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Merge branch '0.3.74'

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UncleCode
2024-11-22 19:55:52 +08:00
parent b654c49e55 8dea3f470f
commit a5decaa7cf
62 changed files with 7867 additions and 765 deletions
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19
.do/app.yaml Normal file
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@@ -0,0 +1,19 @@
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: /

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.do/deploy.template.yaml Normal file
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@@ -0,0 +1,22 @@
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: /

3
.gitignore vendored
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@@ -199,6 +199,7 @@ test_env/
**/.DS_Store
todo.md
todo_executor.md
git_changes.py
git_changes.md
pypi_build.sh
@@ -212,4 +213,4 @@ git_issues.md
.gitboss/
todo_executor.md
protect-all-except-feature.sh
manage-collab.sh
manage-collab.sh

283
CHANGELOG.md
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@@ -1,6 +1,255 @@
# Changelog
# CHANGELOG
## [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.
## [v0.3.73] - 2024-11-05
@@ -70,7 +319,7 @@
- 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:
@@ -92,7 +341,7 @@ When upgrading to v0.3.73, be aware of the following changes:
- Follow recommended fixes for any identified problems
## [2024-11-04 - 13:21:42] Comprehensive Update of Crawl4AI Features and Dependencies
## [v0.3.73] - 2024-11-04
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
@@ -180,7 +429,7 @@ 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 WebScrappingStrategy
- Added support for parsing Base64 encoded images in WebScrapingStrategy
### Added
- Forked and integrated a customized version of the html2text library for more control over Markdown generation
@@ -203,7 +452,7 @@ 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 `WebScrappingStrategy` class has been updated to accommodate new external content exclusion options
- The `WebScrapingStrategy` class has been updated to accommodate new external content exclusion options
## [v0.3.71] - 2024-10-19
@@ -280,7 +529,7 @@ These updates aim to provide more flexibility in text processing, improve perfor
### Improvements
1. **Better Error Handling**:
- Enhanced error reporting in WebScrappingStrategy with detailed error messages and suggestions.
- Enhanced error reporting in WebScrapingStrategy with detailed error messages and suggestions.
- Added console message and error logging for better debugging.
2. **Image Processing Enhancements**:
@@ -338,43 +587,43 @@ 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 WebScrappingStrategy.
#### 2. Image Processing Optimization
- Enhanced image handling in WebScrapingStrategy.
- 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.

38
Dockerfile
View File

@@ -12,7 +12,7 @@ ARG ENABLE_GPU=false
# Platform-specific labels
LABEL maintainer="unclecode"
LABEL description="Crawl4AI - Advanced Web Crawler with AI capabilities"
LABEL description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & scraper"
LABEL version="1.0"
# Environment setup
@@ -62,11 +62,13 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
libatspi2.0-0 \
&& rm -rf /var/lib/apt/lists/*
# GPU support if enabled
RUN if [ "$ENABLE_GPU" = "true" ] ; then \
apt-get update && apt-get install -y --no-install-recommends \
nvidia-cuda-toolkit \
&& rm -rf /var/lib/apt/lists/* ; \
# GPU support if enabled and architecture is supported
RUN if [ "$ENABLE_GPU" = "true" ] && [ "$(dpkg --print-architecture)" != "arm64" ] ; then \
apt-get update && apt-get install -y --no-install-recommends \
nvidia-cuda-toolkit \
&& rm -rf /var/lib/apt/lists/* ; \
else \
echo "Skipping NVIDIA CUDA Toolkit installation (unsupported architecture or GPU disabled)"; \
fi
# Create and set working directory
@@ -96,26 +98,32 @@ RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
# Install the package
RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
pip install -e ".[all]" && \
pip install ".[all]" && \
python -m crawl4ai.model_loader ; \
elif [ "$INSTALL_TYPE" = "torch" ] ; then \
pip install -e ".[torch]" ; \
pip install ".[torch]" ; \
elif [ "$INSTALL_TYPE" = "transformer" ] ; then \
pip install -e ".[transformer]" && \
pip install ".[transformer]" && \
python -m crawl4ai.model_loader ; \
else \
pip install -e "." ; \
pip install "." ; \
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
EXPOSE 8000 11235 9222 8080
# Start the FastAPI server
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "11235"]

43
README.md
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@@ -11,21 +11,21 @@
Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
## 🌟 Meet the Crawl4AI Assistant: Your Copilot for Crawling
## New in 0.3.74 ✨
Use the [Crawl4AI GPT Assistant](https://tinyurl.com/crawl4ai-gpt) as your AI-powered copilot! With this assistant, you can:
- 🚀 **Blazing Fast Scraping**: Significantly improved scraping speed.
- 📥 **Download Manager**: Integrated file crawling, downloading, and tracking within `CrawlResult`.
- 📝 **Markdown Strategy**: Flexible system for custom markdown generation and formats.
- 🔗 **LLM-Friendly Citations**: Auto-converts links to numbered citations with reference lists.
- 🔎 **Markdown Filter**: BM25-based content extraction for cleaner, relevant markdown.
- 🖼️ **Image Extraction**: Supports `srcset`, `picture`, and responsive image formats.
- 🗂️ **Local/Raw HTML**: Crawl `file://` paths and raw HTML (`raw:`) directly.
- 🤖 **Browser Control**: Custom browser setups with stealth integration to bypass bots.
- ☁️ **API & Cache Boost**: CORS, static serving, and enhanced filesystem-based caching.
- 🐳 **API Gateway**: Run as an API service with secure token authentication.
- 🛠️ **Database Upgrades**: Optimized for larger content sets with faster caching.
- 🐛 **Bug Fixes**: Resolved browser context issues, memory leaks, and improved error handling.
- 🧑‍💻 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!
@@ -113,6 +113,20 @@ cd crawl4ai
pip install -e .
```
## One-Click Deployment 🚀
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
### 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.
@@ -127,6 +141,9 @@ 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 to allocate more shared memory for the container
docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic
```
#### Option 2: Build from Repository

10
crawl4ai/__init__.py
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@@ -1,13 +1,15 @@
# __init__.py
from .async_webcrawler import AsyncWebCrawler
from .async_webcrawler import AsyncWebCrawler, CacheMode
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():
@@ -26,5 +28,5 @@ 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.")

