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refactor(docs): update import statement in quickstart.py for improved clarity

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UncleCode
2025-04-05 23:12:06 +08:00
parent 49d904ca0a
commit e1d9e2489c
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docs/examples/quickstart.py
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import os, sys
from crawl4ai.types import LLMConfig
from crawl4ai import LLMConfig
sys.path.append(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

675
docs/examples/quickstart_async.py
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import os, sys
from crawl4ai import LLMConfig
# append parent directory to system path
sys.path.append(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
os.environ["FIRECRAWL_API_KEY"] = "fc-84b370ccfad44beabc686b38f1769692"
import asyncio
# import nest_asyncio
# nest_asyncio.apply()
import time
import json
import os
import re
from typing import Dict, List
from bs4 import BeautifulSoup
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
from crawl4ai.content_filter_strategy import PruningContentFilter
from crawl4ai.extraction_strategy import (
JsonCssExtractionStrategy,
LLMExtractionStrategy,
)
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
print("Crawl4AI: Advanced Web Crawling and Data Extraction")
print("GitHub Repository: https://github.com/unclecode/crawl4ai")
print("Twitter: @unclecode")
print("Website: https://crawl4ai.com")
async def simple_crawl():
print("\n--- Basic Usage ---")
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business", cache_mode=CacheMode.BYPASS
)
print(result.markdown[:500]) # Print first 500 characters
async def simple_example_with_running_js_code():
print("\n--- Executing JavaScript and Using CSS Selectors ---")
# New code to handle the wait_for parameter
wait_for = """() => {
return Array.from(document.querySelectorAll('article.tease-card')).length > 10;
}"""
# wait_for can be also just a css selector
# wait_for = "article.tease-card:nth-child(10)"
async with AsyncWebCrawler(verbose=True) as crawler:
js_code = [
"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
]
result = await crawler.arun(
url="https://www.nbcnews.com/business",
js_code=js_code,
# wait_for=wait_for,
cache_mode=CacheMode.BYPASS,
)
print(result.markdown[:500]) # Print first 500 characters
async def simple_example_with_css_selector():
print("\n--- Using CSS Selectors ---")
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
css_selector=".wide-tease-item__description",
cache_mode=CacheMode.BYPASS,
)
print(result.markdown[:500]) # Print first 500 characters
async def use_proxy():
print("\n--- Using a Proxy ---")
print(
"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", cache_mode=CacheMode.BYPASS
)
if result.success:
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:
result = await crawler.arun(
url=url, screenshot=True, cache_mode=CacheMode.BYPASS
)
if result.success and result.screenshot:
import base64
# Decode the base64 screenshot data
screenshot_data = base64.b64decode(result.screenshot)
# Save the screenshot as a JPEG file
with open(output_path, "wb") as f:
f.write(screenshot_data)
print(f"Screenshot saved successfully to {output_path}")
else:
print("Failed to capture screenshot")
class OpenAIModelFee(BaseModel):
model_name: str = Field(..., description="Name of the OpenAI model.")
input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
output_fee: str = Field(
..., description="Fee for output token for the OpenAI model."
)
async def extract_structured_data_using_llm(
provider: str, api_token: str = None, extra_headers: Dict[str, str] = None
):
print(f"\n--- Extracting Structured Data with {provider} ---")
if api_token is None and provider != "ollama":
print(f"API token is required for {provider}. Skipping this example.")
return
# extra_args = {}
extra_args = {
"temperature": 0,
"top_p": 0.9,
"max_tokens": 2000,
# any other supported parameters for litellm
}
if extra_headers:
extra_args["extra_headers"] = extra_headers
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://openai.com/api/pricing/",
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
llm_config=LLMConfig(provider=provider,api_token=api_token),
schema=OpenAIModelFee.model_json_schema(),
extraction_type="schema",
instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens.
