠️ Don't forget to set <code>`bypass_cache`</code> to True if you want to try different strategies for the same URL. Otherwise, the cached result will be returned. You can also set <code>`always_by_pass_cache`</code> in constructor to True to always bypass the cache.
+            </div>
+        </div>
+        <div class="">Crawl result without raw HTML content:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", include_raw_html=False)</code></pre>
+        </div>
+
+        <!-- Step 3 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            📄
+            <strong
+                >The 'include_raw_html' parameter, when set to True, includes the raw HTML content
+                in the response. By default, it is set to True.</strong
+            >
+        </div>
+        <div class="">Set <code>always_by_pass_cache</code> to True:</div>
+        <div>
+            <pre><code class="language-python">crawler.always_by_pass_cache = True</code></pre>
+        </div>
+        <!-- Step 3.5 Screenshot -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            📸
+            <strong>Let's take a screenshot of the page!</strong>
+        </div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    screenshot=True
+)
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result.screenshot))</code></pre>
+        </div>
+
+
+        <!-- Step 4 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
+        </div>
+        <div class="">Using RegexChunking:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    chunking_strategy=RegexChunking(patterns=["\n\n"])
+)</code></pre>
+        </div>
+        <div class="">Using NlpSentenceChunking:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    chunking_strategy=NlpSentenceChunking()
+)</code></pre>
+        </div>
+
+        <!-- Step 5 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
+        </div>
+        <div class="">Using CosineStrategy:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method="ward", top_k=3)
+)</code></pre>
+        </div>
+
+        <!-- Step 6 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🤖
+            <strong
+                >Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong
+            >
+        </div>
+        <div class="">Using LLMExtractionStrategy without instructions:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=LLMExtractionStrategy(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'))
+)</code></pre>
+        </div>
+
+        <!-- Step 7 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            📜
+            <strong
+                >Let's make it even more interesting: LLMExtractionStrategy with
+                instructions!</strong
+            >
+        </div>
+        <div class="">Using LLMExtractionStrategy with instructions:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=LLMExtractionStrategy(
+    provider="openai/gpt-4o",
+    api_token=os.getenv('OPENAI_API_KEY'),
+    instruction="I am interested in only financial news"
+)
+)</code></pre>
+        </div>
+
+        <!-- Step 8 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🎯
+            <strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
+        </div>
+        <div class="">Using CSS selector to extract H2 tags:</div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    css_selector="h2"
+)</code></pre>
+        </div>
+
+        <!-- Step 9 -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🖱️
+            <strong
+                >Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong
+            >
+        </div>
+        <div class="">Using JavaScript to click 'Load More' button:</div>
+        <div>
+            <pre><code class="language-python">js_code = ["""
+const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+loadMoreButton && loadMoreButton.click();
+"""]
+crawler = WebCrawler(verbos=crawler_strategy, always_by_pass_cache=True)
+result = crawler.run(url="https://www.nbcnews.com/business", js = js_code)</code></pre>
+        <div class="">Remember that you can pass multiple JavaScript code snippets in the list. They all will be executed in the order they are passed.</div>
+        </div>
+
+        <!-- Conclusion -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            🎉
+            <strong
+                >Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth
+                and crawl the web like a pro! 🕸️</strong
+            >
+        </div>
+    </div>
+</section>

pages/partial/installation.html

@@ -0,0 +1,65 @@
+<section id="installation" class="content-section active">
+    <h1 class="text-2xl font-bold">Installation 💻</h1>
+    <p class="mb-4">
+        There are three ways to use Crawl4AI: 
+        <ol class="list-decimal list-inside mb-4">
+            <li class="">
+                As a library
+            </li>
+            <li class="">
+                As a local server (Docker)
+            </li>
+            <li class="">
+                As a Google Colab notebook. <a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
+                ><img
+                    src="https://colab.research.google.com/assets/colab-badge.svg"
+                    alt="Open In Colab"
+                    style="display: inline-block; width: 100px; height: 20px"
+            /></a>
+            </li>
+    </p>
+
+
+    <p class="my-4">To install Crawl4AI as a library, follow these steps:</p>
+
+    <ol class="list-decimal list-inside mb-4">
+        <li class="mb-4">
+            Install the package from GitHub:
+            <pre
+                class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+            ><code>virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai[all] @ git+https://github.com/unclecode/crawl4ai.git"
+            </code></pre>
+        </li>
+        <li class="mb-4">
+            Run the following command to load the required models. This is optional, but it will boost the performance and speed of the crawler. You need to do this only once.
+            <pre
+                class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+            ><code>crawl4ai-download-models</code></pre>
+        </li>
+        <li class="mb-4">
+            Alternatively, you can clone the repository and install the package locally:
+            <pre
+                class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+            ><code  class = "language-python bash">virtualenv venv
+source venv/bin/activate
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e .[all]
+</code></pre>
+        </li>
+        <li class="">
+            Use docker to run the local server:
+            <pre
+                class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+            ><code  class = "language-python bash">docker build -t crawl4ai . 
+# docker build --platform linux/amd64 -t crawl4ai . For Mac users
+docker run -d -p 8000:80 crawl4ai</code></pre>
+        </li>
+    </ol>
+    <p class="mb-4">
+        For more information about how to run Crawl4AI as a local server, please refer to the
+        <a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
+    </p>
+</section>