2
crawl4ai/_version.py → crawl4ai/__version__.py
View File

@@ -1,2 +1,2 @@
# crawl4ai/_version.py
__version__ = "0.3.73"
__version__ = "0.3.74"

429
crawl4ai/async_crawler_strategy.py
View File

@@ -14,6 +14,7 @@ from pydantic import BaseModel
import hashlib
import json
import uuid
from .models import AsyncCrawlResponse
from playwright_stealth import StealthConfig, stealth_async
@@ -34,13 +35,15 @@ stealth_config = StealthConfig(
class ManagedBrowser:
def __init__(self, browser_type: str = "chromium", user_data_dir: Optional[str] = None, headless: bool = False):
def __init__(self, browser_type: str = "chromium", user_data_dir: Optional[str] = None, headless: bool = False, logger = None):
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 = 9222
self.logger = logger
self.shutting_down = False
async def start(self) -> str:
"""
@@ -64,12 +67,50 @@ 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://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
@@ -118,30 +159,40 @@ 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()
await asyncio.sleep(1)
# Wait for process to end gracefully
for _ in range(10): # 10 attempts, 100ms each
if self.browser_process.poll() is not None:
break
await asyncio.sleep(0.1)
# Force kill if still running
if self.browser_process.poll() is None:
self.browser_process.kill()
await asyncio.sleep(0.1) # Brief wait for kill to take effect
except Exception as e:
print(f"Error terminating browser: {e}")
self.logger.error(
message="Error terminating browser: {error}",
tag="ERROR",
params={"error": str(e)}
)
if self.temp_dir and os.path.exists(self.temp_dir):
try:
shutil.rmtree(self.temp_dir)
except Exception as e:
print(f"Error removing temporary directory: {e}")
self.logger.error(
message="Error removing temporary directory: {error}",
tag="ERROR",
params={"error": str(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
@@ -165,7 +216,8 @@ class AsyncCrawlerStrategy(ABC):
pass
class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
def __init__(self, use_cached_html=False, js_code=None, **kwargs):
def __init__(self, use_cached_html=False, js_code=None, logger = None, **kwargs):
self.logger = logger
self.use_cached_html = use_cached_html
self.user_agent = kwargs.get(
"user_agent",
@@ -177,6 +229,7 @@ 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
@@ -186,6 +239,8 @@ 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 = {
@@ -197,6 +252,14 @@ 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()
@@ -214,7 +277,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
self.managed_browser = ManagedBrowser(
browser_type=self.browser_type,
user_data_dir=self.user_data_dir,
headless=self.headless
headless=self.headless,
logger=self.logger
)
cdp_url = await self.managed_browser.start()
self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
@@ -232,42 +296,90 @@ 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",
"--disable-blink-features=AutomationControlled",
"--no-first-run",
"--no-default-browser-check",
"--disable-infobars",
"--window-position=0,0",
"--ignore-certificate-errors",
"--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
# 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)
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
await self.execute_hook('on_browser_created', self.browser)
@@ -285,6 +397,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
self.browser = None
if self.managed_browser:
await asyncio.sleep(0.5)
await self.managed_browser.cleanup()
self.managed_browser = None
@@ -292,9 +405,10 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
await self.playwright.stop()
self.playwright = None
def __del__(self):
if self.browser or self.playwright:
asyncio.get_event_loop().run_until_complete(self.close())
# 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 set_hook(self, hook_type: str, hook: Callable):
if hook_type in self.hooks:
@@ -431,17 +545,99 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
}}
""")
else:
print(f"Warning: Could not access content frame for iframe {i}")
# 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}
)
except Exception as e:
print(f"Error processing iframe {i}: {str(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)}")
# 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")
@@ -461,24 +657,41 @@ 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=True,
java_script_enabled=True
accept_downloads=self.accept_downloads,
)
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:
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
@@ -512,7 +725,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
""")
page = await context.new_page()
# await stealth_async(page) #, stealth_config)
if kwargs.get("magic", False):
await stealth_async(page, stealth_config)
# Add console message and error logging
if kwargs.get("log_console", False):
@@ -520,8 +734,12 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
page.on("pageerror", lambda exc: print(f"Page Error: {exc}"))
try:
if self.verbose:
print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
# 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.use_cached_html:
cache_file_path = os.path.join(
@@ -544,8 +762,12 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
if not kwargs.get("js_only", False):
await self.execute_hook('before_goto', page)
response = await page.goto(
url, wait_until="domcontentloaded", timeout=kwargs.get("page_timeout", 60000)
url,
# wait_until=kwargs.get("wait_until", ["domcontentloaded", "networkidle"]),
wait_until=kwargs.get("wait_until", "domcontentloaded"),
timeout=kwargs.get("page_timeout", 60000)
)
# response = await page.goto("about:blank")
@@ -613,7 +835,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
for js in js_code:
await page.evaluate(js)
await page.wait_for_load_state('networkidle')
# await page.wait_for_timeout(100)
# Check for on execution event
await self.execute_hook('on_execution_started', page)
@@ -631,6 +854,9 @@ 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 = """
@@ -720,9 +946,9 @@ 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()
@@ -747,16 +973,49 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
response_headers=response_headers,
status_code=status_code,
screenshot=screenshot_data,
get_delayed_content=get_delayed_content
get_delayed_content=get_delayed_content,
downloaded_files=self._downloaded_files if self._downloaded_files else None
)
return response
except Error as e:
raise Error(f"[ERROR] 🚫 crawl(): Failed to crawl {url}: {str(e)}")
raise Error(f"async_crawler_strategy.py:_crawleb(): {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)
@@ -898,17 +1157,36 @@ 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:
if self.verbose:
print(f"Warning: Failed to remove overlay elements: {str(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)}")
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)}"
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')
@@ -921,4 +1199,41 @@ 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')

