docs/examples/quickstart_async.config.py

import os, sys

sys.path.append(
    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)

import asyncio
import time
import json
import re
from typing import Dict, List
from bs4 import BeautifulSoup
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, CacheMode, BrowserConfig, CrawlerRunConfig
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
from crawl4ai.content_filter_strategy import BM25ContentFilter, 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")


# Basic Example - Simple Crawl
async def simple_crawl():
    print("\n--- Basic Usage ---")
    browser_config = BrowserConfig(headless=True)
    crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business", config=crawler_config
        )
        print(result.markdown[:500])


async def clean_content():
    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        excluded_tags=["nav", "footer", "aside"],
        remove_overlay_elements=True,
        markdown_generator=DefaultMarkdownGenerator(
            content_filter=PruningContentFilter(
                threshold=0.48, threshold_type="fixed", min_word_threshold=0
            ),
            options={"ignore_links": True},
        ),
    )
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://en.wikipedia.org/wiki/Apple",
            config=crawler_config,
        )
        full_markdown_length = len(result.markdown_v2.raw_markdown)
        fit_markdown_length = len(result.markdown_v2.fit_markdown)
        print(f"Full Markdown Length: {full_markdown_length}")
        print(f"Fit Markdown Length: {fit_markdown_length}")

async def link_analysis():
    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.ENABLED,
        exclude_external_links=True,
        exclude_social_media_links=True,
    )
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business",
            config=crawler_config,
        )
        print(f"Found {len(result.links['internal'])} internal links")
        print(f"Found {len(result.links['external'])} external links")

        for link in result.links['internal'][:5]:
            print(f"Href: {link['href']}\nText: {link['text']}\n")

# JavaScript Execution Example
async def simple_example_with_running_js_code():
    print("\n--- Executing JavaScript and Using CSS Selectors ---")

    browser_config = BrowserConfig(headless=True, java_script_enabled=True)

    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        js_code="const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();",
        # wait_for="() => { return Array.from(document.querySelectorAll('article.tease-card')).length > 10; }"
    )

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business", config=crawler_config
        )
        print(result.markdown[:500])


# CSS Selector Example
async def simple_example_with_css_selector():
    print("\n--- Using CSS Selectors ---")
    browser_config = BrowserConfig(headless=True)
    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS, css_selector=".wide-tease-item__description"
    )

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business", config=crawler_config
        )
        print(result.markdown[:500])

async def media_handling():
    crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, exclude_external_images=True, screenshot=True)
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business",
            config=crawler_config
        )
        for img in result.media['images'][:5]:
            print(f"Image URL: {img['src']}, Alt: {img['alt']}, Score: {img['score']}")

async def custom_hook_workflow(verbose=True):
    async with AsyncWebCrawler() as crawler:
        # Set a 'before_goto' hook to run custom code just before navigation
        crawler.crawler_strategy.set_hook("before_goto", lambda page, context: print("[Hook] Preparing to navigate..."))

        # Perform the crawl operation
        result = await crawler.arun(
            url="https://crawl4ai.com"
        )
        print(result.markdown_v2.raw_markdown[:500].replace("\n", " -- "))


# Proxy Example
async def use_proxy():
    print("\n--- Using a Proxy ---")
    browser_config = BrowserConfig(
        headless=True,
        proxy_config={
            "server": "http://proxy.example.com:8080",
            "username": "username",
            "password": "password",
        },
    )
    crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business", config=crawler_config
        )
        if result.success:
            print(result.markdown[:500])


# Screenshot Example
async def capture_and_save_screenshot(url: str, output_path: str):
    browser_config = BrowserConfig(headless=True)
    crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, screenshot=True)

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(url=url, config=crawler_config)

        if result.success and result.screenshot:
            import base64

            screenshot_data = base64.b64decode(result.screenshot)
            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")


# LLM Extraction Example
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

    browser_config = BrowserConfig(headless=True)

    extra_args = {"temperature": 0, "top_p": 0.9, "max_tokens": 2000}
    if extra_headers:
        extra_args["extra_headers"] = extra_headers