Do not miss any models in the entire content. One extracted model JSON format should look like this:
{"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
extra_args=extra_args,
),
cache_mode=CacheMode.BYPASS,
)
print(result.extracted_content)
async def extract_structured_data_using_css_extractor():
print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
schema = {
"name": "KidoCode Courses",
"baseSelector": "section.charge-methodology .w-tab-content > div",
"fields": [
{
"name": "section_title",
"selector": "h3.heading-50",
"type": "text",
},
{
"name": "section_description",
"selector": ".charge-content",
"type": "text",
},
{
"name": "course_name",
"selector": ".text-block-93",
"type": "text",
},
{
"name": "course_description",
"selector": ".course-content-text",
"type": "text",
},
{
"name": "course_icon",
"selector": ".image-92",
"type": "attribute",
"attribute": "src",
},
],
}
async with AsyncWebCrawler(headless=True, verbose=True) as crawler:
# Create the JavaScript that handles clicking multiple times
js_click_tabs = """
(async () => {
const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");
for(let tab of tabs) {
// scroll to the tab
tab.scrollIntoView();
tab.click();
// Wait for content to load and animations to complete
await new Promise(r => setTimeout(r, 500));
}
})();
"""
result = await crawler.arun(
url="https://www.kidocode.com/degrees/technology",
extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True),
js_code=[js_click_tabs],
cache_mode=CacheMode.BYPASS,
)
companies = json.loads(result.extracted_content)
print(f"Successfully extracted {len(companies)} companies")
print(json.dumps(companies[0], indent=2))
# Advanced Session-Based Crawling with Dynamic Content 🔄
async def crawl_dynamic_content_pages_method_1():
print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")
first_commit = ""
async def on_execution_started(page):
nonlocal first_commit
try:
while True:
await page.wait_for_selector("li.Box-sc-g0xbh4-0 h4")
commit = await page.query_selector("li.Box-sc-g0xbh4-0 h4")
commit = await commit.evaluate("(element) => element.textContent")
commit = re.sub(r"\s+", "", commit)
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}")
async with AsyncWebCrawler(verbose=True) as crawler:
crawler.crawler_strategy.set_hook("on_execution_started", on_execution_started)
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
all_commits = []
js_next_page = """
(() => {
const button = document.querySelector('a[data-testid="pagination-next-button"]');
if (button) button.click();
})();
"""
for page in range(3): # Crawl 3 pages
result = await crawler.arun(
url=url,
session_id=session_id,
css_selector="li.Box-sc-g0xbh4-0",
js=js_next_page if page > 0 else None,
cache_mode=CacheMode.BYPASS,
js_only=page > 0,
headless=False,
)
assert result.success, f"Failed to crawl page {page + 1}"
soup = BeautifulSoup(result.cleaned_html, "html.parser")
commits = soup.select("li")
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")
async def crawl_dynamic_content_pages_method_2():
print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")
async with AsyncWebCrawler(verbose=True) as crawler:
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
all_commits = []
last_commit = ""
js_next_page_and_wait = """
(async () => {
const getCurrentCommit = () => {
const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
return commits.length > 0 ? commits[0].textContent.trim() : null;
};
const initialCommit = getCurrentCommit();
const button = document.querySelector('a[data-testid="pagination-next-button"]');
if (button) button.click();
// Poll for changes
while (true) {
await new Promise(resolve => setTimeout(resolve, 100)); // Wait 100ms
const newCommit = getCurrentCommit();
if (newCommit && newCommit !== initialCommit) {
break;
}
}
})();
"""
schema = {
"name": "Commit Extractor",
"baseSelector": "li.Box-sc-g0xbh4-0",
"fields": [
{
"name": "title",
"selector": "h4.markdown-title",
"type": "text",
"transform": "strip",
},
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
for page in range(3): # Crawl 3 pages
result = await crawler.arun(
url=url,
session_id=session_id,
css_selector="li.Box-sc-g0xbh4-0",
extraction_strategy=extraction_strategy,
js_code=js_next_page_and_wait if page > 0 else None,
js_only=page > 0,
cache_mode=CacheMode.BYPASS,
headless=False,
)
assert result.success, f"Failed to crawl page {page + 1}"
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")
async def crawl_dynamic_content_pages_method_3():
print(
"\n--- Advanced Multi-Page Crawling with JavaScript Execution using `wait_for` ---"
)
async with AsyncWebCrawler(verbose=True) as crawler:
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
all_commits = []
js_next_page = """
const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
if (commits.length > 0) {
window.firstCommit = commits[0].textContent.trim();
}
