128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
"""
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Example demonstrating different extraction strategies with various input formats.
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This example shows how to:
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1. Use different input formats (markdown, HTML, fit_markdown)
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2. Work with JSON-based extractors (CSS and XPath)
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3. Use LLM-based extraction with different input formats
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4. Configure browser and crawler settings properly
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"""
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import asyncio
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import os
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from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
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from crawl4ai.extraction_strategy import (
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LLMExtractionStrategy,
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JsonCssExtractionStrategy,
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JsonXPathExtractionStrategy,
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)
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from crawl4ai.content_filter_strategy import PruningContentFilter
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from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
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async def run_extraction(crawler: AsyncWebCrawler, url: str, strategy, name: str):
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"""Helper function to run extraction with proper configuration"""
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try:
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# Configure the crawler run settings
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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extraction_strategy=strategy,
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markdown_generator=DefaultMarkdownGenerator(
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content_filter=PruningContentFilter() # For fit_markdown support
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),
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)
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# Run the crawler
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result = await crawler.arun(url=url, config=config)
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if result.success:
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print(f"\n=== {name} Results ===")
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print(f"Extracted Content: {result.extracted_content}")
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print(f"Raw Markdown Length: {len(result.markdown_v2.raw_markdown)}")
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print(
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f"Citations Markdown Length: {len(result.markdown_v2.markdown_with_citations)}"
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)
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else:
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print(f"Error in {name}: Crawl failed")
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except Exception as e:
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print(f"Error in {name}: {str(e)}")
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async def main():
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# Example URL (replace with actual URL)
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url = "https://example.com/product-page"
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# Configure browser settings
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browser_config = BrowserConfig(headless=True, verbose=True)
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# Initialize extraction strategies
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# 1. LLM Extraction with different input formats
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markdown_strategy = LLMExtractionStrategy(
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provider="openai/gpt-4o-mini",
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api_token=os.getenv("OPENAI_API_KEY"),
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instruction="Extract product information including name, price, and description",
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)
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html_strategy = LLMExtractionStrategy(
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input_format="html",
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provider="openai/gpt-4o-mini",
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api_token=os.getenv("OPENAI_API_KEY"),
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instruction="Extract product information from HTML including structured data",
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)
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fit_markdown_strategy = LLMExtractionStrategy(
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input_format="fit_markdown",
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provider="openai/gpt-4o-mini",
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api_token=os.getenv("OPENAI_API_KEY"),
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instruction="Extract product information from cleaned markdown",
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)
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# 2. JSON CSS Extraction (automatically uses HTML input)
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css_schema = {
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"baseSelector": ".product",
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"fields": [
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{"name": "title", "selector": "h1.product-title", "type": "text"},
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{"name": "price", "selector": ".price", "type": "text"},
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{"name": "description", "selector": ".description", "type": "text"},
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],
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}
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css_strategy = JsonCssExtractionStrategy(schema=css_schema)
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# 3. JSON XPath Extraction (automatically uses HTML input)
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xpath_schema = {
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"baseSelector": "//div[@class='product']",
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"fields": [
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{
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"name": "title",
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"selector": ".//h1[@class='product-title']/text()",
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"type": "text",
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},
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{
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"name": "price",
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"selector": ".//span[@class='price']/text()",
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"type": "text",
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},
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{
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"name": "description",
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"selector": ".//div[@class='description']/text()",
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"type": "text",
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},
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],
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}
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xpath_strategy = JsonXPathExtractionStrategy(schema=xpath_schema)
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# Use context manager for proper resource handling
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async with AsyncWebCrawler(config=browser_config) as crawler:
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# Run all strategies
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await run_extraction(crawler, url, markdown_strategy, "Markdown LLM")
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await run_extraction(crawler, url, html_strategy, "HTML LLM")
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await run_extraction(crawler, url, fit_markdown_strategy, "Fit Markdown LLM")
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await run_extraction(crawler, url, css_strategy, "CSS Extraction")
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await run_extraction(crawler, url, xpath_strategy, "XPath Extraction")
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if __name__ == "__main__":
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asyncio.run(main())
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