pages/partial/try_it.html

@@ -0,0 +1,217 @@
+<section class="try-it py-8 px-16 pb-20 bg-zinc-900 overflow-hidden">
+    <div class="container mx-auto ">
+        <h2 class="text-2xl font-bold mb-4 text-lime-500">Try It Now</h2>
+        <div class="flex gap-4">
+            <div class="flex flex-col flex-1 gap-2">
+                <div class="flex flex-col">
+                    <label for="url-input" class="text-lime-500 font-bold text-xs">URL(s)</label>
+                    <input
+                        type="text"
+                        id="url-input"
+                        value="https://www.nbcnews.com/business"
+                        class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300"
+                        placeholder="Enter URL(s) separated by commas"
+                    />
+                </div>
+                <div class="flex gap-2">
+                    <div class="flex flex-col">
+                        <label for="threshold" class="text-lime-500 font-bold text-xs">Min Words Threshold</label>
+                        <select
+                            id="threshold"
+                            class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
+                        >
+                            <option value="1">1</option>
+                            <option value="5">5</option>
+                            <option value="10" selected>10</option>
+                            <option value="15">15</option>
+                            <option value="20">20</option>
+                            <option value="25">25</option>
+                        </select>
+                    </div>
+                    <div class="flex flex-col flex-1">
+                        <label for="css-selector" class="text-lime-500 font-bold text-xs">CSS Selector</label>
+                        <input
+                            type="text"
+                            id="css-selector"
+                            class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-lime-700"
+                            placeholder="CSS Selector (e.g. .content, #main, article)"
+                        />
+                    </div>
+                </div>
+                <div class="flex gap-2">
+                    <div class="flex flex-col">
+                        <label for="extraction-strategy-select" class="text-lime-500 font-bold text-xs"
+                            >Extraction Strategy</label
+                        >
+                        <select
+                            id="extraction-strategy-select"
+                            class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
+                        >
+                            <option value="NoExtractionStrategy" selected>NoExtractionStrategy</option>
+                            <option value="CosineStrategy">CosineStrategy</option>
+                            <option value="LLMExtractionStrategy">LLMExtractionStrategy</option>
+                        </select>
+                    </div>
+                    <div class="flex flex-col">
+                        <label for="chunking-strategy-select" class="text-lime-500 font-bold text-xs"
+                            >Chunking Strategy</label
+                        >
+                        <select
+                            id="chunking-strategy-select"
+                            class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
+                        >
+                            <option value="RegexChunking">RegexChunking</option>
+                            <option value="NlpSentenceChunking">NlpSentenceChunking</option>
+                            <option value="TopicSegmentationChunking">TopicSegmentationChunking</option>
+                            <option value="FixedLengthWordChunking">FixedLengthWordChunking</option>
+                            <option value="SlidingWindowChunking">SlidingWindowChunking</option>
+                        </select>
+                    </div>
+                </div>
+                <div id = "llm_settings" class="flex gap-2 hidden hidden">
+                    <div class="flex flex-col">
+                        <label for="provider-model-select" class="text-lime-500 font-bold text-xs"
+                            >Provider Model</label
+                        >
+                        <select
+                            id="provider-model-select"
+                            class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
+                        >
+                            <option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
+                            <option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
+                            <option value="groq/mixtral-8x7b-32768">groq/mixtral-8x7b-32768</option>
+                            <option value="openai/gpt-4-turbo">gpt-4-turbo</option>
+                            <option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
+                            <option value="openai/gpt-4o">gpt-4o</option>
+                            <option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
+                            <option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
+                            <option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
+                        </select>
+                    </div>
+                    <div class="flex flex-col flex-1">
+                        <label for="token-input" class="text-lime-500 font-bold text-xs">API Token</label>
+                        <input
+                            type="password"
+                            id="token-input"
+                            class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300"
+                            placeholder="Enter Groq API token"
+                        />
+                    </div>
+                </div>
+                <div  class="flex gap-2">
+                    <!-- Add two textarea one for getting Keyword Filter and another one Instruction, make both grow whole with-->
+                    <div id = "semantic_filter_div" class="flex flex-col flex-1 hidden">
+                        <label for="keyword-filter" class="text-lime-500 font-bold text-xs">Keyword Filter</label>
+                        <textarea
+                            id="semantic_filter"
+                            rows="3"
+                            class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-zinc-700"
+                            placeholder="Enter keywords for CosineStrategy to narrow down the content."
+                        ></textarea>
+                    </div>
+                    <div id = "instruction_div" class="flex flex-col flex-1 hidden">
+                        <label for="instruction" class="text-lime-500 font-bold text-xs">Instruction</label>
+                        <textarea
+                            id="instruction"
+                            rows="3"
+                            class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-zinc-700"
+                            placeholder="Enter instruction for the LLMEstrategy to instruct the model."
+                        ></textarea>
+                    </div>
+                </div>
+                <div class="flex gap-3">
+                    <div class="flex items-center gap-2">
+                        <input type="checkbox" id="bypass-cache-checkbox" />
+                        <label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
+                    </div>
+                    <div class="flex items-center gap-2">
+                        <input type="checkbox" id="screenshot-checkbox" checked />
+                        <label for="screenshot-checkbox" class="text-lime-500 font-bold">Screenshot</label>
+                    </div>
+                    <div class="flex items-center gap-2 hidden">
+                        <input type="checkbox" id="extract-blocks-checkbox" />
+                        <label for="extract-blocks-checkbox" class="text-lime-500 font-bold">Extract Blocks</label>
+                    </div>
+                    <button id="crawl-btn" class="bg-lime-600 text-black font-bold px-4 py-0 rounded">Crawl</button>
+                </div>
+            </div>
+
+            <div id="loading" class="hidden">
+                <p class="text-white">Loading... Please wait.</p>
+            </div>
+            <div id="result" class="flex-1  overflow-x-auto">
+                <div class="tab-buttons flex gap-2">
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="json">
+                        JSON
+                    </button>
+                    <button
+                        class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                        data-tab="cleaned-html"
+                    >
+                        Cleaned HTML
+                    </button>
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="markdown">
+                        Markdown
+                    </button>
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="media">
+                        Medias
+                    </button>
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="screenshot">
+                        Screenshot
+                    </button>
+                </div>
+                <div class="tab-content code bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
+                    <pre class="h-full flex"><code id="json-result" class="language-json"></code></pre>
+                    <pre class="hidden h-full flex"><code id="cleaned-html-result" class="language-html"></code></pre>
+                    <pre class="hidden h-full flex"><code id="markdown-result" class="language-markdown"></code></pre>
+                    <pre class="hidden h-full flex"><code id="media-result" class="language-json"></code></pre>
+                    <pre class="hidden h-full flex"><code id="screenshot-result"></code></pre>
+                </div>
+            </div>
+
+            <div id="code_help" class="flex-1  overflow-x-auto">
+                <div class="tab-buttons flex gap-2">
+                    <button class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="curl">
+                        cURL
+                    </button>
+                    <button
+                        class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                        data-tab="library"
+                    >
+                        Python
+                    </button>
+                    <button
+                        class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                        data-tab="python"
+                    >
+                        REST API
+                    </button>
+                    <!-- <button
+                        class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
+                        data-tab="nodejs"
+                    >
+                        Node.js
+                    </button> -->
+                </div>
+                <div class="tab-content result bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
+                    <pre class="h-full flex relative overflow-x-auto">
+                        <code id="curl-code" class="language-bash"></code>
+                        <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
+                    </pre>
+                    <pre class="hidden h-full flex relative overflow-x-auto">
+                        <code id="python-code" class="language-python"></code>
+                        <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
+                    </pre>
+                    <pre class="hidden h-full flex relative overflow-x-auto">
+                        <code id="nodejs-code" class="language-javascript"></code>
+                        <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
+                    </pre>
+                    <pre class="hidden h-full flex relative overflow-x-auto">
+                        <code id="library-code" class="language-python"></code>
+                        <button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="library-code">Copy</button>
+                    </pre>
+                </div>
+            </div>
+        </div>
+    </div>
+</section>

