Push async version last changes for merge to main branch

CONTRIBUTORS.md

@@ -6,12 +6,14 @@ We would like to thank the following people for their contributions to Crawl4AI:
 
 - [Unclecode](https://github.com/unclecode) - Project Creator and Main Developer
 - [Nasrin](https://github.com/ntohidi) - Project Manager and Developer
+- [Aravind Karnam](https://github.com/aravindkarnam) - Developer
 
 ## Community Contributors
 
-- [Aravind Karnam](https://github.com/aravindkarnam) - Developed textual description extraction feature
 - [FractalMind](https://github.com/FractalMind) - Created the first official Docker Hub image and fixed Dockerfile errors
 - [ketonkss4](https://github.com/ketonkss4) - Identified Selenium's new capabilities, helping reduce dependencies
+- [jonymusky](https://github.com/jonymusky) - Javascript execution documentation, and wait_for
+- [datehoer](https://github.com/datehoer) - Add browser prxy support
 
 ## Other Contributors
 
@@ -19,7 +21,6 @@ We would like to thank the following people for their contributions to Crawl4AI:
 - [Shiv Kumar](https://github.com/shivkumar0757)
 - [QIN2DIM](https://github.com/QIN2DIM)
 
-
 ## Acknowledgements
 
 We also want to thank all the users who have reported bugs, suggested features, or helped in any other way to make Crawl4AI better.

README.md

@@ -1,4 +1,4 @@
-# Crawl4AI Async Version 🕷️🤖
+# Crawl4AI 0.3.0 Async Version 🕷️🤖
 
 [![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
 [![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
@@ -43,18 +43,78 @@ Crawl4AI simplifies asynchronous web crawling and data extraction, making it acc
 
 ## Installation 🛠️
 
+Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
+
 ### Using pip 🐍
+
+Choose the installation option that best fits your needs:
+
+#### Basic Installation
+
+For basic web crawling and scraping tasks:
+
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai
+```
+
+#### Installation with PyTorch
+
+For advanced text clustering (includes CosineSimilarity cluster strategy):
+
+```bash
+pip install crawl4ai[torch]
+```
+
+#### Installation with Transformers
+
+For text summarization and Hugging Face models:
+
+```bash
+pip install crawl4ai[transformer]
+```
+
+#### Installation with Synchronous Version
+
+If you need the synchronous version using Selenium:
+
+```bash
+pip install crawl4ai[sync]
+```
+
+#### Installation with Cosine Similarity
+
+For using the cosine similarity strategy:
+
+```bash
+pip install crawl4ai[cosine]
+```
+
+#### Full Installation
+
+For all features:
+
+```bash
+pip install crawl4ai[all]
+```
+
+After installation, run the following command to install Playwright dependencies:
+
+```bash
+playwright install
+```
+
+If you've installed the "torch", "transformer", or "all" options, it's recommended to run:
+
+```bash
+crawl4ai-download-models
 ```
 
 ### Using Docker 🐳
 
 ```bash
 # For Mac users (M1/M2)
-# docker build --platform linux/amd64 -t crawl4ai .
+docker build --platform linux/amd64 -t crawl4ai .
+# For other users
 docker build -t crawl4ai .
 docker run -d -p 8000:80 crawl4ai
 ```
@@ -66,6 +126,8 @@ docker pull unclecode/crawl4ai:latest
 docker run -d -p 8000:80 unclecode/crawl4ai:latest
 ```
 