359
crawl4ai/async_database.py
View File

@@ -5,28 +5,89 @@ 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__)
DB_PATH = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
base_directory = 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(DB_PATH, "crawl4ai.db")
DB_PATH = os.path.join(base_directory, "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"""
await self.ainit_db()
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
async def cleanup(self):
"""Cleanup connections when shutting down"""
async with self.pool_lock:
@@ -37,29 +98,43 @@ class AsyncDatabaseManager:
@asynccontextmanager
async def get_connection(self):
"""Connection pool manager"""
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]
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 def execute_with_retry(self, operation, *args):
"""Execute database operations with retry logic"""
@@ -71,13 +146,21 @@ class AsyncDatabaseManager:
return result
except Exception as e:
if attempt == self.max_retries - 1:
logger.error(f"Operation failed after {self.max_retries} attempts: {e}")
self.logger.error(
message="Operation failed after {retries} attempts: {error}",
tag="ERROR",
force_verbose=True,
params={
"retries": self.max_retries,
"error": str(e)
}
)
raise
await asyncio.sleep(1 * (attempt + 1)) # Exponential backoff
async def ainit_db(self):
"""Initialize database schema"""
async def _init(db):
async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
await db.execute('''
CREATE TABLE IF NOT EXISTS crawled_data (
url TEXT PRIMARY KEY,
@@ -89,71 +172,168 @@ class AsyncDatabaseManager:
media TEXT DEFAULT "{}",
links TEXT DEFAULT "{}",
metadata TEXT DEFAULT "{}",
screenshot TEXT DEFAULT ""
screenshot TEXT DEFAULT "",
response_headers TEXT DEFAULT "{}",
downloaded_files TEXT DEFAULT "{}" -- New column added
)
''')
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 def _check_columns(db):
async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
cursor = await db.execute("PRAGMA table_info(crawled_data)")
columns = await cursor.fetchall()
return [column[1] for column in columns]
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()
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):
async def aalter_db_add_column(self, new_column: str, db):
"""Add new column to the database"""
async def _alter(db):
if new_column == 'response_headers':
await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT "{{}}"')
else:
await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
logger.info(f"Added column '{new_column}' to the database.")
self.logger.info(
message="Added column '{column}' to the database",
tag="INIT",
params={"column": new_column}
)
await self.execute_with_retry(_alter)
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 aget_cached_url(self, url: str) -> Optional[CrawlResult]:
"""Retrieve cached URL data as CrawlResult"""
async def _get(db):
async with db.execute(
'SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?',
(url,)
'SELECT * FROM crawled_data WHERE url = ?', (url,)
) as cursor:
return await cursor.fetchone()
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)
try:
return await self.execute_with_retry(_get)
except Exception as e:
logger.error(f"Error retrieving cached URL: {e}")
self.logger.error(
message="Error retrieving cached URL: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)}
)
return None
async def acache_url(self, url: str, html: str, cleaned_html: str, markdown: str, extracted_content: str, success: bool, media: str = "{}", links: str = "{}", metadata: str = "{}", screenshot: str = ""):
"""Cache URL data with retry logic"""
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 _cache(db):
await db.execute('''
INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
INSERT INTO crawled_data (
url, html, cleaned_html, markdown,
extracted_content, success, media, links, metadata,
screenshot, response_headers, downloaded_files
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(url) DO UPDATE SET
html = excluded.html,
cleaned_html = excluded.cleaned_html,
markdown = excluded.markdown,
extracted_content = excluded.extracted_content,
success = excluded.success,
media = excluded.media,
links = excluded.links,
metadata = excluded.metadata,
screenshot = excluded.screenshot
''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
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 [])
))
try:
await self.execute_with_retry(_cache)
except Exception as e:
logger.error(f"Error caching URL: {e}")
self.logger.error(
message="Error caching URL: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)}
)
async def aget_total_count(self) -> int:
"""Get total number of cached URLs"""
@@ -165,7 +345,12 @@ class AsyncDatabaseManager:
try:
return await self.execute_with_retry(_count)
except Exception as e:
logger.error(f"Error getting total count: {e}")
self.logger.error(
message="Error getting total count: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)}
)
return 0
async def aclear_db(self):
@@ -176,7 +361,12 @@ class AsyncDatabaseManager:
try:
await self.execute_with_retry(_clear)
except Exception as e:
logger.error(f"Error clearing database: {e}")
self.logger.error(
message="Error clearing database: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)}
)
async def aflush_db(self):
"""Drop the entire table"""
@@ -186,7 +376,46 @@ class AsyncDatabaseManager:
try:
await self.execute_with_retry(_flush)
except Exception as e:
logger.error(f"Error flushing database: {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
# Create a singleton instance
async_db_manager = AsyncDatabaseManager()
async_db_manager = AsyncDatabaseManager()

231
crawl4ai/async_logger.py Normal file
View File

@@ -0,0 +1,231 @@
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
}
)