pages/tmp.html

@@ -0,0 +1,434 @@
+<div class="w-3/4 p-4">
+    <section id="installation" class="content-section active">
+        <h1 class="text-2xl font-bold">Installation 💻</h1>
+        <p class="mb-4">There are three ways to use Crawl4AI:</p>
+        <ol class="list-decimal list-inside mb-4">
+            <li class="">As a library</li>
+            <li class="">As a local server (Docker)</li>
+            <li class="">
+                As a Google Colab notebook.
+                <a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
+                    ><img
+                        src="https://colab.research.google.com/assets/colab-badge.svg"
+                        alt="Open In Colab"
+                        style="display: inline-block; width: 100px; height: 20px"
+                /></a>
+            </li>
+            <p></p>
+
+            <p class="my-4">To install Crawl4AI as a library, follow these steps:</p>
+
+            <ol class="list-decimal list-inside mb-4">
+                <li class="mb-4">
+                    Install the package from GitHub:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code class="hljs language-bash">pip install git+https://github.com/unclecode/crawl4ai.git</code></pre>
+                </li>
+                <li class="mb-4">
+                    Alternatively, you can clone the repository and install the package locally:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code class="language-python bash hljs">virtualenv venv
+source venv/<span class="hljs-built_in">bin</span>/activate
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e .
+</code></pre>
+                </li>
+                <li class="">
+                    Use docker to run the local server:
+                    <pre
+                        class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
+                    ><code class="language-python bash hljs">docker build -t crawl4ai . 
+<span class="hljs-comment"># docker build --platform linux/amd64 -t crawl4ai . For Mac users</span>
+docker run -d -p <span class="hljs-number">8000</span>:<span class="hljs-number">80</span> crawl4ai</code></pre>
+                </li>
+            </ol>
+            <p class="mb-4">
+                For more information about how to run Crawl4AI as a local server, please refer to the
+                <a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
+            </p>
+        </ol>
+    </section>
+    <section id="how-to-guide" class="content-section">
+        <h1 class="text-2xl font-bold">How to Guide</h1>
+        <div class="flex flex-col gap-4 p-4 bg-zinc-900 text-lime-500">
+            <!-- Step 1 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🌟
+                <strong>Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling fun!</strong>
+            </div>
+            <div class="">
+                First Step: Create an instance of WebCrawler and call the
+                <code>warmup()</code> function.
+            </div>
+            <div>
+                <pre><code class="language-python hljs">crawler = WebCrawler()
+crawler.warmup()</code></pre>
+            </div>
+
+            <!-- Step 2 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
+            </div>
+            <div class="">First crawl (caches the result):</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>)</code></pre>
+            </div>
+            <div class="">Second crawl (Force to crawl again):</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>, bypass_cache=<span class="hljs-literal">True</span>)</code></pre>
+                <div class="bg-red-900 p-2 text-zinc-50">
+                    ⚠️ Don't forget to set <code>`bypass_cache`</code> to True if you want to try different strategies
+                    for the same URL. Otherwise, the cached result will be returned. You can also set
+                    <code>`always_by_pass_cache`</code> in constructor to True to always bypass the cache.
+                </div>
+            </div>
+            <div class="">Crawl result without raw HTML content:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>, include_raw_html=<span class="hljs-literal">False</span>)</code></pre>
+            </div>
+
+            <!-- Step 3 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                📄
+                <strong
+                    >The 'include_raw_html' parameter, when set to True, includes the raw HTML content in the response.
+                    By default, it is set to True.</strong
+                >
+            </div>
+            <div class="">Set <code>always_by_pass_cache</code> to True:</div>
+            <div>
+                <pre><code class="language-python hljs">crawler.always_by_pass_cache = <span class="hljs-literal">True</span></code></pre>
+            </div>
+
+            <!-- Step 4 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
+            </div>
+            <div class="">Using RegexChunking:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+chunking_strategy=RegexChunking(patterns=[<span class="hljs-string">"\n\n"</span>])
+)</code></pre>
+            </div>
+            <div class="">Using NlpSentenceChunking:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+chunking_strategy=NlpSentenceChunking()
+)</code></pre>
+            </div>
+
+            <!-- Step 5 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
+            </div>
+            <div class="">Using CosineStrategy:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+extraction_strategy=CosineStrategy(word_count_threshold=<span class="hljs-number">20</span>, max_dist=<span class="hljs-number">0.2</span>, linkage_method=<span class="hljs-string">"ward"</span>, top_k=<span class="hljs-number">3</span>)
+)</code></pre>
+            </div>
+
+            <!-- Step 6 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🤖
+                <strong>Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong>
+            </div>
+            <div class="">Using LLMExtractionStrategy without instructions:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+extraction_strategy=LLMExtractionStrategy(provider=<span class="hljs-string">"openai/gpt-4o"</span>, api_token=os.getenv(<span class="hljs-string">'OPENAI_API_KEY'</span>))
+)</code></pre>
+            </div>
+
+            <!-- Step 7 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                📜
+                <strong>Let's make it even more interesting: LLMExtractionStrategy with instructions!</strong>
+            </div>
+            <div class="">Using LLMExtractionStrategy with instructions:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+extraction_strategy=LLMExtractionStrategy(
+provider=<span class="hljs-string">"openai/gpt-4o"</span>,
+api_token=os.getenv(<span class="hljs-string">'OPENAI_API_KEY'</span>),
+instruction=<span class="hljs-string">"I am interested in only financial news"</span>
+)
+)</code></pre>
+            </div>
+
+            <!-- Step 8 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🎯
+                <strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
+            </div>
+            <div class="">Using CSS selector to extract H2 tags:</div>
+            <div>
+                <pre><code class="language-python hljs">result = crawler.run(
+url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
+css_selector=<span class="hljs-string">"h2"</span>
+)</code></pre>
+            </div>
+
+            <!-- Step 9 -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🖱️
+                <strong>Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong>
+            </div>
+            <div class="">Using JavaScript to click 'Load More' button:</div>
+            <div>
+                <pre><code class="language-python hljs">js_code = <span class="hljs-string">"""
+const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button =&gt; button.textContent.includes('Load More'));
+loadMoreButton &amp;&amp; loadMoreButton.click();
+"""</span>
+crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=<span class="hljs-literal">True</span>)
+result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>)</code></pre>
+            </div>
+
+            <!-- Conclusion -->
+            <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+                🎉
+                <strong
+                    >Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl the
+                    web like a pro! 🕸️</strong
+                >
+            </div>
+        </div>
+    </section>
+
+    <section id="chunking-strategies" class="content-section">
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>RegexChunking</h3>
+                <p>
+                    <code>RegexChunking</code> is a text chunking strategy that splits a given text into smaller parts
+                    using regular expressions. This is useful for preparing large texts for processing by language
+                    models, ensuring they are divided into manageable segments.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>patterns</code> (list, optional): A list of regular expression patterns used to split the
+                        text. Default is to split by double newlines (<code>['\n\n']</code>).
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">chunker = RegexChunking(patterns=[r'\n\n', r'\. '])
+chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
+</code></pre>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>NlpSentenceChunking</h3>
+                <p>
+                    <code>NlpSentenceChunking</code> uses a natural language processing model to chunk a given text into
+                    sentences. This approach leverages SpaCy to accurately split text based on sentence boundaries.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        None.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">chunker = NlpSentenceChunking()
+chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
+</code></pre>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>TopicSegmentationChunking</h3>
+                <p>
+                    <code>TopicSegmentationChunking</code> uses the TextTiling algorithm to segment a given text into
+                    topic-based chunks. This method identifies thematic boundaries in the text.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>num_keywords</code> (int, optional): The number of keywords to extract for each topic
+                        segment. Default is <code>3</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">chunker = TopicSegmentationChunking(num_keywords=3)
+chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
+</code></pre>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>FixedLengthWordChunking</h3>
+                <p>
+                    <code>FixedLengthWordChunking</code> splits a given text into chunks of fixed length, based on the
+                    number of words.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>chunk_size</code> (int, optional): The number of words in each chunk. Default is
+                        <code>100</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">chunker = FixedLengthWordChunking(chunk_size=100)
+chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
+</code></pre>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>SlidingWindowChunking</h3>
+                <p>
+                    <code>SlidingWindowChunking</code> uses a sliding window approach to chunk a given text. Each chunk
+                    has a fixed length, and the window slides by a specified step size.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>window_size</code> (int, optional): The number of words in each chunk. Default is
+                        <code>100</code>.
+                    </li>
+                    <li>
+                        <code>step</code> (int, optional): The number of words to slide the window. Default is
+                        <code>50</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">chunker = SlidingWindowChunking(window_size=100, step=50)
+chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
+</code></pre>
+            </div>
+        </div>
+    </section>
+    <section id="extraction-strategies" class="content-section">
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>NoExtractionStrategy</h3>
+                <p>
+                    <code>NoExtractionStrategy</code> is a basic extraction strategy that returns the entire HTML
+                    content without any modification. It is useful for cases where no specific extraction is required.
+                    Only clean html, and amrkdown.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <p>None.</p>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">extractor = NoExtractionStrategy()
+extracted_content = extractor.extract(url, html)
+</code></pre>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>LLMExtractionStrategy</h3>
+                <p>
+                    <code>LLMExtractionStrategy</code> uses a Language Model (LLM) to extract meaningful blocks or
+                    chunks from the given HTML content. This strategy leverages an external provider for language model
+                    completions.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>provider</code> (str, optional): The provider to use for the language model completions.
+                        Default is <code>DEFAULT_PROVIDER</code> (e.g., openai/gpt-4).
+                    </li>
+                    <li>
+                        <code>api_token</code> (str, optional): The API token for the provider. If not provided, it will
+                        try to load from the environment variable <code>OPENAI_API_KEY</code>.
+                    </li>
+                    <li>
+                        <code>instruction</code> (str, optional): An instruction to guide the LLM on how to perform the
+                        extraction. This allows users to specify the type of data they are interested in or set the tone
+                        of the response. Default is <code>None</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
+extracted_content = extractor.extract(url, html)
+</code></pre>
+                <p>
+                    By providing clear instructions, users can tailor the extraction process to their specific needs,
+                    enhancing the relevance and utility of the extracted content.
+                </p>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>CosineStrategy</h3>
+                <p>
+                    <code>CosineStrategy</code> uses hierarchical clustering based on cosine similarity to extract
+                    clusters of text from the given HTML content. This strategy is suitable for identifying related
+                    content sections.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>semantic_filter</code> (str, optional): A string containing keywords for filtering relevant
+                        documents before clustering. If provided, documents are filtered based on their cosine
+                        similarity to the keyword filter embedding. Default is <code>None</code>.
+                    </li>
+                    <li>
+                        <code>word_count_threshold</code> (int, optional): Minimum number of words per cluster. Default
+                        is <code>20</code>.
+                    </li>
+                    <li>
+                        <code>max_dist</code> (float, optional): The maximum cophenetic distance on the dendrogram to
+                        form clusters. Default is <code>0.2</code>.
+                    </li>
+                    <li>
+                        <code>linkage_method</code> (str, optional): The linkage method for hierarchical clustering.
+                        Default is <code>'ward'</code>.
+                    </li>
+                    <li>
+                        <code>top_k</code> (int, optional): Number of top categories to extract. Default is
+                        <code>3</code>.
+                    </li>
+                    <li>
+                        <code>model_name</code> (str, optional): The model name for embedding generation. Default is
+                        <code>'BAAI/bge-small-en-v1.5'</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">extractor = CosineStrategy(semantic_filter='artificial intelligence', word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3, model_name='BAAI/bge-small-en-v1.5')
+extracted_content = extractor.extract(url, html)
+</code></pre>
+                <h4>Cosine Similarity Filtering</h4>
+                <p>
+                    When a <code>semantic_filter</code> is provided, the <code>CosineStrategy</code> applies an
+                    embedding-based filtering process to select relevant documents before performing hierarchical
+                    clustering.
+                </p>
+            </div>
+        </div>
+        <div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
+            <div class="text-gray-300 prose prose-sm">
+                <h3>TopicExtractionStrategy</h3>
+                <p>
+                    <code>TopicExtractionStrategy</code> uses the TextTiling algorithm to segment the HTML content into
+                    topics and extracts keywords for each segment. This strategy is useful for identifying and
+                    summarizing thematic content.
+                </p>
+                <h4>Constructor Parameters:</h4>
+                <ul>
+                    <li>
+                        <code>num_keywords</code> (int, optional): Number of keywords to represent each topic segment.
+                        Default is <code>3</code>.
+                    </li>
+                </ul>
+                <h4>Example usage:</h4>
+                <pre><code class="language-python">extractor = TopicExtractionStrategy(num_keywords=3)
+extracted_content = extractor.extract(url, html)
+</code></pre>
+            </div>
+        </div>
+    </section>
+</div>