+For more detailed installation instructions and options, please refer to our [Installation Guide](https://crawl4ai.com/mkdocs/installation).
+
 ## Quick Start 🚀
 
 ```python

crawl4ai/__init__.py

@@ -2,7 +2,7 @@ from .web_crawler import WebCrawler
 from .async_webcrawler import AsyncWebCrawler
 from .models import CrawlResult
 
-__version__ = "0.2.77"
+__version__ = "0.3.0"
 
 __all__ = [
     "WebCrawler",

crawl4ai/async_crawler_strategy.py

@@ -52,6 +52,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         self.use_cached_html = use_cached_html
         self.user_agent = kwargs.get("user_agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")
         self.proxy = kwargs.get("proxy")
+        self.headless = kwargs.get("headless", True)
         self.headers = {}
         self.sessions = {}
         self.session_ttl = 1800 
@@ -80,7 +81,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             self.playwright = await async_playwright().start()
         if self.browser is None:
             browser_args = {
-                "headless": True,
+                "headless": self.headless,
                 # "headless": False,
                 "args": [
                     "--disable-gpu",
@@ -146,6 +147,31 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         for sid in expired_sessions:
             asyncio.create_task(self.kill_session(sid))
             
+            
+    async def csp_compliant_wait(self, page: Page, user_wait_function: str, timeout: float = 30000):
+        wrapper_js = f"""
+        async () => {{
+            const userFunction = {user_wait_function};
+            const startTime = Date.now();
+            while (true) {{
+                if (await userFunction()) {{
+                    return true;
+                }}
+                if (Date.now() - startTime > {timeout}) {{
+                    throw new Error('Timeout waiting for condition');
+                }}
+                await new Promise(resolve => setTimeout(resolve, 100));
+            }}
+        }}
+        """
+        
+        try:
+            await page.evaluate(wrapper_js)
+        except TimeoutError:
+            raise TimeoutError(f"Timeout after {timeout}ms waiting for condition")
+        except Exception as e:
+            raise RuntimeError(f"Error in wait condition: {str(e)}")
+
     async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
         response_headers = {}
         status_code = None
@@ -196,6 +222,9 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 # Get status code and headers
                 status_code = response.status
                 response_headers = response.headers
+            else:
+                status_code = 200
+                response_headers = {}
 
             await page.wait_for_selector('body')
             await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
@@ -203,7 +232,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
             js_code = kwargs.get("js_code", kwargs.get("js", self.js_code))
             if js_code:
                 if isinstance(js_code, str):
-                    await page.evaluate(js_code)
+                    r = await page.evaluate(js_code)
                 elif isinstance(js_code, list):
                     for js in js_code:
                         await page.evaluate(js)
@@ -213,6 +242,37 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
                 # Check for on execution even
                 await self.execute_hook('on_execution_started', page)
                 
+            # New code to handle the wait_for parameter
+            # Example usage:
+            # await crawler.crawl(
+            #     url,
+            #     js_code="// some JavaScript code",
+            #     wait_for="""() => {
+            #         return document.querySelector('#my-element') !== null;
+            #     }"""
+            # )
+            # Example of using a CSS selector:
+            # await crawler.crawl(
+            #     url,
+            #     wait_for="#my-element"
+            # )
+            wait_for = kwargs.get("wait_for")
+            if wait_for:
+                try:
+                    await self.csp_compliant_wait(page, wait_for, timeout=kwargs.get("timeout", 30000))
+                except Exception as e:
+                    raise RuntimeError(f"Custom wait condition failed: {str(e)}")                
+                # try:
+                #     await page.wait_for_function(wait_for)
+                #     # if callable(wait_for):
+                #     #     await page.wait_for_function(wait_for)
+                #     # elif isinstance(wait_for, str):
+                #     #     await page.wait_for_selector(wait_for)
+                #     # else:
+                #     #     raise ValueError("wait_for must be either a callable or a CSS selector string")
+                # except Error as e:
+                #     raise Error(f"Custom wait condition failed: {str(e)}")
+
             html = await page.content()
             page = await self.execute_hook('before_return_html', page, html)
 
@@ -246,6 +306,49 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
         # except Exception as e:
         #     raise Exception(f"Failed to crawl {url}: {str(e)}")
 
+    async def execute_js(self, session_id: str, js_code: str, wait_for_js: str = None, wait_for_css: str = None) -> AsyncCrawlResponse:
+        """
+        Execute JavaScript code in a specific session and optionally wait for a condition.
+        
+        :param session_id: The ID of the session to execute the JS code in.
+        :param js_code: The JavaScript code to execute.
+        :param wait_for_js: JavaScript condition to wait for after execution.
+        :param wait_for_css: CSS selector to wait for after execution.
+        :return: AsyncCrawlResponse containing the page's HTML and other information.
+        :raises ValueError: If the session does not exist.
+        """
+        if not session_id:
+            raise ValueError("Session ID must be provided")
+        
+        if session_id not in self.sessions:
+            raise ValueError(f"No active session found for session ID: {session_id}")
+        
+        context, page, last_used = self.sessions[session_id]
+        
+        try:
+            await page.evaluate(js_code)
+            
+            if wait_for_js:
+                await page.wait_for_function(wait_for_js)
+            
+            if wait_for_css:
+                await page.wait_for_selector(wait_for_css)
+            
+            # Get the updated HTML content
+            html = await page.content()
+            
+            # Get response headers and status code (assuming these are available)
+            response_headers = await page.evaluate("() => JSON.stringify(performance.getEntriesByType('resource')[0].responseHeaders)")
+            status_code = await page.evaluate("() => performance.getEntriesByType('resource')[0].responseStatus")
+            
+            # Update the last used time for this session
+            self.sessions[session_id] = (context, page, time.time())
+            
+            return AsyncCrawlResponse(html=html, response_headers=response_headers, status_code=status_code)
+        except Error as e:
+            raise Error(f"Failed to execute JavaScript or wait for condition in session {session_id}: {str(e)}")
+    
+    
     async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
         semaphore_count = kwargs.get('semaphore_count', calculate_semaphore_count())
         semaphore = asyncio.Semaphore(semaphore_count)

crawl4ai/async_webcrawler.py

@@ -80,6 +80,7 @@ class AsyncWebCrawler:
             
             word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
 
+            async_response : AsyncCrawlResponse = None
             cached = None
             screenshot_data = None
             extracted_content = None
@@ -125,8 +126,8 @@ class AsyncWebCrawler:
                 async_response=async_response,
                 **kwargs,
             )
-            crawl_result.status_code = async_response.status_code
-            crawl_result.responser_headers = async_response.response_headers
+            crawl_result.status_code = async_response.status_code if async_response else 200
+            crawl_result.responser_headers = async_response.response_headers if async_response else {}
             crawl_result.success = bool(html)
             crawl_result.session_id = kwargs.get("session_id", None)
             return crawl_result
@@ -224,11 +225,11 @@ class AsyncWebCrawler:
             if isinstance(extraction_strategy, JsonCssExtractionStrategy) or isinstance(extraction_strategy, JsonCssExtractionStrategy):
                 extraction_strategy.verbose = verbose
                 extracted_content = extraction_strategy.run(url, [html])
-                extracted_content = json.dumps(extracted_content, indent=4, default=str)
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
             else:
                 sections = chunking_strategy.chunk(markdown)
                 extracted_content = extraction_strategy.run(url, sections)
-                extracted_content = json.dumps(extracted_content, indent=4, default=str)
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
 
         if verbose:
             print(

crawl4ai/content_scrapping_strategy.py

@@ -50,7 +50,16 @@ class WebScrappingStrategy(ContentScrappingStrategy):
         if css_selector:
             selected_elements = body.select(css_selector)
             if not selected_elements:
-                raise InvalidCSSSelectorError(f"Invalid CSS selector, No elements found for CSS selector: {css_selector}")
+                return {
+                    'markdown': '',
+                    'cleaned_html': '',
+                    'success': True,
+                    'media': {'images': [], 'videos': [], 'audios': []},
+                    'links': {'internal': [], 'external': []},
+                    'metadata': {},
+                    'message': f"No elements found for CSS selector: {css_selector}"
+                }
+                # raise InvalidCSSSelectorError(f"Invalid CSS selector, No elements found for CSS selector: {css_selector}")
             body = soup.new_tag('div')
             for el in selected_elements:
                 body.append(el)

crawl4ai/crawler_strategy.py