const button = document.querySelector('a[data-testid="pagination-next-button"]');
if (button) button.click();
"""
wait_for = """() => {
const commits = document.querySelectorAll('li.Box-sc-g0xbh4-0 h4');
if (commits.length === 0) return false;
const firstCommit = commits[0].textContent.trim();
return firstCommit !== window.firstCommit;
}"""
schema = {
"name": "Commit Extractor",
"baseSelector": "li.Box-sc-g0xbh4-0",
"fields": [
{
"name": "title",
"selector": "h4.markdown-title",
"type": "text",
"transform": "strip",
},
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
for page in range(3): # Crawl 3 pages
result = await crawler.arun(
url=url,
session_id=session_id,
css_selector="li.Box-sc-g0xbh4-0",
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,
headless=False,
)
assert result.success, f"Failed to crawl page {page + 1}"
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")
async def crawl_custom_browser_type():
# Use Firefox
start = time.time()
async with AsyncWebCrawler(
browser_type="firefox", verbose=True, headless=True
) as crawler:
result = await crawler.arun(
url="https://www.example.com", cache_mode=CacheMode.BYPASS
)
print(result.markdown[:500])
print("Time taken: ", time.time() - start)
# Use WebKit
start = time.time()
async with AsyncWebCrawler(
browser_type="webkit", verbose=True, headless=True
) as crawler:
result = await crawler.arun(
url="https://www.example.com", cache_mode=CacheMode.BYPASS
)
print(result.markdown[:500])
print("Time taken: ", time.time() - start)
# Use Chromium (default)
start = time.time()
async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
result = await crawler.arun(
url="https://www.example.com", cache_mode=CacheMode.BYPASS
)
print(result.markdown[:500])
print("Time taken: ", time.time() - start)
async def crawl_with_user_simultion():
async with AsyncWebCrawler(verbose=True, headless=True) as crawler:
url = "YOUR-URL-HERE"
result = await crawler.arun(
url=url,
cache_mode=CacheMode.BYPASS,
magic=True, # Automatically detects and removes overlays, popups, and other elements that block content
# simulate_user = True,# Causes a series of random mouse movements and clicks to simulate user interaction
# override_navigator = True # Overrides the navigator object to make it look like a real user
)
print(result.markdown)
async def speed_comparison():
# print("\n--- Speed Comparison ---")
# print("Firecrawl (simulated):")
# print("Time taken: 7.02 seconds")
# print("Content length: 42074 characters")
# print("Images found: 49")
# print()
# Simulated Firecrawl performance
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key=os.environ["FIRECRAWL_API_KEY"])
start = time.time()
scrape_status = app.scrape_url(
"https://www.nbcnews.com/business", params={"formats": ["markdown", "html"]}
)
end = time.time()
print("Firecrawl:")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(scrape_status['markdown'])} characters")
print(f"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}")
print()
async with AsyncWebCrawler() as crawler:
# Crawl4AI simple crawl
start = time.time()
result = await crawler.arun(
url="https://www.nbcnews.com/business",
word_count_threshold=0,
cache_mode=CacheMode.BYPASS,
verbose=False,
)
end = time.time()
print("Crawl4AI (simple crawl):")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(result.markdown)} characters")
print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
print()
# Crawl4AI with advanced content filtering
start = time.time()
result = await crawler.arun(
url="https://www.nbcnews.com/business",
word_count_threshold=0,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48, threshold_type="fixed", min_word_threshold=0
)
# content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
),
cache_mode=CacheMode.BYPASS,
verbose=False,
)
end = time.time()
print("Crawl4AI (Markdown Plus):")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(result.markdown.raw_markdown)} characters")
print(f"Fit Markdown: {len(result.markdown.fit_markdown)} characters")
print(f"Images found: {result.markdown.raw_markdown.count('cldnry.s-nbcnews.com')}")
print()
# Crawl4AI with JavaScript execution
start = time.time()
result = await crawler.arun(
url="https://www.nbcnews.com/business",
js_code=[
"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
],
word_count_threshold=0,
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48, threshold_type="fixed", min_word_threshold=0
)
# content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
),
verbose=False,
)
end = time.time()
print("Crawl4AI (with JavaScript execution):")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(result.markdown.raw_markdown)} characters")
print(f"Fit Markdown: {len(result.markdown.fit_markdown)} characters")
print(f"Images found: {result.markdown.raw_markdown.count('cldnry.s-nbcnews.com')}")
print("\nNote on Speed Comparison:")
print("The speed test conducted here may not reflect optimal conditions.")