requirements-dev.txt

@@ -0,0 +1,5 @@
+-r requirements.txt
+pytest
+pytest-asyncio
+selenium
+setuptools

requirements.txt

@@ -8,9 +8,4 @@ playwright>=1.47,<1.48
 python-dotenv~=1.0
 requests~=2.26
 beautifulsoup4~=4.12
-tf-playwright-stealth~=1.0
-xxhash~=3.4
-rank-bm25~=0.2
-aiofiles~=24.0
-colorama~=0.4
-snowballstemmer~=2.2
+playwright_stealth~=1.0

setup.py

@@ -5,51 +5,39 @@ from pathlib import Path
 import shutil
 import subprocess
 import sys
-import asyncio
 
 # Create the .crawl4ai folder in the user's home directory if it doesn't exist
 # If the folder already exists, remove the cache folder
-base_dir = os.getenv("CRAWL4_AI_BASE_DIRECTORY")
-crawl4ai_folder = Path(base_dir) if base_dir else Path.home()
-crawl4ai_folder = crawl4ai_folder / ".crawl4ai"
+crawl4ai_folder = os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()) / ".crawl4ai"
 cache_folder = crawl4ai_folder / "cache"
-content_folders = [
-    "html_content",
-    "cleaned_html",
-    "markdown_content",
-    "extracted_content",
-    "screenshots",
-]
 
-# Clean up old cache if exists
 if cache_folder.exists():
     shutil.rmtree(cache_folder)
 
-# Create new folder structure
 crawl4ai_folder.mkdir(exist_ok=True)
 cache_folder.mkdir(exist_ok=True)
-for folder in content_folders:
-    (crawl4ai_folder / folder).mkdir(exist_ok=True)
 
-# Read requirements and version
+# Read the requirements from requirements.txt
 __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
 with open(os.path.join(__location__, "requirements.txt")) as f:
     requirements = f.read().splitlines()
-
-with open("crawl4ai/__version__.py") as f:
+    
+# Read version from __init__.py
+with open("crawl4ai/_version.py") as f:
     for line in f:
         if line.startswith("__version__"):
             version = line.split("=")[1].strip().strip('"')
             break
 
-# Define requirements
+# Define the requirements for different environments
 default_requirements = requirements
-torch_requirements = ["torch", "nltk", "scikit-learn"]
+# torch_requirements = ["torch", "nltk", "spacy", "scikit-learn"]
+# transformer_requirements = ["transformers", "tokenizers", "onnxruntime"]
+torch_requirements = ["torch", "nltk",  "scikit-learn"]
 transformer_requirements = ["transformers", "tokenizers"]
-cosine_similarity_requirements = ["torch", "transformers", "nltk"]
+cosine_similarity_requirements = ["torch", "transformers", "nltk" ]
 sync_requirements = ["selenium"]
 
-
 def install_playwright():
     print("Installing Playwright browsers...")
     try:
@@ -57,37 +45,15 @@ def install_playwright():
         print("Playwright installation completed successfully.")
     except subprocess.CalledProcessError as e:
         print(f"Error during Playwright installation: {e}")
-        print(
-            "Please run 'python -m playwright install' manually after the installation."
-        )
+        print("Please run 'python -m playwright install' manually after the installation.")
     except Exception as e:
         print(f"Unexpected error during Playwright installation: {e}")
-        print(
-            "Please run 'python -m playwright install' manually after the installation."
-        )
-
-
-def run_migration():
-    """Initialize database during installation"""
-    try:
-        print("Starting database initialization...")
-        from crawl4ai.async_database import async_db_manager
-
-        asyncio.run(async_db_manager.initialize())
-        print("Database initialization completed successfully.")
-    except ImportError:
-        print("Warning: Database module not found. Will initialize on first use.")
-    except Exception as e:
-        print(f"Warning: Database initialization failed: {e}")
-        print("Database will be initialized on first use")
-
+        print("Please run 'python -m playwright install' manually after the installation.")
 
 class PostInstallCommand(install):
     def run(self):
         install.run(self)
         install_playwright()
-        # run_migration()
-
 
 setup(
     name="Crawl4AI",
@@ -100,23 +66,17 @@ setup(
     author_email="unclecode@kidocode.com",
     license="MIT",
     packages=find_packages(),
-    install_requires=default_requirements
-    + ["playwright", "aiofiles"],  # Added aiofiles
+    install_requires=default_requirements + ["playwright"],  # Add playwright to default requirements
     extras_require={
         "torch": torch_requirements,
         "transformer": transformer_requirements,
         "cosine": cosine_similarity_requirements,
         "sync": sync_requirements,
-        "all": default_requirements
-        + torch_requirements
-        + transformer_requirements
-        + cosine_similarity_requirements
-        + sync_requirements,
+        "all": default_requirements + torch_requirements + transformer_requirements + cosine_similarity_requirements + sync_requirements,
     },
     entry_points={
-        "console_scripts": [
-            "crawl4ai-download-models=crawl4ai.model_loader:main",
-            "crawl4ai-migrate=crawl4ai.migrations:main",  # Added migration command
+        'console_scripts': [
+            'crawl4ai-download-models=crawl4ai.model_loader:main',
         ],
     },
     classifiers=[
@@ -131,6 +91,6 @@ setup(
     ],
     python_requires=">=3.7",
     cmdclass={
-        "install": PostInstallCommand,
+        'install': PostInstallCommand,
     },
-)
+)