@@ -258,6 +258,18 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                         lambda driver: driver.execute_script("return document.readyState") == "complete"
                     )
             
+            # Optionally, wait for some condition after executing the JS code : Contributed by (https://github.com/jonymusky)
+            wait_for = kwargs.get('wait_for', False)
+            if wait_for:
+                if callable(wait_for):
+                    print("[LOG] 🔄 Waiting for condition...")
+                    WebDriverWait(self.driver, 20).until(wait_for)
+                else:
+                    print("[LOG] 🔄 Waiting for condition...")
+                    WebDriverWait(self.driver, 20).until(
+                        EC.presence_of_element_located((By.CSS_SELECTOR, wait_for))
+                    ) 
+            
             if not can_not_be_done_headless:
                 html = sanitize_input_encode(self.driver.page_source)
             self.driver = self.execute_hook('before_return_html', self.driver, html)

crawl4ai/model_loader.py

@@ -80,7 +80,6 @@ def load_bge_small_en_v1_5():
     model, device = set_model_device(model)
     return tokenizer, model
 
-
 @lru_cache()
 def load_text_classifier():
     from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -147,7 +146,6 @@ def load_nltk_punkt():
         nltk.download('punkt')
     return nltk.data.find('tokenizers/punkt')
 
-
 @lru_cache()
 def load_spacy_model():
     import spacy

crawl4ai/web_crawler.py

@@ -201,7 +201,7 @@ class WebCrawler:
 
                 sections = chunking_strategy.chunk(markdown)
                 extracted_content = extraction_strategy.run(url, sections)
-                extracted_content = json.dumps(extracted_content, indent=4, default=str)
+                extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
 
                 if verbose:
                     print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds.")

docs/examples/quickstart_async.py

@@ -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
         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 ---")
@@ -195,7 +335,7 @@ async def speed_comparison():
             url="https://www.nbcnews.com/business",
             word_count_threshold=0,
             bypass_cache=True,
-            verbose=False
+            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
+            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())

docs/examples/research_assistant.py

@@ -1,4 +1,4 @@
-# Make sur to install the required packageschainlit and groq
+# Make sure to install the required packageschainlit and groq
 import os, time
 from openai import AsyncOpenAI
 import chainlit as cl

docs/examples/sample_ecommerce.html

@@ -0,0 +1,106 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Sample E-commerce Page for JsonCssExtractionStrategy Testing</title>
+    <style>
+        body { font-family: Arial, sans-serif; line-height: 1.6; padding: 20px; }
+        .category { border: 1px solid #ddd; margin-bottom: 20px; padding: 10px; }
+        .product { border: 1px solid #eee; margin: 10px 0; padding: 10px; }
+        .product-details, .product-reviews, .related-products { margin-top: 10px; }
+        .review { background-color: #f9f9f9; margin: 5px 0; padding: 5px; }
+    </style>
+</head>
+<body>
+    <h1>Sample E-commerce Product Catalog</h1>
+    <div id="catalog"></div>
+
+    <script>
+        const categories = ['Electronics', 'Home & Kitchen', 'Books'];
+        const products = [
+            {
+                name: 'Smartphone X',
+                price: '$999',
+                brand: 'TechCorp',
+                model: 'X-2000',
+                features: ['5G capable', '6.5" OLED screen', '128GB storage'],
+                reviews: [
+                    { reviewer: 'John D.', rating: '4.5', text: 'Great phone, love the camera!' },
+                    { reviewer: 'Jane S.', rating: '5', text: 'Best smartphone I\'ve ever owned.' }
+                ],
+                related: [
+                    { name: 'Phone Case', price: '$29.99' },
+                    { name: 'Screen Protector', price: '$9.99' }
+                ]
+            },
+            {
+                name: 'Laptop Pro',
+                price: '$1499',
+                brand: 'TechMaster',
+                model: 'LT-3000',
+                features: ['Intel i7 processor', '16GB RAM', '512GB SSD'],
+                reviews: [
+                    { reviewer: 'Alice W.', rating: '4', text: 'Powerful machine, but a bit heavy.' },
+                    { reviewer: 'Bob M.', rating: '5', text: 'Perfect for my development work!' }
+                ],
+                related: [
+                    { name: 'Laptop Bag', price: '$49.99' },
+                    { name: 'Wireless Mouse', price: '$24.99' }
+                ]
+            }
+        ];
+
+        function createProductHTML(product) {
+            return `
+                <div class="product">
+                    <h3 class="product-name">${product.name}</h3>
+                    <p class="product-price">${product.price}</p>
+                    <div class="product-details">
+                        <span class="brand">${product.brand}</span>