print("When we call Firecrawl's API, we're seeing its best performance,")
print("while Crawl4AI's performance is limited by the local network speed.")
print("For a more accurate comparison, it's recommended to run these tests")
print("on servers with a stable and fast internet connection.")
print("Despite these limitations, Crawl4AI still demonstrates faster performance.")
print("If you run these tests in an environment with better network conditions,")
print("you may observe an even more significant speed advantage for Crawl4AI.")
async def generate_knowledge_graph():
class Entity(BaseModel):
name: str
description: str
class Relationship(BaseModel):
entity1: Entity
entity2: Entity
description: str
relation_type: str
class KnowledgeGraph(BaseModel):
entities: List[Entity]
relationships: List[Relationship]
extraction_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY")), # In case of Ollama just pass "no-token"
schema=KnowledgeGraph.model_json_schema(),
extraction_type="schema",
instruction="""Extract entities and relationships from the given text.""",
)
async with AsyncWebCrawler() as crawler:
url = "https://paulgraham.com/love.html"
result = await crawler.arun(
url=url,
cache_mode=CacheMode.BYPASS,
extraction_strategy=extraction_strategy,
# magic=True
)
# print(result.extracted_content)
with open(os.path.join(__location__, "kb.json"), "w") as f:
f.write(result.extracted_content)
async def fit_markdown_remove_overlay():
async with AsyncWebCrawler(
headless=True, # Set to False to see what is happening
verbose=True,
user_agent_mode="random",
user_agent_generator_config={"device_type": "mobile", "os_type": "android"},
) as crawler:
result = await crawler.arun(
url="https://www.kidocode.com/degrees/technology",
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48, threshold_type="fixed", min_word_threshold=0
),
options={"ignore_links": True},
),
# markdown_generator=DefaultMarkdownGenerator(
# content_filter=BM25ContentFilter(user_query="", bm25_threshold=1.0),
# options={
# "ignore_links": True
# }
# ),
)
if result.success:
print(len(result.markdown.raw_markdown))
print(len(result.markdown.markdown_with_citations))
print(len(result.markdown.fit_markdown))
# Save clean html
with open(os.path.join(__location__, "output/cleaned_html.html"), "w") as f:
f.write(result.cleaned_html)
with open(
os.path.join(__location__, "output/output_raw_markdown.md"), "w"
) as f:
f.write(result.markdown.raw_markdown)
with open(
os.path.join(__location__, "output/output_markdown_with_citations.md"),
"w",
) as f:
f.write(result.markdown.markdown_with_citations)
with open(
os.path.join(__location__, "output/output_fit_markdown.md"), "w"
) as f:
f.write(result.markdown.fit_markdown)
print("Done")
async def main():
# await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
# await simple_crawl()
# await simple_example_with_running_js_code()
# await simple_example_with_css_selector()
# # await use_proxy()
# await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
# await extract_structured_data_using_css_extractor()
# LLM extraction examples
# await extract_structured_data_using_llm()
# await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
# await extract_structured_data_using_llm("ollama/llama3.2")
# You always can pass custom headers to the extraction strategy
# custom_headers = {
# "Authorization": "Bearer your-custom-token",
# "X-Custom-Header": "Some-Value"
# }
# await extract_structured_data_using_llm(extra_headers=custom_headers)
# await crawl_dynamic_content_pages_method_1()
# await crawl_dynamic_content_pages_method_2()
await crawl_dynamic_content_pages_method_3()
# await crawl_custom_browser_type()
# await speed_comparison()
if __name__ == "__main__":
asyncio.run(main())

0
docs/examples/quickstart_examples.py → docs/examples/quickstart_examples_set_1.py
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2
docs/examples/quickstart_async.config.py → docs/examples/quickstart_examples_set_2.py
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@@ -1,6 +1,6 @@
import os, sys
from crawl4ai import LLMConfig