tests/async/test_async_doanloader.py

@@ -1,229 +0,0 @@
-import os
-import sys
-import asyncio
-import shutil
-from typing import List
-import tempfile
-import time
-
-# Add the parent directory to the Python path
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-
-from crawl4ai.async_webcrawler import AsyncWebCrawler
-
-class TestDownloads:
-    def __init__(self):
-        self.temp_dir = tempfile.mkdtemp(prefix="crawl4ai_test_")
-        self.download_dir = os.path.join(self.temp_dir, "downloads")
-        os.makedirs(self.download_dir, exist_ok=True)
-        self.results: List[str] = []
-        
-    def cleanup(self):
-        shutil.rmtree(self.temp_dir)
-        
-    def log_result(self, test_name: str, success: bool, message: str = ""):
-        result = f"{'✅' if success else '❌'} {test_name}: {message}"
-        self.results.append(result)
-        print(result)
-        
-    async def test_basic_download(self):
-        """Test basic file download functionality"""
-        try:
-            async with AsyncWebCrawler(
-                accept_downloads=True,
-                downloads_path=self.download_dir,
-                verbose=True
-            ) as crawler:
-                # Python.org downloads page typically has stable download links
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="""
-                    // Click first download link
-                    const downloadLink = document.querySelector('a[href$=".exe"]');
-                    if (downloadLink) downloadLink.click();
-                    """
-                )
-                
-                success = result.downloaded_files is not None and len(result.downloaded_files) > 0
-                self.log_result(
-                    "Basic Download",
-                    success,
-                    f"Downloaded {len(result.downloaded_files or [])} files" if success else "No files downloaded"
-                )
-        except Exception as e:
-            self.log_result("Basic Download", False, str(e))
-
-    async def test_persistent_context_download(self):
-        """Test downloads with persistent context"""
-        try:
-            user_data_dir = os.path.join(self.temp_dir, "user_data")
-            os.makedirs(user_data_dir, exist_ok=True)
-            
-            async with AsyncWebCrawler(
-                accept_downloads=True,
-                downloads_path=self.download_dir,
-                use_persistent_context=True,
-                user_data_dir=user_data_dir,
-                verbose=True
-            ) as crawler:
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="""
-                    const downloadLink = document.querySelector('a[href$=".exe"]');
-                    if (downloadLink) downloadLink.click();
-                    """
-                )
-                
-                success = result.downloaded_files is not None and len(result.downloaded_files) > 0
-                self.log_result(
-                    "Persistent Context Download",
-                    success,
-                    f"Downloaded {len(result.downloaded_files or [])} files" if success else "No files downloaded"
-                )
-        except Exception as e:
-            self.log_result("Persistent Context Download", False, str(e))
-
-    async def test_multiple_downloads(self):
-        """Test multiple simultaneous downloads"""
-        try:
-            async with AsyncWebCrawler(
-                accept_downloads=True,
-                downloads_path=self.download_dir,
-                verbose=True
-            ) as crawler:
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="""
-                    // Click multiple download links
-                    const downloadLinks = document.querySelectorAll('a[href$=".exe"]');
-                    downloadLinks.forEach(link => link.click());
-                    """
-                )
-                
-                success = result.downloaded_files is not None and len(result.downloaded_files) > 1
-                self.log_result(
-                    "Multiple Downloads",
-                    success,
-                    f"Downloaded {len(result.downloaded_files or [])} files" if success else "Not enough files downloaded"
-                )
-        except Exception as e:
-            self.log_result("Multiple Downloads", False, str(e))
-
-    async def test_different_browsers(self):
-        """Test downloads across different browser types"""
-        browsers = ["chromium", "firefox", "webkit"]
-        
-        for browser_type in browsers:
-            try:
-                async with AsyncWebCrawler(
-                    accept_downloads=True,
-                    downloads_path=self.download_dir,
-                    browser_type=browser_type,
-                    verbose=True
-                ) as crawler:
-                    result = await crawler.arun(
-                        url="https://www.python.org/downloads/",
-                        js_code="""
-                        const downloadLink = document.querySelector('a[href$=".exe"]');
-                        if (downloadLink) downloadLink.click();
-                        """
-                    )
-                    
-                    success = result.downloaded_files is not None and len(result.downloaded_files) > 0
-                    self.log_result(
-                        f"{browser_type.title()} Download",
-                        success,
-                        f"Downloaded {len(result.downloaded_files or [])} files" if success else "No files downloaded"
-                    )
-            except Exception as e:
-                self.log_result(f"{browser_type.title()} Download", False, str(e))
-
-    async def test_edge_cases(self):
-        """Test various edge cases"""
-        
-        # Test 1: Downloads without specifying download path
-        try:
-            async with AsyncWebCrawler(
-                accept_downloads=True,
-                verbose=True
-            ) as crawler:
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="document.querySelector('a[href$=\".exe\"]').click()"
-                )
-                self.log_result(
-                    "Default Download Path",
-                    True,
-                    f"Downloaded to default path: {result.downloaded_files[0] if result.downloaded_files else 'None'}"
-                )
-        except Exception as e:
-            self.log_result("Default Download Path", False, str(e))
-
-        # Test 2: Downloads with invalid path
-        try:
-            async with AsyncWebCrawler(
-                accept_downloads=True,
-                downloads_path="/invalid/path/that/doesnt/exist",
-                verbose=True
-            ) as crawler:
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="document.querySelector('a[href$=\".exe\"]').click()"
-                )
-                self.log_result("Invalid Download Path", False, "Should have raised an error")
-        except Exception as e:
-            self.log_result("Invalid Download Path", True, "Correctly handled invalid path")
-
-        # Test 3: Download with accept_downloads=False
-        try:
-            async with AsyncWebCrawler(
-                accept_downloads=False,
-                verbose=True
-            ) as crawler:
-                result = await crawler.arun(
-                    url="https://www.python.org/downloads/",
-                    js_code="document.querySelector('a[href$=\".exe\"]').click()"
-                )
-                success = result.downloaded_files is None
-                self.log_result(
-                    "Disabled Downloads",
-                    success,
-                    "Correctly ignored downloads" if success else "Unexpectedly downloaded files"
-                )
-        except Exception as e:
-            self.log_result("Disabled Downloads", False, str(e))
-
-    async def run_all_tests(self):
-        """Run all test cases"""
-        print("\n🧪 Running Download Tests...\n")
-        
-        test_methods = [
-            self.test_basic_download,
-            self.test_persistent_context_download,
-            self.test_multiple_downloads,
-            self.test_different_browsers,
-            self.test_edge_cases
-        ]
-        
-        for test in test_methods:
-            print(f"\n📝 Running {test.__doc__}...")
-            await test()
-            await asyncio.sleep(2)  # Brief pause between tests
-            
-        print("\n📊 Test Results Summary:")
-        for result in self.results:
-            print(result)
-            
-        successes = len([r for r in self.results if '✅' in r])
-        total = len(self.results)
-        print(f"\nTotal: {successes}/{total} tests passed")
-        
-        self.cleanup()
-
-async def main():
-    tester = TestDownloads()
-    await tester.run_all_tests()
-
-if __name__ == "__main__":
-    asyncio.run(main())