497
crawl4ai/async_webcrawler.py
View File

@@ -1,36 +1,106 @@
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
from typing import Optional, List, Union
import json
import asyncio
from .models import CrawlResult
from .models import CrawlResult, MarkdownGenerationResult
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 .content_scrapping_strategy import WebScrappingStrategy
from .config import MIN_WORD_THRESHOLD, IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
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 .utils import (
sanitize_input_encode,
InvalidCSSSelectorError,
format_html
)
from ._version import __version__ as crawl4ai_version
from urllib.parse import urlparse
import random
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_by_pass_cache: bool = False,
always_bypass_cache: bool = False,
always_by_pass_cache: Optional[bool] = None, # Deprecated parameter
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
)
self.always_by_pass_cache = always_by_pass_cache
# self.crawl4ai_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
# 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.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)
@@ -46,21 +116,14 @@ class AsyncWebCrawler:
await self.crawler_strategy.__aexit__(exc_type, exc_val, exc_tb)
async def awarmup(self):
# 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,
)
"""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}")
self.ready = True
if self.verbose:
print("[LOG] 🌞 AsyncWebCrawler is ready to crawl")
# if self.verbose:
# print(f"{Fore.GREEN}{self.tag_format('READY')} {self.log_icons['READY']} AsyncWebCrawler initialized{Style.RESET_ALL}")
async def arun(
self,
@@ -68,14 +131,82 @@ 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):
@@ -86,61 +217,126 @@ class AsyncWebCrawler:
word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
async_response: AsyncCrawlResponse = None
cached = None
cached_result = None
screenshot_data = None
extracted_content = None
if not bypass_cache and not self.always_by_pass_cache:
cached = await async_db_manager.aget_cached_url(url)
if kwargs.get("warmup", True) and not self.ready:
return None
if cached:
html = sanitize_input_encode(cached[1])
extracted_content = sanitize_input_encode(cached[4])
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 screenshot:
screenshot_data = cached[9]
screenshot_data = cached_result.screenshot
if not screenshot_data:
cached = None
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"
)
if not cached or not html:
t1 = time.time()
# Fetch fresh content if needed
if not cached_result or not html:
t1 = time.perf_counter()
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.time()
if verbose:
print(
f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds"
)
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")
# Process the HTML content
crawl_result = await self.aprocess_html(
url,
html,
extracted_content,
word_count_threshold,
extraction_strategy,
chunking_strategy,
css_selector,
screenshot_data,
verbose,
bool(cached),
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),
async_response=async_response,
bypass_cache=bypass_cache,
is_web_url=cache_context.is_web_url,
is_local_file=cache_context.is_local_file,
is_raw_html=cache_context.is_raw_html,
**kwargs,
)
crawl_result.status_code = async_response.status_code if async_response else 200
crawl_result.response_headers = async_response.response_headers if async_response else {}
# 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.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"[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)
# 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=e.msg,
tag="ERROR"
)
return CrawlResult(
url=url,
html="",
markdown=f"[ERROR] 🚫 arun(): Failed to crawl {cache_context.display_url}, error: {e.msg}",
success=False,
error_message=e.msg
)
async def arun_many(
self,
@@ -148,6 +344,9 @@ 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,
@@ -155,22 +354,102 @@ class AsyncWebCrawler:
verbose=True,
**kwargs,
) -> List[CrawlResult]:
tasks = [
self.arun(
url,
word_count_threshold,
extraction_strategy,
chunking_strategy,
bypass_cache,
css_selector,
screenshot,
user_agent,
verbose,
**kwargs
)
for url in urls
]
return await asyncio.gather(*tasks)
"""
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]
async def aprocess_html(
self,
@@ -180,33 +459,30 @@ 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:
t1 = time.time()
scrapping_strategy = WebScrappingStrategy()
_url = url if not kwargs.get("is_raw_html", False) else "Raw HTML"
t1 = time.perf_counter()
scrapping_strategy = WebScrapingStrategy()
# result = await scrapping_strategy.ascrap(
result = scrapping_strategy.scrap(
url,
html,
word_count_threshold=word_count_threshold,
css_selector=css_selector,
only_text=kwargs.get("only_text", False),
only_text=kwargs.pop("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}")
@@ -215,6 +491,8 @@ 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", ""))
@@ -222,13 +500,21 @@ 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
@@ -238,32 +524,27 @@ 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"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds."
# 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
}
)
screenshot = None if not screenshot else screenshot
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=format_html(cleaned_html),
markdown_v2=markdown_v2,
markdown=markdown,
fit_markdown=fit_markdown,
fit_html= fit_html,
@@ -277,13 +558,15 @@ class AsyncWebCrawler:
)
async def aclear_cache(self):
# await async_db_manager.aclear_db()
"""Clear the cache database."""
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()

79
crawl4ai/cache_context.py Normal file
View File

@@ -0,0 +1,79 @@
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

6
crawl4ai/config.py
View File

@@ -51,3 +51,9 @@ 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

196
crawl4ai/content_cleaning_strategy.py
View File

@@ -1,196 +0,0 @@
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

328
crawl4ai/content_filter_strategy.py Normal file
View File

@@ -0,0 +1,328 @@
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
# 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
if soup.title:
query_parts.append(soup.title.string)
elif 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) -> 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))
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) -> 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')
body = soup.find('body')
query = self.extract_page_query(soup.find('head'), body)
candidates = self.extract_text_chunks(body)
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()]
# 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]

474
crawl4ai/content_scrapping_strategy.py → crawl4ai/content_scraping_strategy.py
View File

