crawl4ai/docs/examples/quickstart.py

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import os
import time
from crawl4ai.web_crawler import WebCrawler
from crawl4ai.chunking_strategy import *
from crawl4ai.extraction_strategy import *
from crawl4ai.crawler_strategy import *
from rich import print
from rich.console import Console
from functools import lru_cache
console = Console()
@lru_cache()
def create_crawler():
crawler = WebCrawler(verbose=True)
crawler.warmup()
return crawler
def print_result(result):
# Print each key in one line and just the first 10 characters of each one's value and three dots
console.print(f"\t[bold]Result:[/bold]")
for key, value in result.model_dump().items():
if isinstance(value, str) and value:
console.print(f"\t{key}: [green]{value[:20]}...[/green]")
if result.extracted_content:
items = json.loads(result.extracted_content)
print(f"\t[bold]{len(items)} blocks is extracted![/bold]")
def cprint(message, press_any_key=False):
console.print(message)
if press_any_key:
console.print("Press any key to continue...", style="")
input()
def basic_usage(crawler):
cprint("🛠️ [bold cyan]Basic Usage: Simply provide a URL and let Crawl4ai do the magic![/bold cyan]")
result = crawler.run(url="https://www.nbcnews.com/business")
cprint("[LOG] 📦 [bold yellow]Basic crawl result:[/bold yellow]")
print_result(result)
def screenshot_usage(crawler):
cprint("\n📸 [bold cyan]Let's take a screenshot of the page![/bold cyan]")
result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
cprint("[LOG] 📦 [bold yellow]Screenshot result:[/bold yellow]")
# Save the screenshot to a file
with open("screenshot.png", "wb") as f:
f.write(base64.b64decode(result.screenshot))
cprint("Screenshot saved to 'screenshot.png'!")
print_result(result)
def understanding_parameters(crawler):
cprint("\n🧠 [bold cyan]Understanding 'bypass_cache' and 'include_raw_html' parameters:[/bold cyan]")
cprint("By default, Crawl4ai caches the results of your crawls. This means that subsequent crawls of the same URL will be much faster! Let's see this in action.")
# First crawl (reads from cache)
cprint("1⃣ First crawl (caches the result):", True)
start_time = time.time()
result = crawler.run(url="https://www.nbcnews.com/business")
end_time = time.time()
cprint(f"[LOG] 📦 [bold yellow]First crawl took {end_time - start_time} seconds and result (from cache):[/bold yellow]")
print_result(result)
# Force to crawl again
cprint("2⃣ Second crawl (Force to crawl again):", True)
start_time = time.time()
result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
end_time = time.time()
cprint(f"[LOG] 📦 [bold yellow]Second crawl took {end_time - start_time} seconds and result (forced to crawl):[/bold yellow]")
print_result(result)
def add_chunking_strategy(crawler):
# Adding a chunking strategy: RegexChunking
cprint("\n🧩 [bold cyan]Let's add a chunking strategy: RegexChunking![/bold cyan]", True)
cprint("RegexChunking is a simple chunking strategy that splits the text based on a given regex pattern. Let's see it in action!")
result = crawler.run(
url="https://www.nbcnews.com/business",
chunking_strategy=RegexChunking(patterns=["\n\n"])
)
cprint("[LOG] 📦 [bold yellow]RegexChunking result:[/bold yellow]")
print_result(result)
# Adding another chunking strategy: NlpSentenceChunking
cprint("\n🔍 [bold cyan]Time to explore another chunking strategy: NlpSentenceChunking![/bold cyan]", True)
cprint("NlpSentenceChunking uses NLP techniques to split the text into sentences. Let's see how it performs!")
result = crawler.run(
url="https://www.nbcnews.com/business",
chunking_strategy=NlpSentenceChunking()
)
cprint("[LOG] 📦 [bold yellow]NlpSentenceChunking result:[/bold yellow]")
print_result(result)
def add_extraction_strategy(crawler):
# Adding an extraction strategy: CosineStrategy
cprint("\n🧠 [bold cyan]Let's get smarter with an extraction strategy: CosineStrategy![/bold cyan]", True)
cprint("CosineStrategy uses cosine similarity to extract semantically similar blocks of text. Let's see it in action!")
result = crawler.run(
url="https://www.nbcnews.com/business",
extraction_strategy=CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method="ward", top_k=3, sim_threshold = 0.3, verbose=True)
)
cprint("[LOG] 📦 [bold yellow]CosineStrategy result:[/bold yellow]")
print_result(result)
# Using semantic_filter with CosineStrategy
cprint("You can pass other parameters like 'semantic_filter' to the CosineStrategy to extract semantically similar blocks of text. Let's see it in action!")
