a68cbb232b
Add new features to enhance browser automation and HTML extraction: - Add CDP browser launch capability with customizable ports and profiles - Implement JsonLxmlExtractionStrategy for faster HTML parsing - Add CLI command 'crwl cdp' for launching standalone CDP browsers - Support connecting to external CDP browsers via URL - Optimize selector caching and context-sensitive queries BREAKING CHANGE: LLMConfig import path changed from crawl4ai.types to crawl4ai
87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
import os
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import asyncio
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from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
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from crawl4ai import LLMConfig
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from crawl4ai.content_filter_strategy import LLMContentFilter
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async def test_llm_filter():
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# Create an HTML source that needs intelligent filtering
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url = "https://docs.python.org/3/tutorial/classes.html"
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browser_config = BrowserConfig(
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headless=True,
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verbose=True
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)
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# run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
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run_config = CrawlerRunConfig(cache_mode=CacheMode.ENABLED)
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async with AsyncWebCrawler(config=browser_config) as crawler:
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# First get the raw HTML
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result = await crawler.arun(url, config=run_config)
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html = result.cleaned_html
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# Initialize LLM filter with focused instruction
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filter = LLMContentFilter(
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llm_config=LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')),
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instruction="""
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Focus on extracting the core educational content about Python classes.
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Include:
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- Key concepts and their explanations
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- Important code examples
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- Essential technical details
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Exclude:
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- Navigation elements
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- Sidebars
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- Footer content
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- Version information
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- Any non-essential UI elements
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Format the output as clean markdown with proper code blocks and headers.
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""",
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verbose=True
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)
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filter = LLMContentFilter(
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llm_config=LLMConfig(provider="openai/gpt-4o",api_token=os.getenv('OPENAI_API_KEY')),
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chunk_token_threshold=2 ** 12 * 2, # 2048 * 2
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ignore_cache = True,
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instruction="""
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Extract the main educational content while preserving its original wording and substance completely. Your task is to:
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1. Maintain the exact language and terminology used in the main content
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2. Keep all technical explanations, examples, and educational content intact
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3. Preserve the original flow and structure of the core content
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4. Remove only clearly irrelevant elements like:
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- Navigation menus
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- Advertisement sections
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- Cookie notices
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- Footers with site information
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- Sidebars with external links
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- Any UI elements that don't contribute to learning
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The goal is to create a clean markdown version that reads exactly like the original article,
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keeping all valuable content but free from distracting elements. Imagine you're creating
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a perfect reading experience where nothing valuable is lost, but all noise is removed.
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""",
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verbose=True
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)
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# Apply filtering
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filtered_content = filter.filter_content(html)
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# Show results
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print("\nFiltered Content Length:", len(filtered_content))
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print("\nFirst 500 chars of filtered content:")
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if filtered_content:
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print(filtered_content[0][:500])
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# Save on disc the markdown version
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with open("filtered_content.md", "w", encoding="utf-8") as f:
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f.write("\n".join(filtered_content))
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# Show token usage
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filter.show_usage()
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if __name__ == "__main__":
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asyncio.run(test_llm_filter()) |