@@ -1,5 +1,6 @@
import re # Point 1: Pre-Compile Regular Expressions
from abc import ABC, abstractmethod
from typing import Dict, Any
from typing import Dict, Any, Optional
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor
import asyncio, requests, re, os
@@ -7,105 +8,54 @@ 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_cleaning_strategy import ContentCleaningStrategy
from .content_filter_strategy import RelevantContentFilter, BM25ContentFilter
from .markdown_generation_strategy import MarkdownGenerationStrategy, DefaultMarkdownGenerationStrategy
from .models import MarkdownGenerationResult
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
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
# 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*)")
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)
# 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 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
# 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:
super().handle_tag(tag, attrs, start)
print(f"Failed to retrieve file size for {img_url}")
return None
except InvalidSchema as e:
return None
finally:
return
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):
class ContentScrapingStrategy(ABC):
@abstractmethod
def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
pass
@@ -114,21 +64,127 @@ class ContentScrappingStrategy(ABC):
async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
pass
class WebScrappingStrategy(ContentScrappingStrategy):
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)
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', DefaultMarkdownGenerationStrategy())
if markdown_generator:
try:
markdown_result: MarkdownGenerationResult = markdown_generator.generate_markdown(
cleaned_html=cleaned_html,
base_url=url,
html2text_options=kwargs.get('html2text', {}),
content_filter=kwargs.get('content_filter', None)
)
return {
'markdown': markdown_result.raw_markdown,
'fit_markdown': markdown_result.fit_markdown or "Set flag 'fit_markdown' to True to get cleaned HTML content.",
'fit_html': markdown_result.fit_html or "Set flag 'fit_markdown' to True to get cleaned HTML content.",
'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
# 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, 'html.parser')
soup = BeautifulSoup(html, 'lxml')
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)
@@ -171,7 +227,26 @@ class WebScrappingStrategy(ContentScrappingStrategy):
return text_content
return None
def process_image(img, url, index, total_images):
def process_image_old(img, url, index, total_images):
def parse_srcset(srcset_str):
"""Parse srcset attribute into list of image URLs with their sizes."""
if not srcset_str:
return []
sources = []
# Split on http/https and filter empty strings
urls = [f"http{part}" for part in srcset_str.split("http") if part]
for url in urls:
# Remove trailing comma and whitespace, then split to get width
url = url.strip().rstrip(',')
parts = url.rsplit(' ', 1)
img_url = parts[0].strip()
width = parts[1].rstrip('w') if len(parts) > 1 else None
sources.append({'url': img_url, 'width': width})
return sources
#Check if an image has valid display and inside undesired html elements
def is_valid_image(img, parent, parent_classes):
style = img.get('style', '')
@@ -187,32 +262,6 @@ class WebScrappingStrategy(ContentScrappingStrategy):
#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')
@@ -246,14 +295,14 @@ class WebScrappingStrategy(ContentScrappingStrategy):
score+=1
return score
if not is_valid_image(img, img.parent, img.parent.get('class', [])):
return None
score = score_image_for_usefulness(img, url, index, total_images)
if score <= IMAGE_SCORE_THRESHOLD:
if score <= kwargs.get('image_score_threshold', IMAGE_SCORE_THRESHOLD):
return None
return {
base_result = {
'src': img.get('src', ''),
'data-src': img.get('data-src', ''),
'alt': img.get('alt', ''),
@@ -262,6 +311,109 @@ class WebScrappingStrategy(ContentScrappingStrategy):
'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 = []
# Base image info template
base_info = {
'alt': alt,
'desc': find_closest_parent_with_useful_text(img),
'score': score,
'type': 'image'
}
# 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:
@@ -294,7 +446,6 @@ class WebScrappingStrategy(ContentScrappingStrategy):
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'):
@@ -414,9 +565,12 @@ class WebScrappingStrategy(ContentScrappingStrategy):
try:
remove_unwanted_attributes(element, IMPORTANT_ATTRS, kwargs.get('keep_data_attributes', False))
except Exception as e:
print('Error removing unwanted attributes:', str(e))
# print('Error removing unwanted attributes:', str(e))
self._log('error',
message="Error removing unwanted attributes: {error}",
tag="SCRAPE",
params={"error": str(e)}
)
# Process children
for child in list(element.children):
if isinstance(child, NavigableString) and not isinstance(child, Comment):
@@ -437,30 +591,30 @@ class WebScrappingStrategy(ContentScrappingStrategy):
return keep_element
except Exception as e:
print('Error processing element:', str(e))
# print('Error processing element:', str(e))
self._log('error',
message="Error processing element: {error}",
tag="SCRAPE",
params={"error": str(e)}
)
return False
#process images by filtering and extracting contextual text from the page
# imgs = body.find_all('img')
# media['images'] = [
# result for result in
# (process_image(img, url, i, len(imgs)) for i, img in enumerate(imgs))
# if result is not None
# ]
process_element(body)
# Update the links dictionary with unique links
links['internal'] = list(internal_links_dict.values())
links['external'] = list(external_links_dict.values())
# # Process images using ThreadPoolExecutor
imgs = body.find_all('img')
with ThreadPoolExecutor() as executor:
image_results = list(executor.map(process_image, imgs, [url]*len(imgs), range(len(imgs)), [len(imgs)]*len(imgs)))
media['images'] = [result for result in image_results if result is not None]
# 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
]
def flatten_nested_elements(node):
if isinstance(node, NavigableString):
@@ -478,8 +632,9 @@ class WebScrappingStrategy(ContentScrappingStrategy):
# Replace base64 data with empty string
img['src'] = base64_pattern.sub('', src)
str_body = ""
try:
str(body)
str_body = body.encode_contents().decode('utf-8')
except Exception as e:
# Reset body to the original HTML
success = False
@@ -504,35 +659,26 @@ class WebScrappingStrategy(ContentScrappingStrategy):
# 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(' ', ' ')
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)
markdown_content = self._generate_markdown_content(
cleaned_html=cleaned_html,
html=html,
url=url,
success=success,
**kwargs
)
return {
'markdown': markdown,
'fit_markdown': fit_markdown,
'fit_html': fit_html,
**markdown_content,
'cleaned_html': cleaned_html,
'success': success,
'media': media,

116
crawl4ai/markdown_generation_strategy.py Normal file
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@@ -0,0 +1,116 @@
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."""
@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 DefaultMarkdownGenerationStrategy(MarkdownGenerationStrategy):
"""Default implementation of markdown generation strategy."""
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
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] = None
if 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)

152
crawl4ai/migrations.py Normal file
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@@ -0,0 +1,152 @@
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()

30
crawl4ai/models.py
View File

@@ -1,10 +1,19 @@
from pydantic import BaseModel, HttpUrl
from typing import List, Dict, Optional
from typing import List, Dict, Optional, Callable, Awaitable, Union
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
@@ -12,8 +21,10 @@ 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[str] = None
markdown: Optional[Union[str, MarkdownGenerationResult]] = None
markdown_v2: Optional[MarkdownGenerationResult] = None
fit_markdown: Optional[str] = None
fit_html: Optional[str] = None
extracted_content: Optional[str] = None
@@ -21,4 +32,17 @@ class CrawlResult(BaseModel):
error_message: Optional[str] = None
session_id: Optional[str] = None
response_headers: Optional[dict] = None
status_code: Optional[int] = 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

