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This commit is contained in:
unclecode
2024-09-24 20:52:08 +08:00
parent d628bc4034
commit 4d48bd31ca
61 changed files with 6219 additions and 891 deletions
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317
docs/examples/quickstart_async.py
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@@ -1,4 +1,11 @@
import os, sys
# append parent directory to system path
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import asyncio
import nest_asyncio
nest_asyncio.apply()
import time
import json
import os
@@ -6,13 +13,17 @@ import re
from bs4 import BeautifulSoup
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy
from crawl4ai.extraction_strategy import (
JsonCssExtractionStrategy,
LLMExtractionStrategy,
)
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:
@@ -21,19 +32,32 @@ async def simple_crawl():
async def js_and_css():
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();"]
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,
# js_code=js_code,
css_selector="article.tease-card",
bypass_cache=True
# wait_for=wait_for,
bypass_cache=True,
)
print(result.extracted_content[: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.")
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(
@@ -45,42 +69,88 @@ async def use_proxy():
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.")
output_fee: str = Field(
..., description="Fee for output token for the OpenAI model."
)
async def extract_openai_fees():
async def extract_structured_data_using_llm():
print("\n--- Extracting Structured Data with OpenAI ---")
print("Note: Set your OpenAI API key as an environment variable to run this example.")
if not os.getenv('OPENAI_API_KEY'):
print(
"Note: Set your OpenAI API key as an environment variable to run this example."
)
if not os.getenv("OPENAI_API_KEY"):
print("OpenAI API key not found. Skipping this example.")
return
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url='https://openai.com/api/pricing/',
url="https://openai.com/api/pricing/",
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'),
provider="openai/gpt-4o",
api_token=os.getenv("OPENAI_API_KEY"),
schema=OpenAIModelFee.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"}."""
),
{"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
),
bypass_cache=True,
)
print(result.extracted_content)
async def crawl_typescript_commits():
async def extract_structured_data_using_css_extractor():
print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
schema = {
"name": "Coinbase Crypto Prices",
"baseSelector": ".cds-tableRow-t45thuk",
"fields": [
{
"name": "crypto",
"selector": "td:nth-child(1) h2",
"type": "text",
},
{
"name": "symbol",
"selector": "td:nth-child(1) p",
"type": "text",
},
{
"name": "price",
"selector": "td:nth-child(2)",
"type": "text",
}
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://www.coinbase.com/explore",
extraction_strategy=extraction_strategy,
bypass_cache=True,
)
assert result.success, "Failed to crawl the page"
news_teasers = json.loads(result.extracted_content)
print(f"Successfully extracted {len(news_teasers)} news teasers")
print(json.dumps(news_teasers[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
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)
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
@@ -89,7 +159,7 @@ async def crawl_typescript_commits():
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)
crawler.crawler_strategy.set_hook("on_execution_started", on_execution_started)
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
@@ -107,12 +177,13 @@ async def crawl_typescript_commits():
css_selector="li.Box-sc-g0xbh4-0",
js=js_next_page if page > 0 else None,
bypass_cache=True,
js_only=page > 0
js_only=page > 0,
headless=False,
)
assert result.success, f"Failed to crawl page {page + 1}"
soup = BeautifulSoup(result.cleaned_html, 'html.parser')
soup = BeautifulSoup(result.cleaned_html, "html.parser")
commits = soup.select("li")
all_commits.extend(commits)
@@ -121,64 +192,133 @@ async def crawl_typescript_commits():
await crawler.crawler_strategy.kill_session(session_id)
print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
async def extract_news_teasers():
print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
schema = {
"name": "News Teaser Extractor",
"baseSelector": ".wide-tease-item__wrapper",
"fields": [
{
"name": "category",
"selector": ".unibrow span[data-testid='unibrow-text']",
"type": "text",
},
{
"name": "headline",
"selector": ".wide-tease-item__headline",
"type": "text",
},
{
"name": "summary",
"selector": ".wide-tease-item__description",
"type": "text",
},
{
"name": "time",
"selector": "[data-testid='wide-tease-date']",
"type": "text",
},
{
"name": "image",
"type": "nested",
"selector": "picture.teasePicture img",
"fields": [
{"name": "src", "type": "attribute", "attribute": "src"},
{"name": "alt", "type": "attribute", "attribute": "alt"},
],
},
{
"name": "link",
"selector": "a[href]",
"type": "attribute",
"attribute": "href",
},
],
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
async def crawl_dynamic_content_pages_method_2():
print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
extraction_strategy=extraction_strategy,
bypass_cache=True,
)
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
all_commits = []
last_commit = ""
assert result.success, "Failed to crawl the page"
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;
};
news_teasers = json.loads(result.extracted_content)
print(f"Successfully extracted {len(news_teasers)} news teasers")
print(json.dumps(news_teasers[0], indent=2))
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,
bypass_cache=True,
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.lastCommit = 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.lastCommit;
}"""
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,
bypass_cache=True,
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 speed_comparison():
print("\n--- Speed Comparison ---")
@@ -194,8 +334,8 @@ async def speed_comparison():
result = await crawler.arun(
url="https://www.nbcnews.com/business",
word_count_threshold=0,
bypass_cache=True,
verbose=False
bypass_cache=True,
verbose=False,
)
end = time.time()
print("Crawl4AI (simple crawl):")
@@ -208,10 +348,12 @@ async def speed_comparison():
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();"],
js_code=[
"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
],
word_count_threshold=0,
bypass_cache=True,
verbose=False
bypass_cache=True,
verbose=False,
)
end = time.time()
print("Crawl4AI (with JavaScript execution):")
@@ -233,10 +375,13 @@ async def main():
await simple_crawl()
await js_and_css()
await use_proxy()
await extract_openai_fees()
await crawl_typescript_commits()
await extract_news_teasers()
await extract_structured_data_using_llm()
await extract_structured_data_using_css_extractor()
await crawl_dynamic_content_pages_method_1()
await crawl_dynamic_content_pages_method_2()
await crawl_dynamic_content_pages_method_3()
await speed_comparison()
if __name__ == "__main__":
asyncio.run(main())
asyncio.run(main())