tests/async/test_content_filter.py

@@ -1,175 +0,0 @@
-import os, sys
-import pytest
-from bs4 import BeautifulSoup
-from typing import List
-
-# Add the parent directory to the Python path
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-
-from crawl4ai.content_filter_strategy import BM25ContentFilter
-
-@pytest.fixture
-def basic_html():
-    return """
-    <html>
-        <head>
-            <title>Test Article</title>
-            <meta name="description" content="Test description">
-            <meta name="keywords" content="test, keywords">
-        </head>
-        <body>
-            <h1>Main Heading</h1>
-            <article>
-                <p>This is a long paragraph with more than fifty words. It continues with more text to ensure we meet the minimum word count threshold. We need to make sure this paragraph is substantial enough to be considered for extraction according to our filtering rules. This should be enough words now.</p>
-                <div class="navigation">Skip this nav content</div>
-            </article>
-        </body>
-    </html>
-    """
-
-@pytest.fixture
-def wiki_html():
-    return """
-    <html>
-        <head>
-            <title>Wikipedia Article</title>
-        </head>
-        <body>
-            <h1>Article Title</h1>
-            <h2>Section 1</h2>
-            <p>Short but important section header description.</p>
-            <div class="content">
-                <p>Long paragraph with sufficient words to meet the minimum threshold. This paragraph continues with more text to ensure we have enough content for proper testing. We need to make sure this has enough words to pass our filters and be considered valid content for extraction purposes.</p>
-            </div>
-        </body>
-    </html>
-    """
-
-@pytest.fixture
-def no_meta_html():
-    return """
-    <html>
-        <body>
-            <h1>Simple Page</h1>
-            <p>First paragraph that should be used as fallback for query when no meta tags exist. This text needs to be long enough to serve as a meaningful fallback for our content extraction process.</p>
-        </body>
-    </html>
-    """
-
-class TestBM25ContentFilter:
-    def test_basic_extraction(self, basic_html):
-        """Test basic content extraction functionality"""
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(basic_html)
-        
-        assert contents, "Should extract content"
-        assert len(contents) >= 1, "Should extract at least one content block"
-        assert "long paragraph" in ' '.join(contents).lower()
-        assert "navigation" not in ' '.join(contents).lower()
-
-    def test_user_query_override(self, basic_html):
-        """Test that user query overrides metadata extraction"""
-        user_query = "specific test query"
-        filter = BM25ContentFilter(user_query=user_query)
-        
-        # Access internal state to verify query usage
-        soup = BeautifulSoup(basic_html, 'lxml')
-        extracted_query = filter.extract_page_query(soup.find('head'))
-        
-        assert extracted_query == user_query
-        assert "Test description" not in extracted_query
-
-    def test_header_extraction(self, wiki_html):
-        """Test that headers are properly extracted despite length"""
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(wiki_html)
-        
-        combined_content = ' '.join(contents).lower()
-        assert "section 1" in combined_content, "Should include section header"
-        assert "article title" in combined_content, "Should include main title"
-
-    def test_no_metadata_fallback(self, no_meta_html):
-        """Test fallback behavior when no metadata is present"""
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(no_meta_html)
-        
-        assert contents, "Should extract content even without metadata"
-        assert "First paragraph" in ' '.join(contents), "Should use first paragraph content"
-
-    def test_empty_input(self):
-        """Test handling of empty input"""
-        filter = BM25ContentFilter()
-        assert filter.filter_content("") == []
-        assert filter.filter_content(None) == []
-
-    def test_malformed_html(self):
-        """Test handling of malformed HTML"""
-        malformed_html = "<p>Unclosed paragraph<div>Nested content</p></div>"
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(malformed_html)
-        
-        assert isinstance(contents, list), "Should return list even with malformed HTML"
-        
-    def test_threshold_behavior(self, basic_html):
-        """Test different BM25 threshold values"""
-        strict_filter = BM25ContentFilter(bm25_threshold=2.0)
-        lenient_filter = BM25ContentFilter(bm25_threshold=0.5)
-        
-        strict_contents = strict_filter.filter_content(basic_html)
-        lenient_contents = lenient_filter.filter_content(basic_html)
-        
-        assert len(strict_contents) <= len(lenient_contents), \
-            "Strict threshold should extract fewer elements"
-
-    def test_html_cleaning(self, basic_html):
-        """Test HTML cleaning functionality"""
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(basic_html)
-        
-        cleaned_content = ' '.join(contents)
-        assert 'class=' not in cleaned_content, "Should remove class attributes"
-        assert 'style=' not in cleaned_content, "Should remove style attributes"
-        assert '<script' not in cleaned_content, "Should remove script tags"
-
-    def test_large_content(self):
-        """Test handling of large content blocks"""
-        large_html = f"""
-        <html><body>
-            <article>{'<p>Test content. ' * 1000}</article>
-        </body></html>
-        """
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(large_html)
-        assert contents, "Should handle large content blocks"
-
-    @pytest.mark.parametrize("unwanted_tag", [
-        'script', 'style', 'nav', 'footer', 'header'
-    ])
-    def test_excluded_tags(self, unwanted_tag):
-        """Test that specific tags are properly excluded"""
-        html = f"""
-        <html><body>
-            <{unwanted_tag}>Should not appear</{unwanted_tag}>
-            <p>Should appear</p>
-        </body></html>
-        """
-        filter = BM25ContentFilter()
-        contents = filter.filter_content(html)
-        
-        combined_content = ' '.join(contents).lower()
-        assert "should not appear" not in combined_content
-        
-    def test_performance(self, basic_html):
-        """Test performance with timer"""
-        filter = BM25ContentFilter()
-        
-        import time
-        start = time.perf_counter()
-        filter.filter_content(basic_html)
-        duration = time.perf_counter() - start
-        
-        assert duration < 1.0, f"Processing took too long: {duration:.2f} seconds"
-
-if __name__ == "__main__":
-    pytest.main([__file__])

tests/async/test_content_scraper_strategy.py

@@ -1,162 +0,0 @@
-import asyncio
-from bs4 import BeautifulSoup
-from typing import Dict, Any
-import os
-import sys
-import time
-import csv
-from tabulate import tabulate
-from dataclasses import dataclass
-from typing import List, Dict
-
-parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.append(parent_dir)
-__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-
-from crawl4ai.content_scraping_strategy import WebScrapingStrategy
-from crawl4ai.content_scraping_strategy import WebScrapingStrategy as WebScrapingStrategyCurrent
-# from crawl4ai.content_scrapping_strategy_current import WebScrapingStrategy as WebScrapingStrategyCurrent
-
-@dataclass
-class TestResult:
-    name: str
-    success: bool
-    images: int
-    internal_links: int
-    external_links: int
-    markdown_length: int
-    execution_time: float
-
-class StrategyTester:
-    def __init__(self):
-        self.new_scraper = WebScrapingStrategy()
-        self.current_scraper = WebScrapingStrategyCurrent()
-        with open(__location__ + '/sample_wikipedia.html', 'r', encoding='utf-8') as f:
-            self.WIKI_HTML = f.read()
-        self.results = {'new': [], 'current': []}
-        
-    def run_test(self, name: str, **kwargs) -> tuple[TestResult, TestResult]:
-        results = []
-        for scraper in [self.new_scraper, self.current_scraper]:
-            start_time = time.time()
-            result = scraper._get_content_of_website_optimized(
-                url="https://en.wikipedia.org/wiki/Test",
-                html=self.WIKI_HTML,
-                **kwargs
-            )
-            execution_time = time.time() - start_time
-            
-            test_result = TestResult(
-                name=name,
-                success=result['success'],
-                images=len(result['media']['images']),
-                internal_links=len(result['links']['internal']),
-                external_links=len(result['links']['external']),
-                markdown_length=len(result['markdown']),
-                execution_time=execution_time
-            )
-            results.append(test_result)
-        
-        return results[0], results[1]  # new, current
-
-    def run_all_tests(self):
-        test_cases = [
-            ("Basic Extraction", {}),
-            ("Exclude Tags", {'excluded_tags': ['table', 'div.infobox', 'div.navbox']}),
-            ("Word Threshold", {'word_count_threshold': 50}),
-            ("CSS Selector", {'css_selector': 'div.mw-parser-output > p'}),
-            ("Link Exclusions", {
-                'exclude_external_links': True,
-                'exclude_social_media_links': True,
-                'exclude_domains': ['facebook.com', 'twitter.com']
-            }),
-            ("Media Handling", {
-                'exclude_external_images': True,
-                'image_description_min_word_threshold': 20
-            }),
-            ("Text Only", {
-                'only_text': True,
-                'remove_forms': True
-            }),
-            ("HTML Cleaning", {
-                'clean_html': True,
-                'keep_data_attributes': True
-            }),
-            ("HTML2Text Options", {
-                'html2text': {
-                    'skip_internal_links': True,
-                    'single_line_break': True,
-                    'mark_code': True,
-                    'preserve_tags': ['pre', 'code']
-                }
-            })
-        ]
-
-        all_results = []
-        for name, kwargs in test_cases:
-            try:
-                new_result, current_result = self.run_test(name, **kwargs)
-                all_results.append((name, new_result, current_result))
-            except Exception as e:
-                print(f"Error in {name}: {str(e)}")
-                
-        self.save_results_to_csv(all_results)
-        self.print_comparison_table(all_results)
-
-    def save_results_to_csv(self, all_results: List[tuple]):
-        csv_file = os.path.join(__location__, 'strategy_comparison_results.csv')
-        with open(csv_file, 'w', newline='') as f:
-            writer = csv.writer(f)
-            writer.writerow(['Test Name', 'Strategy', 'Success', 'Images', 'Internal Links', 
-                           'External Links', 'Markdown Length', 'Execution Time'])
-            
-            for name, new_result, current_result in all_results:
-                writer.writerow([name, 'New', new_result.success, new_result.images,
-                               new_result.internal_links, new_result.external_links,
-                               new_result.markdown_length, f"{new_result.execution_time:.3f}"])
-                writer.writerow([name, 'Current', current_result.success, current_result.images,
-                               current_result.internal_links, current_result.external_links,
-                               current_result.markdown_length, f"{current_result.execution_time:.3f}"])
-
-    def print_comparison_table(self, all_results: List[tuple]):
-        table_data = []
-        headers = ['Test Name', 'Strategy', 'Success', 'Images', 'Internal Links', 
-                  'External Links', 'Markdown Length', 'Time (s)']
-
-        for name, new_result, current_result in all_results:
-            # Check for differences
-            differences = []
-            if new_result.images != current_result.images: differences.append('images')
-            if new_result.internal_links != current_result.internal_links: differences.append('internal_links')
-            if new_result.external_links != current_result.external_links: differences.append('external_links')
-            if new_result.markdown_length != current_result.markdown_length: differences.append('markdown')
-            
-            # Add row for new strategy
-            new_row = [
-                name, 'New', new_result.success, new_result.images,
-                new_result.internal_links, new_result.external_links,
-                new_result.markdown_length, f"{new_result.execution_time:.3f}"
-            ]
-            table_data.append(new_row)
-            
-            # Add row for current strategy
-            current_row = [
-                '', 'Current', current_result.success, current_result.images,
-                current_result.internal_links, current_result.external_links,
-                current_result.markdown_length, f"{current_result.execution_time:.3f}"
-            ]
-            table_data.append(current_row)
-            
-            # Add difference summary if any
-            if differences:
-                table_data.append(['', '⚠️ Differences', ', '.join(differences), '', '', '', '', ''])
-            
-            # Add empty row for better readability
-            table_data.append([''] * len(headers))
-
-        print("\nStrategy Comparison Results:")
-        print(tabulate(table_data, headers=headers, tablefmt='grid'))
-
-if __name__ == "__main__":
-    tester = StrategyTester()
-    tester.run_all_tests()