    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        word_count_threshold=1,
        page_timeout=80000,
        extraction_strategy=LLMExtractionStrategy(
            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.""",
            extra_args=extra_args,
        ),
    )

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://openai.com/api/pricing/", config=crawler_config
        )
        print(result.extracted_content)


# CSS Extraction Example
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",
            },
        ],
    }

    browser_config = BrowserConfig(headless=True, java_script_enabled=True)

    js_click_tabs = """
    (async () => {
        const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");
        for(let tab of tabs) {
            tab.scrollIntoView();
            tab.click();
            await new Promise(r => setTimeout(r, 500));
        }
    })();
    """

    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        extraction_strategy=JsonCssExtractionStrategy(schema),
        js_code=[js_click_tabs],
    )

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://www.kidocode.com/degrees/technology", config=crawler_config
        )

        companies = json.loads(result.extracted_content)
        print(f"Successfully extracted {len(companies)} companies")
        print(json.dumps(companies[0], indent=2))


# Dynamic Content Examples - Method 1
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, **kwargs):
        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}")

    browser_config = BrowserConfig(headless=False, java_script_enabled=True)

    async with AsyncWebCrawler(config=browser_config) 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):
            crawler_config = CrawlerRunConfig(
                cache_mode=CacheMode.BYPASS,
                css_selector="li.Box-sc-g0xbh4-0",
                js_code=js_next_page if page > 0 else None,
                js_only=page > 0,
                session_id=session_id,
            )

            result = await crawler.arun(url=url, config=crawler_config)
            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")

        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")


# Dynamic Content Examples - Method 2
async def crawl_dynamic_content_pages_method_2():
    print("\n--- Advanced Multi-Page Crawling with JavaScript Execution ---")

    browser_config = BrowserConfig(headless=False, java_script_enabled=True)

    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();

        while (true) {
            await new Promise(resolve => setTimeout(resolve, 100));
            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",
            },
        ],
    }

    async with AsyncWebCrawler(config=browser_config) as crawler:
        url = "https://github.com/microsoft/TypeScript/commits/main"
        session_id = "typescript_commits_session"
        all_commits = []

        extraction_strategy = JsonCssExtractionStrategy(schema)

        for page in range(3):
            crawler_config = CrawlerRunConfig(
                cache_mode=CacheMode.BYPASS,
                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,
                session_id=session_id,
            )

            result = await crawler.arun(url=url, config=crawler_config)
            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")

        print(f"Successfully crawled {len(all_commits)} commits across 3 pages")


async def cosine_similarity_extraction():
    crawl_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        extraction_strategy=CosineStrategy(
            word_count_threshold=10,
            max_dist=0.2, # Maximum distance between two words
            linkage_method="ward", # Linkage method for hierarchical clustering (ward, complete, average, single)
            top_k=3, # Number of top keywords to extract
            sim_threshold=0.3, # Similarity threshold for clustering
            semantic_filter="McDonald's economic impact, American consumer trends", # Keywords to filter the content semantically using embeddings
            verbose=True
        ),        
    )
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business/consumer/how-mcdonalds-e-coli-crisis-inflation-politics-reflect-american-story-rcna177156",
            config=crawl_config
        )
        print(json.loads(result.extracted_content)[:5])

# Browser Comparison
async def crawl_custom_browser_type():
    print("\n--- Browser Comparison ---")

    # Firefox
    browser_config_firefox = BrowserConfig(browser_type="firefox", headless=True)
    start = time.time()
    async with AsyncWebCrawler(config=browser_config_firefox) as crawler:
        result = await crawler.arun(
            url="https://www.example.com",
            config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS),
        )
        print("Firefox:", time.time() - start)
        print(result.markdown[:500])

    # WebKit
    browser_config_webkit = BrowserConfig(browser_type="webkit", headless=True)
    start = time.time()
    async with AsyncWebCrawler(config=browser_config_webkit) as crawler:
        result = await crawler.arun(
            url="https://www.example.com",
            config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS),
        )
        print("WebKit:", time.time() - start)
        print(result.markdown[:500])