+                        <span class="model">${product.model}</span>
+                    </div>
+                    <ul class="product-features">
+                        ${product.features.map(feature => `<li>${feature}</li>`).join('')}
+                    </ul>
+                    <div class="product-reviews">
+                        ${product.reviews.map(review => `
+                            <div class="review">
+                                <span class="reviewer">${review.reviewer}</span>
+                                <span class="rating">${review.rating}</span>
+                                <p class="review-text">${review.text}</p>
+                            </div>
+                        `).join('')}
+                    </div>
+                    <ul class="related-products">
+                        ${product.related.map(item => `
+                            <li>
+                                <span class="related-name">${item.name}</span>
+                                <span class="related-price">${item.price}</span>
+                            </li>
+                        `).join('')}
+                    </ul>
+                </div>
+            `;
+        }
+
+        function createCategoryHTML(category, products) {
+            return `
+                <div class="category">
+                    <h2 class="category-name">${category}</h2>
+                    ${products.map(createProductHTML).join('')}
+                </div>
+            `;
+        }
+
+        function populateCatalog() {
+            const catalog = document.getElementById('catalog');
+            categories.forEach(category => {
+                catalog.innerHTML += createCategoryHTML(category, products);
+            });
+        }
+
+        populateCatalog();
+    </script>
+</body>
+</html>

docs/examples/tmp/research_assistant_audio_not_completed.py

@@ -1,4 +1,4 @@
-# Make sur to install the required packageschainlit and groq
+# Make sure to install the required packageschainlit and groq
 import os, time
 from openai import AsyncOpenAI
 import chainlit as cl

docs/md _sync/api/core_classes_and_functions.md

@@ -0,0 +1,141 @@
+# Core Classes and Functions
+
+## Overview
+
+In this section, we will delve into the core classes and functions that make up the Crawl4AI library. This includes the `WebCrawler` class, various `CrawlerStrategy` classes, `ChunkingStrategy` classes, and `ExtractionStrategy` classes. Understanding these core components will help you leverage the full power of Crawl4AI for your web crawling and data extraction needs.
+
+## WebCrawler Class
+
+The `WebCrawler` class is the main class you'll interact with. It provides the interface for crawling web pages and extracting data.
+
+### Initialization
+
+```python
+from crawl4ai import WebCrawler
+
+# Create an instance of WebCrawler
+crawler = WebCrawler()
+```
+
+### Methods
+
+- **`warmup()`**: Prepares the crawler for use, such as loading necessary models.
+- **`run(url: str, **kwargs)`**: Runs the crawler on the specified URL with optional parameters for customization.
+
+```python
+crawler.warmup()
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(result)
+```
+
+## CrawlerStrategy Classes
+
+The `CrawlerStrategy` classes define how the web crawling is executed. The base class is `CrawlerStrategy`, which is extended by specific implementations like `LocalSeleniumCrawlerStrategy`.
+
+### CrawlerStrategy Base Class
+
+An abstract base class that defines the interface for different crawler strategies.
+
+```python
+from abc import ABC, abstractmethod
+
+class CrawlerStrategy(ABC):
+    @abstractmethod
+    def crawl(self, url: str, **kwargs) -> str:
+        pass
+    
+    @abstractmethod
+    def take_screenshot(self, save_path: str):
+        pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
+    
+    @abstractmethod
+    def set_hook(self, hook_type: str, hook: Callable):
+        pass
+```
+
+### LocalSeleniumCrawlerStrategy Class
+
+A concrete implementation of `CrawlerStrategy` that uses Selenium to crawl web pages.
+
+#### Initialization
+
+```python
+from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
+
+strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
+```
+
+#### Methods
+
+- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+```python
+result = strategy.crawl("https://www.example.com")
+strategy.take_screenshot("screenshot.png")
+strategy.update_user_agent("Mozilla/5.0")
+strategy.set_hook("before_get_url", lambda: print("About to get URL"))
+```
+
+## ChunkingStrategy Classes
+
+The `ChunkingStrategy` classes define how the text from a web page is divided into chunks. Here are a few examples:
+
+### RegexChunking Class
+
+Splits text using regular expressions.
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+chunker = RegexChunking(patterns=[r'\n\n'])
+chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
+```
+
+### NlpSentenceChunking Class
+
+Uses NLP to split text into sentences.
+
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+chunker = NlpSentenceChunking()
+chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
+```
+
+## ExtractionStrategy Classes
+
+The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks. Here are a few examples:
+
+### CosineStrategy Class
+
+Clusters text chunks based on cosine similarity.
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### LLMExtractionStrategy Class
+
+Uses a Language Model to extract meaningful blocks from HTML.
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+## Conclusion
+
+By understanding these core classes and functions, you can customize and extend Crawl4AI to suit your specific web crawling and data extraction needs. Happy crawling! 🕷️🤖
+

docs/md _sync/api/detailed_api_documentation.md

@@ -0,0 +1,338 @@
+# Detailed API Documentation
+
+## Overview
+
+This section provides comprehensive documentation for the Crawl4AI API, covering all classes, methods, and their parameters. This guide will help you understand how to utilize the API to its full potential, enabling efficient web crawling and data extraction.
+
+## WebCrawler Class
+
+The `WebCrawler` class is the primary interface for crawling web pages and extracting data.
+
+### Initialization
+
+```python
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+```
+
+### Methods
+
+#### `warmup()`
+
+Prepares the crawler for use, such as loading necessary models.
+
+```python
+crawler.warmup()
+```
+
+#### `run(url: str, **kwargs) -> CrawlResult`
+
+Crawls the specified URL and returns the result.
+
+- **Parameters:**
+  - `url` (str): The URL to crawl.
+  - `**kwargs`: Additional parameters for customization.
+
+- **Returns:**
+  - `CrawlResult`: An object containing the crawl result.
+
+- **Example:**
+
+```python
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(result)
+```
+
+### CrawlResult Class
+
+Represents the result of a crawl operation.
+
+- **Attributes:**
+  - `url` (str): The URL of the crawled page.
+  - `html` (str): The raw HTML of the page.
+  - `success` (bool): Whether the crawl was successful.
+  - `cleaned_html` (Optional[str]): The cleaned HTML.
+  - `media` (Dict[str, List[Dict]]): Media tags in the page (images, audio, video).
+  - `links` (Dict[str, List[Dict]]): Links in the page (external, internal).
+  - `screenshot` (Optional[str]): Base64 encoded screenshot.
+  - `markdown` (Optional[str]): Extracted content in Markdown format.
+  - `extracted_content` (Optional[str]): Extracted meaningful content.
+  - `metadata` (Optional[dict]): Metadata from the page.
+  - `error_message` (Optional[str]): Error message if any.
+
+## CrawlerStrategy Classes
+
+The `CrawlerStrategy` classes define how the web crawling is executed.
+
+### CrawlerStrategy Base Class
+
+An abstract base class for different crawler strategies.
+
+#### Methods
+
+- **`crawl(url: str, **kwargs) -> str`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+### LocalSeleniumCrawlerStrategy Class
+
+Uses Selenium to crawl web pages.
+
+#### Initialization
+
+```python
+from crawl4ai.crawler_strategy import LocalSeleniumCrawlerStrategy
+
+strategy = LocalSeleniumCrawlerStrategy(js_code=["console.log('Hello, world!');"])
+```
+
+#### Methods
+
+- **`crawl(url: str, **kwargs)`**: Crawls the specified URL.
+- **`take_screenshot(save_path: str)`**: Takes a screenshot of the current page.
+- **`update_user_agent(user_agent: str)`**: Updates the user agent for the browser.
+- **`set_hook(hook_type: str, hook: Callable)`**: Sets a hook for various events.
+
+#### Example
+
+```python
+result = strategy.crawl("https://www.example.com")
+strategy.take_screenshot("screenshot.png")
+strategy.update_user_agent("Mozilla/5.0")
+strategy.set_hook("before_get_url", lambda: print("About to get URL"))
+```
+
+## ChunkingStrategy Classes
+
+The `ChunkingStrategy` classes define how the text from a web page is divided into chunks.
+
+### RegexChunking Class
+
+Splits text using regular expressions.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+chunker = RegexChunking(patterns=[r'\n\n'])
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into chunks.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
+```
+
+### NlpSentenceChunking Class
+
+Uses NLP to split text into sentences.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+chunker = NlpSentenceChunking()
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into sentences.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
+```
+
+### TopicSegmentationChunking Class
+
+Uses the TextTiling algorithm to segment text into topics.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import TopicSegmentationChunking
+
+chunker = TopicSegmentationChunking(num_keywords=3)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into topic-based segments.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
+```
+
+### FixedLengthWordChunking Class
+
+Splits text into chunks of fixed length based on the number of words.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import FixedLengthWordChunking
+
+chunker = FixedLengthWordChunking(chunk_size=100)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text into fixed-length word chunks.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
+```
+
+### SlidingWindowChunking Class
+
+Uses a sliding window approach to chunk text.
+
+#### Initialization
+
+```python
+from crawl4ai.chunking_strategy import SlidingWindowChunking
+
