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Enhance Crawl4AI with new features and documentation

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- Fix crawler text mode for improved performance; cover missing `srcset` and `data_srcset` attributes in image tags.
  - Introduced Managed Browsers for enhanced crawling experience.
  - Updated documentation for clearer navigation on configuration.
  - Changed 'text_only' to 'text_mode' in configuration and methods.
  - Improved performance and relevance in content filtering strategies.
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docs/md_v2/advanced/session-management-advanced.md
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# Session-Based Crawling for Dynamic Content
### Session-Based Crawling for Dynamic Content
In modern web applications, content is often loaded dynamically without changing the URL. Examples include "Load More" buttons, infinite scrolling, or paginated content that updates via JavaScript. To effectively crawl such websites, Crawl4AI provides powerful session-based crawling capabilities.
In modern web applications, content is often loaded dynamically without changing the URL. Examples include "Load More" buttons, infinite scrolling, or paginated content that updates via JavaScript. Crawl4AI provides session-based crawling capabilities to handle such scenarios effectively.
This guide will explore advanced techniques for crawling dynamic content using Crawl4AI's session management features.
This guide explores advanced techniques for crawling dynamic content using Crawl4AI's session management features.
---
## Understanding Session-Based Crawling
Session-based crawling allows you to maintain a persistent browser session across multiple requests. This is crucial when:
Session-based crawling allows you to reuse a persistent browser session across multiple actions. This means the same browser tab (or page object) is used throughout, enabling:
1. The content changes dynamically without URL changes
2. You need to interact with the page (e.g., clicking buttons) between requests
3. The site requires authentication or maintains state across pages
1. **Efficient handling of dynamic content** without reloading the page.
2. **JavaScript actions before and after crawling** (e.g., clicking buttons or scrolling).
3. **State maintenance** for authenticated sessions or multi-step workflows.
4. **Faster sequential crawling**, as it avoids reopening tabs or reallocating resources.
Crawl4AI's `AsyncWebCrawler` class supports session-based crawling through the `session_id` parameter and related methods.
**Note:** Session-based crawling is ideal for sequential operations, not parallel tasks.
---
## Basic Concepts
Before diving into examples, let's review some key concepts:
Before diving into examples, here are some key concepts:
- **Session ID**: A unique identifier for a browsing session. Use the same `session_id` across multiple `arun` calls to maintain state.
- **JavaScript Execution**: Use the `js_code` parameter to execute JavaScript on the page, such as clicking a "Load More" button.
- **CSS Selectors**: Use these to target specific elements for extraction or interaction.
- **Extraction Strategy**: Define how to extract structured data from the page.
- **Wait Conditions**: Specify conditions to wait for before considering the page loaded.
- **Session ID**: A unique identifier for a browsing session. Use the same `session_id` across multiple requests to maintain state.
- **BrowserConfig & CrawlerRunConfig**: These configuration objects control browser settings and crawling behavior.
- **JavaScript Execution**: Use `js_code` to perform actions like clicking buttons.
- **CSS Selectors**: Target specific elements for interaction or data extraction.
- **Extraction Strategy**: Define rules to extract structured data.
- **Wait Conditions**: Specify conditions to wait for before proceeding.
---
## Example 1: Basic Session-Based Crawling
Let's start with a basic example of session-based crawling:
A simple example using session-based crawling:
```python
import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
from crawl4ai.cache_context import CacheMode
async def basic_session_crawl():
async with AsyncWebCrawler(verbose=True) as crawler:
session_id = "my_session"
async with AsyncWebCrawler() as crawler:
session_id = "dynamic_content_session"
url = "https://example.com/dynamic-content"
for page in range(3):
result = await crawler.arun(
config = CrawlerRunConfig(
url=url,
session_id=session_id,
js_code="document.querySelector('.load-more-button').click();" if page > 0 else None,
@@ -46,6 +55,7 @@ async def basic_session_crawl():
cache_mode=CacheMode.BYPASS
)
result = await crawler.arun(config=config)
print(f"Page {page + 1}: Found {result.extracted_content.count('.content-item')} items")
await crawler.crawler_strategy.kill_session(session_id)
@@ -53,17 +63,16 @@ async def basic_session_crawl():
asyncio.run(basic_session_crawl())
```
This example demonstrates:
1. Using a consistent `session_id` across multiple `arun` calls
2. Executing JavaScript to load more content after the first page
3. Using a CSS selector to extract specific content
4. Properly closing the session after crawling
This example shows:
1. Reusing the same `session_id` across multiple requests.
2. Executing JavaScript to load more content dynamically.
3. Properly closing the session to free resources.
---
## Advanced Technique 1: Custom Execution Hooks
Crawl4AI allows you to set custom hooks that execute at different stages of the crawling process. This is particularly useful for handling complex loading scenarios.