tests/async/test_markdown_genertor.py

@@ -1,165 +0,0 @@
-# ## Issue #236
-# - **Last Updated:** 2024-11-11 01:42:14
-# - **Title:** [user data crawling opens two windows, unable to control correct user browser](https://github.com/unclecode/crawl4ai/issues/236)
-# - **State:** open
-
-import os, sys, time
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-__location__ = os.path.realpath(    os.path.join(os.getcwd(), os.path.dirname(__file__)))
-import asyncio
-import os
-import time
-from typing import Dict, Any
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
-
-# Get current directory
-__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-
-def print_test_result(name: str, result: Dict[str, Any], execution_time: float):
-    """Helper function to print test results."""
-    print(f"\n{'='*20} {name} {'='*20}")
-    print(f"Execution time: {execution_time:.4f} seconds")
-    
-    
-    # Save markdown to files
-    for key, content in result.items():
-        if isinstance(content, str):
-            with open(__location__ + f"/output/{name.lower()}_{key}.md", "w") as f:
-                f.write(content)
-    
-    # # Print first few lines of each markdown version
-    # for key, content in result.items():
-    #     if isinstance(content, str):
-    #         preview = '\n'.join(content.split('\n')[:3])
-    #         print(f"\n{key} (first 3 lines):")
-    #         print(preview)
-    #         print(f"Total length: {len(content)} characters")
-
-def test_basic_markdown_conversion():
-    """Test basic markdown conversion with links."""
-    with open(__location__ + "/data/wikipedia.html", "r") as f:
-        cleaned_html = f.read()
-
-    generator = DefaultMarkdownGenerator()
-    
-    start_time = time.perf_counter()
-    result = generator.generate_markdown(
-        cleaned_html=cleaned_html,
-        base_url="https://en.wikipedia.org"
-    )
-    execution_time = time.perf_counter() - start_time
-    
-    print_test_result("Basic Markdown Conversion", {
-        'raw': result.raw_markdown,
-        'with_citations': result.markdown_with_citations,
-        'references': result.references_markdown
-    }, execution_time)
-    
-    # Basic assertions
-    assert result.raw_markdown, "Raw markdown should not be empty"
-    assert result.markdown_with_citations, "Markdown with citations should not be empty"
-    assert result.references_markdown, "References should not be empty"
-    assert "⟨" in result.markdown_with_citations, "Citations should use ⟨⟩ brackets"
-    assert "## References" in result.references_markdown, "Should contain references section"
-
-def test_relative_links():
-    """Test handling of relative links with base URL."""
-    markdown = """
-    Here's a [relative link](/wiki/Apple) and an [absolute link](https://example.com).
-    Also an [image](/images/test.png) and another [page](/wiki/Banana).
-    """
-    
-    generator = DefaultMarkdownGenerator()
-    result = generator.generate_markdown(
-        cleaned_html=markdown,
-        base_url="https://en.wikipedia.org"
-    )
-    
-    assert "https://en.wikipedia.org/wiki/Apple" in result.references_markdown
-    assert "https://example.com" in result.references_markdown
-    assert "https://en.wikipedia.org/images/test.png" in result.references_markdown
-
-def test_duplicate_links():
-    """Test handling of duplicate links."""
-    markdown = """
-    Here's a [link](/test) and another [link](/test) and a [different link](/other).
-    """
-    
-    generator = DefaultMarkdownGenerator()
-    result = generator.generate_markdown(
-        cleaned_html=markdown,
-        base_url="https://example.com"
-    )
-    
-    # Count citations in markdown
-    citations = result.markdown_with_citations.count("⟨1⟩")
-    assert citations == 2, "Same link should use same citation number"
-
-def test_link_descriptions():
-    """Test handling of link titles and descriptions."""
-    markdown = """
-    Here's a [link with title](/test "Test Title") and a [link with description](/other) to test.
-    """
-    
-    generator = DefaultMarkdownGenerator()
-    result = generator.generate_markdown(
-        cleaned_html=markdown,
-        base_url="https://example.com"
-    )
-    
-    assert "Test Title" in result.references_markdown, "Link title should be in references"
-    assert "link with description" in result.references_markdown, "Link text should be in references"
-
-def test_performance_large_document():
-    """Test performance with large document."""
-    with open(__location__ + "/data/wikipedia.md", "r") as f:
-        markdown = f.read()
-    
-    # Test with multiple iterations
-    iterations = 5
-    times = []
-    
-    generator = DefaultMarkdownGenerator()
-    
-    for i in range(iterations):
-        start_time = time.perf_counter()
-        result = generator.generate_markdown(
-            cleaned_html=markdown,
-            base_url="https://en.wikipedia.org"
-        )
-        end_time = time.perf_counter()
-        times.append(end_time - start_time)
-    
-    avg_time = sum(times) / len(times)
-    print(f"\n{'='*20} Performance Test {'='*20}")
-    print(f"Average execution time over {iterations} iterations: {avg_time:.4f} seconds")
-    print(f"Min time: {min(times):.4f} seconds")
-    print(f"Max time: {max(times):.4f} seconds")
-
-def test_image_links():
-    """Test handling of image links."""
-    markdown = """
-    Here's an ![image](/image.png "Image Title") and another ![image](/other.jpg).
-    And a regular [link](/page).
-    """
-    
-    generator = DefaultMarkdownGenerator()
-    result = generator.generate_markdown(
-        cleaned_html=markdown,
-        base_url="https://example.com"
-    )
-    
-    assert "![" in result.markdown_with_citations, "Image markdown syntax should be preserved"
-    assert "Image Title" in result.references_markdown, "Image title should be in references"
-
-if __name__ == "__main__":
-    print("Running markdown generation strategy tests...")
-    
-    test_basic_markdown_conversion()
-    test_relative_links()
-    test_duplicate_links()
-    test_link_descriptions()
-    test_performance_large_document()
-    test_image_links()
-    