from crawl4ai.types import LLMConfig
sys.path.append(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

405
docs/examples/quickstart_sync.py
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@@ -1,405 +0,0 @@
import os
import time
from crawl4ai import LLMConfig
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("\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", only_text=True)
cprint("[LOG] 📦 [bold yellow]Basic crawl result:[/bold yellow]")
print_result(result)
def basic_usage_some_params(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", word_count_threshold=1, only_text=True
)
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("1️⃣ 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("2️⃣ 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(
llm_config = LLMConfig(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(
llm_config=LLMConfig(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(
llm_config=LLMConfig(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", js=js_code)
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", js=js_code)
cprint(
"[LOG] 📦 [bold yellow]JavaScript Code (Load More button) result:[/bold yellow]"
)
print_result(result)
def using_crawler_hooks(crawler):
# Example usage of the hooks for authentication and setting a cookie
def on_driver_created(driver):
print("[HOOK] on_driver_created")
# Example customization: maximize the window
driver.maximize_window()
# Example customization: logging in to a hypothetical website
driver.get("https://example.com/login")
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.NAME, "username"))
)
driver.find_element(By.NAME, "username").send_keys("testuser")
driver.find_element(By.NAME, "password").send_keys("password123")
driver.find_element(By.NAME, "login").click()
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.ID, "welcome"))
)
# Add a custom cookie
driver.add_cookie({"name": "test_cookie", "value": "cookie_value"})
return driver
def before_get_url(driver):
print("[HOOK] before_get_url")
# Example customization: add a custom header
# Enable Network domain for sending headers
driver.execute_cdp_cmd("Network.enable", {})
# Add a custom header
driver.execute_cdp_cmd(
"Network.setExtraHTTPHeaders", {"headers": {"X-Test-Header": "test"}}
)
return driver
def after_get_url(driver):
print("[HOOK] after_get_url")
# Example customization: log the URL
print(driver.current_url)
return driver
def before_return_html(driver, html):
print("[HOOK] before_return_html")
# Example customization: log the HTML
print(len(html))
return driver
cprint(
"\n🔗 [bold cyan]Using Crawler Hooks: Let's see how we can customize the crawler using hooks![/bold cyan]",
True,
)
crawler_strategy = LocalSeleniumCrawlerStrategy(verbose=True)
crawler_strategy.set_hook("on_driver_created", on_driver_created)
crawler_strategy.set_hook("before_get_url", before_get_url)
crawler_strategy.set_hook("after_get_url", after_get_url)
crawler_strategy.set_hook("before_return_html", before_return_html)
crawler = WebCrawler(verbose=True, crawler_strategy=crawler_strategy)
crawler.warmup()
result = crawler.run(url="https://example.com")
cprint("[LOG] 📦 [bold yellow]Crawler Hooks result:[/bold yellow]")
print_result(result=result)
def using_crawler_hooks_dleay_example(crawler):
def delay(driver):
print("Delaying for 5 seconds...")
time.sleep(5)
print("Resuming...")
def create_crawler():
crawler_strategy = LocalSeleniumCrawlerStrategy(verbose=True)
crawler_strategy.set_hook("after_get_url", delay)
crawler = WebCrawler(verbose=True, crawler_strategy=crawler_strategy)
crawler.warmup()
return crawler
cprint(
"\n🔗 [bold cyan]Using Crawler Hooks: Let's add a delay after fetching the url to make sure entire page is fetched.[/bold cyan]"
)
crawler = create_crawler()
result = crawler.run(url="https://google.com", bypass_cache=True)
cprint("[LOG] 📦 [bold yellow]Crawler Hooks 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()
crawler.always_by_pass_cache = True
basic_usage(crawler)
# basic_usage_some_params(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()