    # Chromium (default)
    browser_config_chromium = BrowserConfig(browser_type="chromium", headless=True)
    start = time.time()
    async with AsyncWebCrawler(config=browser_config_chromium) as crawler:
        result = await crawler.arun(
            url="https://www.example.com",
            config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS),
        )
        print("Chromium:", time.time() - start)
        print(result.markdown[:500])


# Anti-Bot and User Simulation
async def crawl_with_user_simulation():
    browser_config = BrowserConfig(
        headless=True,
        user_agent_mode="random",
        user_agent_generator_config={"device_type": "mobile", "os_type": "android"},
    )

    crawler_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        magic=True,
        simulate_user=True,
        override_navigator=True,
    )

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(url="YOUR-URL-HERE", config=crawler_config)
        print(result.markdown)

async def ssl_certification():
    # Configure crawler to fetch SSL certificate
    config = CrawlerRunConfig(
        fetch_ssl_certificate=True,
        cache_mode=CacheMode.BYPASS  # Bypass cache to always get fresh certificates
    )

    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url='https://example.com',
            config=config
        )
        
        if result.success and result.ssl_certificate:
            cert = result.ssl_certificate
            
            # 1. Access certificate properties directly
            print("\nCertificate Information:")
            print(f"Issuer: {cert.issuer.get('CN', '')}")
            print(f"Valid until: {cert.valid_until}")
            print(f"Fingerprint: {cert.fingerprint}")
            
            # 2. Export certificate in different formats
            cert.to_json(os.path.join(tmp_dir, "certificate.json"))  # For analysis
            print("\nCertificate exported to:")
            print(f"- JSON: {os.path.join(tmp_dir, 'certificate.json')}")
            
            pem_data = cert.to_pem(os.path.join(tmp_dir, "certificate.pem"))  # For web servers
            print(f"- PEM: {os.path.join(tmp_dir, 'certificate.pem')}")
            
            der_data = cert.to_der(os.path.join(tmp_dir, "certificate.der"))  # For Java apps
            print(f"- DER: {os.path.join(tmp_dir, 'certificate.der')}")

# Speed Comparison
async def speed_comparison():
    print("\n--- Speed Comparison ---")

    # Firecrawl comparison
    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()

    # Crawl4AI comparisons
    browser_config = BrowserConfig(headless=True)

    # Simple crawl
    async with AsyncWebCrawler(config=browser_config) as crawler:
        start = time.time()
        result = await crawler.arun(
            url="https://www.nbcnews.com/business",
            config=CrawlerRunConfig(
                cache_mode=CacheMode.BYPASS, word_count_threshold=0
            ),
        )
        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()

        # Advanced filtering
        start = time.time()
        result = await crawler.arun(
            url="https://www.nbcnews.com/business",
            config=CrawlerRunConfig(
                cache_mode=CacheMode.BYPASS,
                word_count_threshold=0,
                markdown_generator=DefaultMarkdownGenerator(
                    content_filter=PruningContentFilter(
                        threshold=0.48, threshold_type="fixed", min_word_threshold=0
                    )
                ),
            ),
        )
        end = time.time()
        print("Crawl4AI (Markdown Plus):")
        print(f"Time taken: {end - start:.2f} seconds")
        print(f"Content length: {len(result.markdown_v2.raw_markdown)} characters")
        print(f"Fit Markdown: {len(result.markdown_v2.fit_markdown)} characters")
        print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
        print()


# Main execution
async def main():
    # Basic examples
    # await simple_crawl()
    # await simple_example_with_running_js_code()
    # await simple_example_with_css_selector()

    # Advanced examples
    # await extract_structured_data_using_css_extractor()
    await extract_structured_data_using_llm(
        "openai/gpt-4o", os.getenv("OPENAI_API_KEY")
    )
    # await crawl_dynamic_content_pages_method_1()
    # await crawl_dynamic_content_pages_method_2()

    # Browser comparisons
    # await crawl_custom_browser_type()

    # Performance testing
    # await speed_comparison()

    # Screenshot example
    # await capture_and_save_screenshot(
    #     "https://www.example.com",
    #     os.path.join(__location__, "tmp/example_screenshot.jpg")
    # )


if __name__ == "__main__":
    asyncio.run(main())