tests/docker_example.py

@@ -1,332 +0,0 @@
-import requests
-import json
-import time
-import sys
-import base64
-import os
-from typing import Dict, Any
-
-class Crawl4AiTester:
-    def __init__(self, base_url: str = "http://localhost:11235", api_token: str = None):
-        self.base_url = base_url
-        self.api_token = api_token or os.getenv('CRAWL4AI_API_TOKEN')  # Check environment variable as fallback
-        self.headers = {'Authorization': f'Bearer {self.api_token}'} if self.api_token else {}
-        
-    def submit_and_wait(self, request_data: Dict[str, Any], timeout: int = 300) -> Dict[str, Any]:
-        # Submit crawl job
-        response = requests.post(f"{self.base_url}/crawl", json=request_data, headers=self.headers)
-        if response.status_code == 403:
-            raise Exception("API token is invalid or missing")
-        task_id = response.json()["task_id"]
-        print(f"Task ID: {task_id}")
-        
-        # Poll for result
-        start_time = time.time()
-        while True:
-            if time.time() - start_time > timeout:
-                raise TimeoutError(f"Task {task_id} did not complete within {timeout} seconds")
-                
-            result = requests.get(f"{self.base_url}/task/{task_id}", headers=self.headers)
-            status = result.json()
-            
-            if status["status"] == "failed":
-                print("Task failed:", status.get("error"))
-                raise Exception(f"Task failed: {status.get('error')}")
-                
-            if status["status"] == "completed":
-                return status
-                
-            time.sleep(2)
-            
-    def submit_sync(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
-        response = requests.post(f"{self.base_url}/crawl_sync", json=request_data, headers=self.headers, timeout=60)
-        if response.status_code == 408:
-            raise TimeoutError("Task did not complete within server timeout")
-        response.raise_for_status()
-        return response.json()
-
-def test_docker_deployment(version="basic"):
-    tester = Crawl4AiTester(
-        # base_url="http://localhost:11235" ,
-        base_url="https://crawl4ai-sby74.ondigitalocean.app",
-        api_token="test"
-    )
-    print(f"Testing Crawl4AI Docker {version} version")
-    
-    # Health check with timeout and retry
-    max_retries = 5
-    for i in range(max_retries):
-        try:
-            health = requests.get(f"{tester.base_url}/health", timeout=10)
-            print("Health check:", health.json())
-            break
-        except requests.exceptions.RequestException as e:
-            if i == max_retries - 1:
-                print(f"Failed to connect after {max_retries} attempts")
-                sys.exit(1)
-            print(f"Waiting for service to start (attempt {i+1}/{max_retries})...")
-            time.sleep(5)
-    
-    # Test cases based on version
-    test_basic_crawl(tester)
-    test_basic_crawl(tester)
-    test_basic_crawl_sync(tester)
-    
-    # if version in ["full", "transformer"]:
-    #     test_cosine_extraction(tester)
-
-    # test_js_execution(tester)
-    # test_css_selector(tester)
-    # test_structured_extraction(tester)
-    # test_llm_extraction(tester)
-    # test_llm_with_ollama(tester)
-    # test_screenshot(tester)
-    
-
-def test_basic_crawl(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10, 
-        "session_id": "test"
-    }
-    
-    result = tester.submit_and_wait(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result["result"]["success"]
-    assert len(result["result"]["markdown"]) > 0
-
-def test_basic_crawl_sync(tester: Crawl4AiTester):
-    print("\n=== Testing Basic Crawl (Sync) ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 10,
-        "session_id": "test"
-    }
-    
-    result = tester.submit_sync(request)
-    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
-    assert result['status'] == 'completed'
-    assert result['result']['success']
-    assert len(result['result']['markdown']) > 0
-    
-def test_js_execution(tester: Crawl4AiTester):
-    print("\n=== Testing JS Execution ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "js_code": [
-            "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
-        ],
-        "wait_for": "article.tease-card:nth-child(10)",
-        "crawler_params": {
-            "headless": True
-        }
-    }
-    
-    result = tester.submit_and_wait(request)
-    print(f"JS execution result length: {len(result['result']['markdown'])}")
-    assert result["result"]["success"]
-
-def test_css_selector(tester: Crawl4AiTester):
-    print("\n=== Testing CSS Selector ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 7,
-        "css_selector": ".wide-tease-item__description",
-        "crawler_params": {
-            "headless": True
-        },
-        "extra": {"word_count_threshold": 10}
-        
-    }
-    
-    result = tester.submit_and_wait(request)
-    print(f"CSS selector result length: {len(result['result']['markdown'])}")
-    assert result["result"]["success"]
-
-def test_structured_extraction(tester: Crawl4AiTester):
-    print("\n=== Testing Structured Extraction ===")
-    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",
-            }
-        ],
-    }
-    
-    request = {
-        "urls": "https://www.coinbase.com/explore",
-        "priority": 9,
-        "extraction_config": {
-            "type": "json_css",
-            "params": {
-                "schema": schema
-            }
-        }
-    }
-    
-    result = tester.submit_and_wait(request)
-    extracted = json.loads(result["result"]["extracted_content"])
-    print(f"Extracted {len(extracted)} items")
-    print("Sample item:", json.dumps(extracted[0], indent=2))
-    assert result["result"]["success"]
-    assert len(extracted) > 0
-
-def test_llm_extraction(tester: Crawl4AiTester):
-    print("\n=== Testing LLM Extraction ===")
-    schema = {
-        "type": "object",
-        "properties": {
-            "model_name": {
-                "type": "string",
-                "description": "Name of the OpenAI model."
-            },
-            "input_fee": {
-                "type": "string",
-                "description": "Fee for input token for the OpenAI model."
-            },
-            "output_fee": {
-                "type": "string",
-                "description": "Fee for output token for the OpenAI model."
-            }
-        },
-        "required": ["model_name", "input_fee", "output_fee"]
-    }
-    
-    request = {
-        "urls": "https://openai.com/api/pricing",
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
-            "params": {
-                "provider": "openai/gpt-4o-mini",
-                "api_token": os.getenv("OPENAI_API_KEY"),
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens."""
-            }
-        },
-        "crawler_params": {"word_count_threshold": 1}
-    }
-    
-    try:
-        result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
-        print(f"Extracted {len(extracted)} model pricing entries")
-        print("Sample entry:", json.dumps(extracted[0], indent=2))
-        assert result["result"]["success"]
-    except Exception as e:
-        print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
-
-def test_llm_with_ollama(tester: Crawl4AiTester):
-    print("\n=== Testing LLM with Ollama ===")
-    schema = {
-        "type": "object",
-        "properties": {
-            "article_title": {
-                "type": "string",
-                "description": "The main title of the news article"
-            },
-            "summary": {
-                "type": "string",
-                "description": "A brief summary of the article content"
-            },
-            "main_topics": {
-                "type": "array",
-                "items": {"type": "string"},
-                "description": "Main topics or themes discussed in the article"
-            }
-        }
-    }
-    
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "extraction_config": {
-            "type": "llm",
-            "params": {
-                "provider": "ollama/llama2",
-                "schema": schema,
-                "extraction_type": "schema",
-                "instruction": "Extract the main article information including title, summary, and main topics."
-            }
-        },
-        "extra": {"word_count_threshold": 1},
-        "crawler_params": {"verbose": True}
-    }
-    
-    try:
-        result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
-        print("Extracted content:", json.dumps(extracted, indent=2))
-        assert result["result"]["success"]
-    except Exception as e:
-        print(f"Ollama extraction test failed: {str(e)}")
-
-def test_cosine_extraction(tester: Crawl4AiTester):
-    print("\n=== Testing Cosine Extraction ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 8,
-        "extraction_config": {
-            "type": "cosine",
-            "params": {
-                "semantic_filter": "business finance economy",
-                "word_count_threshold": 10,
-                "max_dist": 0.2,
-                "top_k": 3
-            }
-        }
-    }
-    
-    try:
-        result = tester.submit_and_wait(request)
-        extracted = json.loads(result["result"]["extracted_content"])
-        print(f"Extracted {len(extracted)} text clusters")
-        print("First cluster tags:", extracted[0]["tags"])
-        assert result["result"]["success"]
-    except Exception as e:
-        print(f"Cosine extraction test failed: {str(e)}")
-
-def test_screenshot(tester: Crawl4AiTester):
-    print("\n=== Testing Screenshot ===")
-    request = {
-        "urls": "https://www.nbcnews.com/business",
-        "priority": 5,
-        "screenshot": True,
-        "crawler_params": {
-            "headless": True
-        }
-    }
-    
-    result = tester.submit_and_wait(request)
-    print("Screenshot captured:", bool(result["result"]["screenshot"]))
-    
-    if result["result"]["screenshot"]:
-        # Save screenshot
-        screenshot_data = base64.b64decode(result["result"]["screenshot"])
-        with open("test_screenshot.jpg", "wb") as f:
-            f.write(screenshot_data)
-        print("Screenshot saved as test_screenshot.jpg")
-    
-    assert result["result"]["success"]
-
-if __name__ == "__main__":
-    version = sys.argv[1] if len(sys.argv) > 1 else "basic"
-    # version = "full"
-    test_docker_deployment(version)