Here's an example that waits for new content to appear before proceeding:
Use custom hooks to handle complex scenarios, such as waiting for content to load dynamically:
```python
async def advanced_session_crawl_with_hooks():
@@ -75,202 +84,96 @@ async def advanced_session_crawl_with_hooks():
while True:
await page.wait_for_selector("li.commit-item h4")
commit = await page.query_selector("li.commit-item h4")
commit = await commit.evaluate("(element) => element.textContent")
commit = commit.strip()
commit = await commit.evaluate("(element) => element.textContent").strip()
if commit and commit != first_commit:
first_commit = commit
break
await asyncio.sleep(0.5)
except Exception as e:
print(f"Warning: New content didn't appear after JavaScript execution: {e}")
print(f"Warning: New content didn't appear: {e}")
async with AsyncWebCrawler(verbose=True) as crawler:
async with AsyncWebCrawler() as crawler:
session_id = "commit_session"
url = "https://github.com/example/repo/commits/main"
crawler.crawler_strategy.set_hook("on_execution_started", on_execution_started)
url = "https://github.com/example/repo/commits/main"
session_id = "commit_session"
all_commits = []
js_next_page = """
const button = document.querySelector('a.pagination-next');
if (button) button.click();
"""
js_next_page = """document.querySelector('a.pagination-next').click();"""
for page in range(3):
result = await crawler.arun(
config = CrawlerRunConfig(
url=url,
session_id=session_id,
css_selector="li.commit-item",
js_code=js_next_page if page > 0 else None,
cache_mode=CacheMode.BYPASS,
js_only=page > 0
css_selector="li.commit-item",
js_only=page > 0,
cache_mode=CacheMode.BYPASS
)
commits = result.extracted_content.select("li.commit-item")
all_commits.extend(commits)
print(f"Page {page + 1}: Found {len(commits)} commits")
result = await crawler.arun(config=config)
print(f"Page {page + 1}: Found {len(result.extracted_content)} commits")
await crawler.crawler_strategy.kill_session(session_id)
print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
asyncio.run(advanced_session_crawl_with_hooks())
```
This technique uses a custom `on_execution_started` hook to ensure new content has loaded before proceeding to the next step.
This technique ensures new content loads before the next action.
---
## Advanced Technique 2: Integrated JavaScript Execution and Waiting
Instead of using separate hooks, you can integrate the waiting logic directly into your JavaScript execution. This approach can be more concise and easier to manage for some scenarios.
Here's an example:
Combine JavaScript execution and waiting logic for concise handling of dynamic content:
```python
async def integrated_js_and_wait_crawl():
async with AsyncWebCrawler(verbose=True) as crawler:
url = "https://github.com/example/repo/commits/main"
async with AsyncWebCrawler() as crawler:
session_id = "integrated_session"
all_commits = []
url = "https://github.com/example/repo/commits/main"
js_next_page_and_wait = """
(async () => {
const getCurrentCommit = () => {
const commits = document.querySelectorAll('li.commit-item h4');
return commits.length > 0 ? commits[0].textContent.trim() : null;
};
const getCurrentCommit = () => document.querySelector('li.commit-item h4').textContent.trim();
const initialCommit = getCurrentCommit();
const button = document.querySelector('a.pagination-next');
if (button) button.click();
while (true) {
document.querySelector('a.pagination-next').click();
while (getCurrentCommit() === initialCommit) {
await new Promise(resolve => setTimeout(resolve, 100));
const newCommit = getCurrentCommit();
if (newCommit && newCommit !== initialCommit) {
break;
}
}
})();
"""
schema = {
"name": "Commit Extractor",
"baseSelector": "li.commit-item",
"fields": [
{
"name": "title",
"selector": "h4.commit-title",
"type": "text",
"transform": "strip",
},
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
for page in range(3):
result = await crawler.arun(
config = CrawlerRunConfig(
url=url,
session_id=session_id,
css_selector="li.commit-item",
extraction_strategy=extraction_strategy,
js_code=js_next_page_and_wait if page > 0 else None,
css_selector="li.commit-item",
js_only=page > 0,
cache_mode=CacheMode.BYPASS
)
commits = json.loads(result.extracted_content)
all_commits.extend(commits)
print(f"Page {page + 1}: Found {len(commits)} commits")
result = await crawler.arun(config=config)
print(f"Page {page + 1}: Found {len(result.extracted_content)} commits")
await crawler.crawler_strategy.kill_session(session_id)
print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
asyncio.run(integrated_js_and_wait_crawl())
```
This approach combines the JavaScript for clicking the "next" button and waiting for new content to load into a single script.