34
crawl4ai/tools.py Normal file
View File

@@ -0,0 +1,34 @@
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

220
crawl4ai/utils.py
View File

@@ -14,6 +14,97 @@ 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 .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
@@ -736,46 +827,54 @@ 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:
if not html and not soup:
return {}
if not soup:
soup = BeautifulSoup(html, 'lxml')
head = soup.head
if not head:
return metadata
# Parse HTML content with BeautifulSoup
if not soup:
soup = BeautifulSoup(html, 'html.parser')
# Title
title_tag = soup.find('title')
metadata['title'] = title_tag.string if title_tag else None
title_tag = head.find('title')
metadata['title'] = title_tag.string.strip() if title_tag and title_tag.string else None
# Meta description
description_tag = soup.find('meta', attrs={'name': 'description'})
metadata['description'] = description_tag['content'] if description_tag else None
description_tag = head.find('meta', attrs={'name': 'description'})
metadata['description'] = description_tag.get('content', '').strip() if description_tag else None
# Meta keywords
keywords_tag = soup.find('meta', attrs={'name': 'keywords'})
metadata['keywords'] = keywords_tag['content'] if keywords_tag else None
keywords_tag = head.find('meta', attrs={'name': 'keywords'})
metadata['keywords'] = keywords_tag.get('content', '').strip() if keywords_tag else None
# Meta author
author_tag = soup.find('meta', attrs={'name': 'author'})
metadata['author'] = author_tag['content'] if author_tag else None
author_tag = head.find('meta', attrs={'name': 'author'})
metadata['author'] = author_tag.get('content', '').strip() if author_tag else None
# Open Graph metadata
og_tags = soup.find_all('meta', attrs={'property': lambda value: value and value.startswith('og:')})
og_tags = head.find_all('meta', attrs={'property': re.compile(r'^og:')})
for tag in og_tags:
property_name = tag['property']
metadata[property_name] = tag['content']
property_name = tag.get('property', '').strip()
content = tag.get('content', '').strip()
if property_name and content:
metadata[property_name] = content
# Twitter Card metadata
twitter_tags = soup.find_all('meta', attrs={'name': lambda value: value and value.startswith('twitter:')})
twitter_tags = head.find_all('meta', attrs={'name': re.compile(r'^twitter:')})
for tag in twitter_tags:
property_name = tag['name']
metadata[property_name] = tag['content']
property_name = tag.get('name', '').strip()
content = tag.get('content', '').strip()
if property_name and content:
metadata[property_name] = content
return metadata
def extract_xml_tags(string):
tags = re.findall(r'<(\w+)>', string)
return list(set(tags))
@@ -1046,3 +1145,82 @@ 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

30
crawl4ai/version_manager.py Normal file
View File

@@ -0,0 +1,30 @@
# 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

17
crawl4ai/web_crawler.py
View File

@@ -10,6 +10,7 @@ from .extraction_strategy import *
from .crawler_strategy import *
from typing import List
from concurrent.futures import ThreadPoolExecutor
from .content_scraping_strategy import WebScrapingStrategy
from .config import *
import warnings
import json
@@ -181,7 +182,21 @@ class WebCrawler:
# Extract content from HTML
try:
t1 = time.time()
result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
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))
if verbose:
print(f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds")

19
deploy/railway/README.md Normal file
View File

@@ -0,0 +1,19 @@
# Railway Deployment
## Quick Deploy
[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/crawl4ai)
## Manual Setup
1. Fork this repository
2. Create a new Railway project
3. Configure environment variables:
- `INSTALL_TYPE`: basic or all
- `ENABLE_GPU`: true/false
4. Deploy!
## Configuration
See `railway.toml` for:
- Memory limits
- Health checks
- Restart policies
- Scaling options

33
deploy/railway/button.json Normal file
View File

@@ -0,0 +1,33 @@
{
"name": "Crawl4AI",
"description": "LLM Friendly Web Crawler & Scraper",
"render": {
"dockerfile": {
"path": "Dockerfile"
}
},
"env": [
{
"key": "INSTALL_TYPE",
"description": "Installation type (basic/all)",
"default": "basic",
"required": true
},
{
"key": "ENABLE_GPU",
"description": "Enable GPU support",
"default": "false",
"required": false
}
],
"services": [
{
"name": "web",
"dockerfile": "./Dockerfile",
"healthcheck": {
"path": "/health",
"port": 11235
}
}
]
}

18
deploy/railway/railway.toml Normal file
View File

@@ -0,0 +1,18 @@
# railway.toml
[build]
builder = "DOCKERFILE"
dockerfilePath = "Dockerfile"
[deploy]
startCommand = "uvicorn main:app --host 0.0.0.0 --port $PORT"
healthcheckPath = "/health"
restartPolicyType = "ON_FAILURE"
restartPolicyMaxRetries = 3
[deploy.memory]
soft = 2048 # 2GB min for Playwright
hard = 4096 # 4GB max
[deploy.scaling]
min = 1
max = 1

27
docker-compose.hub.yml Normal file
View File

@@ -0,0 +1,27 @@
services:
crawl4ai:
image: unclecode/crawl4ai:basic # Pull image from Docker Hub
ports:
- "11235:11235" # FastAPI server
- "8000:8000" # Alternative port
- "9222:9222" # Browser debugging
- "8080:8080" # Additional port
environment:
- CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-} # Optional API token
- OPENAI_API_KEY=${OPENAI_API_KEY:-} # Optional OpenAI API key
- CLAUDE_API_KEY=${CLAUDE_API_KEY:-} # Optional Claude API key
volumes:
- /dev/shm:/dev/shm # Shared memory for browser operations
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

33
docker-compose.local.yml Normal file
View File

@@ -0,0 +1,33 @@
services:
crawl4ai:
build:
context: .
dockerfile: Dockerfile
args:
PYTHON_VERSION: 3.10
INSTALL_TYPE: all
ENABLE_GPU: false
ports:
- "11235:11235" # FastAPI server
- "8000:8000" # Alternative port
- "9222:9222" # Browser debugging
- "8080:8080" # Additional port
environment:
- CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-} # Optional API token
- OPENAI_API_KEY=${OPENAI_API_KEY:-} # Optional OpenAI API key
- CLAUDE_API_KEY=${CLAUDE_API_KEY:-} # Optional Claude API key
volumes:
- /dev/shm:/dev/shm # Shared memory for browser operations
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

62
docker-compose.yml Normal file
View File

@@ -0,0 +1,62 @@
services:
crawl4ai:
build:
context: .
dockerfile: Dockerfile
args:
PYTHON_VERSION: 3.10
INSTALL_TYPE: all
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

69
docs/examples/docker_example.py
View File

@@ -7,12 +7,16 @@ import os
from typing import Dict, Any
class Crawl4AiTester:
def __init__(self, base_url: str = "http://localhost:11235"):
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') 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)
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}")
@@ -22,7 +26,7 @@ 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}")
result = requests.get(f"{self.base_url}/task/{task_id}", headers=self.headers)
status = result.json()
if status["status"] == "failed":
@@ -33,9 +37,30 @@ 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()
tester = Crawl4AiTester(
base_url="http://localhost:11235" ,
# base_url="https://api.crawl4ai.com" # just for example
# api_token="test" # just for example
)
print(f"Testing Crawl4AI Docker {version} version")
# Health check with timeout and retry
@@ -53,7 +78,10 @@ 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(tester)
# test_basic_crawl_sync(tester)
test_basic_crawl_direct(tester)
# if version in ["full", "transformer"]:
# test_cosine_extraction(tester)
@@ -70,7 +98,8 @@ def test_basic_crawl(tester: Crawl4AiTester):
print("\n=== Testing Basic Crawl ===")
request = {
"urls": "https://www.nbcnews.com/business",
"priority": 10
"priority": 10,
"session_id": "test"
}
result = tester.submit_and_wait(request)
@@ -78,6 +107,34 @@ 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 = {