result = crawler.run(
url="https://www.nbcnews.com/business",
extraction_strategy=CosineStrategy(
semantic_filter="inflation rent prices",
)
)
cprint("[LOG] 📦 [bold yellow]CosineStrategy result with semantic filter:[/bold yellow]")
print_result(result)
def add_llm_extraction_strategy(crawler):
# Adding an LLM extraction strategy without instructions
cprint("\n🤖 [bold cyan]Time to bring in the big guns: LLMExtractionStrategy without instructions![/bold cyan]", True)
cprint("LLMExtractionStrategy uses a large language model to extract relevant information from the web page. Let's see it in action!")
result = crawler.run(
url="https://www.nbcnews.com/business",
extraction_strategy=LLMExtractionStrategy(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'))
)
cprint("[LOG] 📦 [bold yellow]LLMExtractionStrategy (no instructions) result:[/bold yellow]")
print_result(result)
# Adding an LLM extraction strategy with instructions
cprint("\n📜 [bold cyan]Let's make it even more interesting: LLMExtractionStrategy with instructions![/bold cyan]", True)
cprint("Let's say we are only interested in financial news. Let's see how LLMExtractionStrategy performs with instructions!")
result = crawler.run(
url="https://www.nbcnews.com/business",
extraction_strategy=LLMExtractionStrategy(
provider="openai/gpt-4o",
api_token=os.getenv('OPENAI_API_KEY'),
instruction="I am interested in only financial news"
)
)
cprint("[LOG] 📦 [bold yellow]LLMExtractionStrategy (with instructions) result:[/bold yellow]")
print_result(result)
result = crawler.run(
url="https://www.nbcnews.com/business",
extraction_strategy=LLMExtractionStrategy(
provider="openai/gpt-4o",
api_token=os.getenv('OPENAI_API_KEY'),
instruction="Extract only content related to technology"
)
)
cprint("[LOG] 📦 [bold yellow]LLMExtractionStrategy (with technology instruction) result:[/bold yellow]")
print_result(result)
def targeted_extraction(crawler):
# Using a CSS selector to extract only H2 tags
cprint("\n🎯 [bold cyan]Targeted extraction: Let's use a CSS selector to extract only H2 tags![/bold cyan]", True)
result = crawler.run(
url="https://www.nbcnews.com/business",
css_selector="h2"
)
cprint("[LOG] 📦 [bold yellow]CSS Selector (H2 tags) result:[/bold yellow]")
print_result(result)
def interactive_extraction(crawler):
# Passing JavaScript code to interact with the page
cprint("\n🖱️ [bold cyan]Let's get interactive: Passing JavaScript code to click 'Load More' button![/bold cyan]", True)
cprint("In this example we try to click the 'Load More' button on the page using JavaScript code.")
js_code = """
const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
loadMoreButton && loadMoreButton.click();
"""
crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
result = crawler.run(
url="https://www.nbcnews.com/business",
)
cprint("[LOG] 📦 [bold yellow]JavaScript Code (Load More button) result:[/bold yellow]")
print_result(result)
def multiple_scrip(crawler):
# Passing JavaScript code to interact with the page
cprint("\n🖱️ [bold cyan]Let's get interactive: Passing JavaScript code to click 'Load More' button![/bold cyan]", True)
cprint("In this example we try to click the 'Load More' button on the page using JavaScript code.")
js_code = ["""
const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
loadMoreButton && loadMoreButton.click();
"""] * 2
crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
result = crawler.run(
url="https://www.nbcnews.com/business",
)
cprint("[LOG] 📦 [bold yellow]JavaScript Code (Load More button) result:[/bold yellow]")
print_result(result)
def main():
cprint("🌟 [bold green]Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling fun! 🌐[/bold green]")
cprint("⛳️ [bold cyan]First Step: Create an instance of WebCrawler and call the `warmup()` function.[/bold cyan]")
cprint("If this is the first time you're running Crawl4ai, this might take a few seconds to load required model files.")
crawler = create_crawler()
basic_usage(crawler)
understanding_parameters(crawler)
crawler.always_by_pass_cache = True
screenshot_usage(crawler)
add_chunking_strategy(crawler)
add_extraction_strategy(crawler)
add_llm_extraction_strategy(crawler)
targeted_extraction(crawler)
interactive_extraction(crawler)
multiple_scrip(crawler)
cprint("\n🎉 [bold green]Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl the web like a pro! 🕸️[/bold green]")
if __name__ == "__main__":
main()