## Advanced Technique 3: Using the `wait_for` Parameter
Crawl4AI provides a `wait_for` parameter that allows you to specify a condition to wait for before considering the page fully loaded. This can be particularly useful for dynamic content.
Here's an example:
```python
async def wait_for_parameter_crawl():
async with AsyncWebCrawler(verbose=True) as crawler:
url = "https://github.com/example/repo/commits/main"
session_id = "wait_for_session"
all_commits = []
js_next_page = """
const commits = document.querySelectorAll('li.commit-item h4');
if (commits.length > 0) {
window.lastCommit = commits[0].textContent.trim();
}
const button = document.querySelector('a.pagination-next');
if (button) button.click();
"""
wait_for = """() => {
const commits = document.querySelectorAll('li.commit-item h4');
if (commits.length === 0) return false;
const firstCommit = commits[0].textContent.trim();
return firstCommit !== window.lastCommit;
}"""
schema = {
"name": "Commit Extractor",
"baseSelector": "li.commit-item",
"fields": [
{
"name": "title",
"selector": "h4.commit-title",
"type": "text",
"transform": "strip",
},
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
for page in range(3):
result = await crawler.arun(
url=url,
session_id=session_id,
css_selector="li.commit-item",
extraction_strategy=extraction_strategy,
js_code=js_next_page if page > 0 else None,
wait_for=wait_for if page > 0 else None,
js_only=page > 0,
cache_mode=CacheMode.BYPASS
)
commits = json.loads(result.extracted_content)
all_commits.extend(commits)
print(f"Page {page + 1}: Found {len(commits)} commits")
await crawler.crawler_strategy.kill_session(session_id)
print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
asyncio.run(wait_for_parameter_crawl())
```
This technique separates the JavaScript execution (clicking the "next" button) from the waiting condition, providing more flexibility and clarity in some scenarios.
---
## Best Practices for Session-Based Crawling
1. **Use Unique Session IDs**: Ensure each crawling session has a unique `session_id` to prevent conflicts.
2. **Close Sessions**: Always close sessions using `kill_session` when you're done to free up resources.
3. **Handle Errors**: Implement proper error handling to deal with unexpected situations during crawling.
4. **Respect Website Terms**: Ensure your crawling adheres to the website's terms of service and robots.txt file.
5. **Implement Delays**: Add appropriate delays between requests to avoid overwhelming the target server.
6. **Use Extraction Strategies**: Leverage `JsonCssExtractionStrategy` or other extraction strategies for structured data extraction.
7. **Optimize JavaScript**: Keep your JavaScript execution concise and efficient to improve crawling speed.
8. **Monitor Performance**: Keep an eye on memory usage and crawling speed, especially for long-running sessions.
1. **Unique Session IDs**: Assign descriptive and unique `session_id` values.
2. **Close Sessions**: Always clean up sessions with `kill_session` after use.
3. **Error Handling**: Anticipate and handle errors gracefully.
4. **Respect Websites**: Follow terms of service and robots.txt.
5. **Delays**: Add delays to avoid overwhelming servers.
6. **Optimize JavaScript**: Keep scripts concise for better performance.
7. **Monitor Resources**: Track memory and CPU usage for long sessions.
---
## Conclusion
Session-based crawling with Crawl4AI provides powerful capabilities for handling dynamic content and complex web applications. By leveraging session management, JavaScript execution, and waiting strategies, you can effectively crawl and extract data from a wide range of modern websites.
Remember to use these techniques responsibly and in compliance with website policies and ethical web scraping practices.
For more advanced usage and API details, refer to the Crawl4AI API documentation.
Session-based crawling in Crawl4AI is a robust solution for handling dynamic content and multi-step workflows. By combining session management, JavaScript execution, and structured extraction strategies, you can effectively navigate and extract data from modern web applications. Always adhere to ethical web scraping practices and respect website policies.