12
docs/examples/quickstart_async.py
View File

@@ -71,12 +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:

238
docs/examples/v0.3.74.overview.py Normal file
View File

@@ -0,0 +1,238 @@
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. Content Filtering with BM25 Example
async def content_filtering_example():
"""Example of using the new BM25 content filtering"""
async with AsyncWebCrawler(verbose=True) as crawler:
# Create filter with custom query for OpenAI's blog
content_filter = BM25ContentFilter(
# user_query="Investment and fundraising",
# user_query="Robotic",
bm25_threshold=1.0
)
result = await crawler.arun(
url="https://techcrunch.com/",
content_filter=content_filter,
cache_mode=CacheMode.BYPASS
)
print(f"Filtered content: {len(result.fit_markdown)}")
print(f"Filtered content: {result.fit_markdown}")
# Save html
with open(os.path.join(__data__, "techcrunch.html"), "w") as f:
f.write(result.fit_html)
with open(os.path.join(__data__, "filtered_content.md"), "w") as f:
f.write(result.fit_markdown)
# 3. 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)
# 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 result: {result}")
if result["status"] == "completed":
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 Content Filtering Example:")
await content_filtering_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())

84
docs/md_v2/advanced/managed_browser.md Normal file
View File

@@ -0,0 +1,84 @@
# 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.

10
docs/md_v2/advanced/session-management-advanced.md
View File

@@ -30,7 +30,7 @@ Let's start with a basic example of session-based crawling:
```python
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
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",
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
commits = json.loads(result.extracted_content)

2
docs/md_v2/advanced/session-management.md
View File

@@ -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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
if result.success:

42
docs/md_v2/api/arun.md
View File

@@ -8,11 +8,26 @@ 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
bypass_cache=False, # Skip cache for this request
cache_mode=CacheMode.ENABLED, # Control cache behavior
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
@@ -162,14 +177,13 @@ await crawler.arun(
## Parameter Interactions and Notes
1. **Magic Mode Combinations**
1. **Cache and Performance Setup**
```python
# Full anti-detection setup
# Optimal caching for repeated crawls
await crawler.arun(
magic=True,
headless=False,
simulate_user=True,
override_navigator=True
cache_mode=CacheMode.ENABLED,
word_count_threshold=10,
process_iframes=False
)
```
@@ -179,7 +193,8 @@ 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
delay_before_return_html=2.0,
cache_mode=CacheMode.WRITE_ONLY # Cache results after dynamic load
)
```
@@ -192,7 +207,8 @@ await crawler.arun(
extraction_strategy=my_strategy,
chunking_strategy=my_chunking,
process_iframes=True,
remove_overlay_elements=True
remove_overlay_elements=True,
cache_mode=CacheMode.ENABLED
)
```
@@ -201,7 +217,7 @@ await crawler.arun(
1. **Performance Optimization**
```python
await crawler.arun(
bypass_cache=False, # Use cache when possible
cache_mode=CacheMode.ENABLED, # Use full caching
word_count_threshold=10, # Filter out noise
process_iframes=False # Skip iframes if not needed
)
@@ -212,7 +228,8 @@ 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
page_timeout=60000, # Longer timeout for slow pages
cache_mode=CacheMode.WRITE_ONLY # Cache results after successful crawl
)
```
@@ -221,6 +238,7 @@ 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
keep_data_attributes=False, # Remove data attributes
cache_mode=CacheMode.ENABLED # Use cache for faster processing
)
```

1
docs/md_v2/api/crawl-result.md
View File

@@ -20,6 +20,7 @@ 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

3
docs/md_v2/api/parameters.md
View File

@@ -32,4 +32,5 @@
| 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 | 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 |

79
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View File

@@ -0,0 +1,79 @@
# 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
```

84
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@@ -0,0 +1,84 @@
# 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.

307
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View File

@@ -7,66 +7,325 @@ 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
```
Test the deployment:
## 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:
```python
import requests
# Test health endpoint
health = requests.get("http://localhost:11235/health")
print("Health check:", health.json())
# 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 basic crawl
# Making authenticated requests
response = requests.post(
"http://localhost:11235/crawl",
headers=headers,
json={
"urls": "https://www.nbcnews.com/business",
"urls": "https://example.com",
"priority": 10
}
)
# Checking task status
task_id = response.json()["task_id"]
print("Task ID:", task_id)
status = requests.get(
f"http://localhost:11235/task/{task_id}",
headers=headers
)
```
## Available Images 🏷️
### Using with Docker Compose
- `unclecode/crawl4ai:basic` - Basic web crawling capabilities
- `unclecode/crawl4ai:all` - Full installation with all features
- `unclecode/crawl4ai:gpu` - GPU-enabled version for ML features
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`).
## 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
```
### Volume Mounting
Mount a directory for persistent data:
```bash
# With security and LLM support
docker run -p 11235:11235 \
-v $(pwd)/data:/app/data \
-e CRAWL4AI_API_TOKEN=your_secret_token \
-e OPENAI_API_KEY=sk-... \
-e ANTHROPIC_API_KEY=sk-ant-... \
unclecode/crawl4ai:all
```
### Resource Limits
### Using Docker Compose (Recommended) 🐳
Control container resources:
Create a `docker-compose.yml`:
```yaml
version: '3.8'
services:
crawl4ai:
image: unclecode/crawl4ai:all
ports:
- "11235:11235"
environment:
- CRAWL4AI_API_TOKEN=${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:
```bash
docker run -p 11235:11235 \
--memory=4g \
--cpus=2 \
unclecode/crawl4ai:all
CRAWL4AI_API_TOKEN=secret123 OPENAI_API_KEY=sk-... docker-compose up
```
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

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@@ -0,0 +1,148 @@
# 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.

45
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@@ -58,6 +58,51 @@ 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.

235
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@@ -0,0 +1,235 @@
# 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.

22
docs/md_v2/basic/quickstart.md
View File

@@ -8,7 +8,7 @@ First, let's import the necessary modules and create an instance of `AsyncWebCra
```python
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CasheMode
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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", bypass_cache=True)
result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
# Use WebKit
async with AsyncWebCrawler(browser_type="webkit", verbose=True, headless=True) as crawler:
result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
# Use Chromium (default)
async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
result = await crawler.arun(url="https://www.example.com", bypass_cache=True)
result = await crawler.arun(url="https://www.example.com", cache_mode=CacheMode.BYPASS)
```
### 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",
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
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", bypass_cache=True)
result2 = await crawler.arun(url="https://www.nbcnews.com/business", cache_mode=CacheMode.BYPASS)
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",
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
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,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
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",
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
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,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
verbose=False,
)
end = time.time()

10
docs/md_v2/basic/simple-crawling.md
View File

@@ -12,7 +12,9 @@ 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__":
@@ -24,7 +26,7 @@ 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")
result = await crawler.arun(url="https://example.com", fit_markdown=True)
# Different content formats
print(result.html) # Raw HTML
@@ -81,7 +83,7 @@ Here's a more comprehensive example showing common usage patterns:
```python
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
async def main():
async with AsyncWebCrawler(verbose=True) as crawler:
@@ -97,7 +99,7 @@ async def main():
remove_overlay_elements=True,
# Cache control
bypass_cache=False # Use cache if available
cache_mode=CacheMode.ENABLE # Use cache if available
)
if result.success:

4
docs/md_v2/tutorial/episode_11_2_Extraction_Strategies_LLM.md
View File

@@ -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."
),
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
print(result.extracted_content)
```

4
docs/md_v2/tutorial/episode_11_3_Extraction_Strategies_Cosine.md
View File

@@ -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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
print(result.extracted_content)
```

10
docs/md_v2/tutorial/tutorial.md
View File

@@ -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
from crawl4ai import AsyncWebCrawler, CacheMode
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."
),
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
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,
bypass_cache=True
cache_mode=CacheMode.BYPASS
)
print(result.extracted_content)
```

125
main.py
View File

@@ -10,6 +10,8 @@ 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
@@ -23,7 +25,8 @@ import logging
from enum import Enum
from dataclasses import dataclass
import json
from crawl4ai import AsyncWebCrawler, CrawlResult
from crawl4ai import AsyncWebCrawler, CrawlResult, CacheMode
from crawl4ai.config import MIN_WORD_THRESHOLD
from crawl4ai.extraction_strategy import (
LLMExtractionStrategy,
CosineStrategy,
@@ -51,18 +54,31 @@ 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
crawler_params: Dict[str, Any] = {}
priority: int = Field(default=5, ge=1, le=10)
ttl: Optional[int] = 3600
chunking_strategy: Optional[ChunkingStrategy] = None
content_filter: Optional[ContentFilter] = None
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:
@@ -276,12 +292,15 @@ 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:
@@ -293,6 +312,8 @@ 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,
)
@@ -321,7 +342,27 @@ app.add_middleware(
# Mount the pages directory as a static directory
app.mount("/pages", StaticFiles(directory=__location__ + "/pages"), name="pages")
app.mount("/mkdocs", StaticFiles(directory="site", html=True), name="mkdocs")
# 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")
site_templates = Jinja2Templates(directory=__location__ + "/site")
templates = Jinja2Templates(directory=__location__ + "/pages")
@@ -337,15 +378,18 @@ async def shutdown_event():
@app.get("/")
def read_root():
return RedirectResponse(url="/mkdocs")
if os.path.exists(__location__ + "/site"):
return RedirectResponse(url="/mkdocs")
# Return a json response
return {"message": "Crawl4AI API service is running"}
@app.post("/crawl")
@app.post("/crawl", dependencies=[Depends(verify_token)])
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}")
@app.get("/task/{task_id}", dependencies=[Depends(verify_token)])
async def get_task_status(task_id: str):
task_info = crawler_service.task_manager.get_task(task_id)
if not task_info:
@@ -367,6 +411,71 @@ 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()

0
middlewares.py
View File

1
mkdocs.yml
View File

@@ -17,6 +17,7 @@ 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'

5
requirements-dev.txt
View File

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

7
requirements.txt
View File

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

33
setup.py
View File

@@ -5,34 +5,38 @@ 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
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 the requirements from requirements.txt
# Read requirements and version
__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()
# Read version from __init__.py
with open("crawl4ai/_version.py") as f:
with open("crawl4ai/__version__.py") as f:
for line in f:
if line.startswith("__version__"):
version = line.split("=")[1].strip().strip('"')
break
# Define the requirements for different environments
# Define requirements
default_requirements = requirements
# 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" ]
@@ -50,10 +54,24 @@ def install_playwright():
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")
class PostInstallCommand(install):
def run(self):
install.run(self)
install_playwright()
# run_migration()
setup(
name="Crawl4AI",
@@ -66,7 +84,7 @@ setup(
author_email="unclecode@kidocode.com",
license="MIT",
packages=find_packages(),
install_requires=default_requirements + ["playwright"], # Add playwright to default requirements
install_requires=default_requirements + ["playwright", "aiofiles"], # Added aiofiles
extras_require={
"torch": torch_requirements,
"transformer": transformer_requirements,
@@ -77,6 +95,7 @@ setup(
entry_points={
'console_scripts': [
'crawl4ai-download-models=crawl4ai.model_loader:main',
'crawl4ai-migrate=crawl4ai.migrations:main', # Added migration command
],
},
classifiers=[

2179
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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())

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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__])

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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()

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# ## 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 DefaultMarkdownGenerationStrategy
# 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 = DefaultMarkdownGenerationStrategy()
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 = DefaultMarkdownGenerationStrategy()
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 = DefaultMarkdownGenerationStrategy()
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 = DefaultMarkdownGenerationStrategy()
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 = DefaultMarkdownGenerationStrategy()
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 = DefaultMarkdownGenerationStrategy()
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()

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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)