+chunker = SlidingWindowChunking(window_size=100, step=50)
+```
+
+#### Methods
+
+- **`chunk(text: str) -> List[str]`**: Splits the text using a sliding window approach.
+
+#### Example
+
+```python
+chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
+```
+
+## ExtractionStrategy Classes
+
+The `ExtractionStrategy` classes define how meaningful content is extracted from the chunks.
+
+### NoExtractionStrategy Class
+
+Returns the entire HTML content without any modification.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import NoExtractionStrategy
+
+extractor = NoExtractionStrategy()
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Returns the HTML content.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### LLMExtractionStrategy Class
+
+Uses a Language Model to extract meaningful blocks from HTML.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts meaningful content using the LLM.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### CosineStrategy Class
+
+Clusters text chunks based on cosine similarity.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+extractor = CosineStrategy(semantic_filter="finance", word_count_threshold=10)
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts clusters of text based on cosine similarity.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+### TopicExtractionStrategy Class
+
+Uses the TextTiling algorithm to segment HTML content into topics and extract keywords.
+
+#### Initialization
+
+```python
+from crawl4ai.extraction_strategy import TopicExtractionStrategy
+
+extractor = TopicExtractionStrategy(num_keywords=3)
+```
+
+#### Methods
+
+- **`extract(url: str, html: str) -> str`**: Extracts topic-based segments and keywords.
+
+#### Example
+
+```python
+extracted_content = extractor.extract(url="https://www.example.com", html="<html>...</html>")
+```
+
+## Parameters
+
+Here are the common parameters used across various classes and methods:
+
+- **`url`** (str): The URL to crawl.
+- **`html`** (str): The HTML content of the page.
+- **`user_agent`** (str): The user agent for the HTTP requests.
+- **`patterns`** (list): A list of regular expression patterns for chunking.
+- **`num_keywords`** (int): Number of keywords for topic extraction.
+- **`chunk_size`** (int): Number of words in each chunk.
+- **`window_size`** (int): Number of words in the sliding window.
+- **`step`** (int): Step size for the sliding window.
+- **`semantic_filter`** (str): Keywords for filtering relevant documents.
+- **`word_count_threshold`** (int): Minimum number of words per cluster.
+- **`max_dist`** (float): Maximum cophenetic distance for clustering.
+- **`linkage_method`** (str): Linkage method for hierarchical clustering.
+- **`top_k`** (int): Number of top categories to extract.
+- **`provider`** (
+
+str): Provider for language model completions.
+- **`api_token`** (str): API token for the provider.
+- **`instruction`** (str): Instruction to guide the LLM extraction.
+
+## Conclusion
+
+This detailed API documentation provides a thorough understanding of the classes, methods, and parameters in the Crawl4AI library. With this knowledge, you can effectively use the API to perform advanced web crawling and data extraction tasks.

docs/md _sync/assets/highlight_init.js

@@ -0,0 +1,6 @@
+document.addEventListener('DOMContentLoaded', (event) => {
+    document.querySelectorAll('pre code').forEach((block) => {
+      hljs.highlightBlock(block);
+    });
+  });
+  

docs/md _sync/assets/styles.css

@@ -0,0 +1,153 @@
+@font-face {
+    font-family: "Monaco";
+    font-style: normal;
+    font-weight: normal;
+    src: local("Monaco"), url("Monaco.woff") format("woff");
+}
+
+:root {
+    --global-font-size: 16px;
+    --global-line-height: 1.5em;
+    --global-space: 10px;
+    --font-stack: Menlo, Monaco, Lucida Console, Liberation Mono, DejaVu Sans Mono, Bitstream Vera Sans Mono,
+        Courier New, monospace, serif;
+    --font-stack: dm, Monaco, Courier New, monospace, serif;
+    --mono-font-stack: Menlo, Monaco, Lucida Console, Liberation Mono, DejaVu Sans Mono, Bitstream Vera Sans Mono,
+        Courier New, monospace, serif;
+
+    --background-color: #151515; /* Dark background */
+    --font-color: #eaeaea; /* Light font color for contrast */
+    --invert-font-color: #151515; /* Dark color for inverted elements */
+    --primary-color: #1a95e0; /* Primary color can remain the same or be adjusted for better contrast */
+    --secondary-color: #727578; /* Secondary color for less important text */
+    --error-color: #ff5555; /* Bright color for errors */
+    --progress-bar-background: #444; /* Darker background for progress bar */
+    --progress-bar-fill: #1a95e0; /* Bright color for progress bar fill */
+    --code-bg-color: #1e1e1e; /* Darker background for code blocks */
+    --input-style: solid; /* Keeping input style solid */
+    --block-background-color: #202020; /* Darker background for block elements */
+    --global-font-color: #eaeaea; /* Light font color for global elements */
+
+    --background-color: #222225;
+
+    --background-color: #070708;
+    --page-width: 70em;
+    --font-color: #e8e9ed;
+    --invert-font-color: #222225;
+    --secondary-color: #a3abba;
+    --secondary-color: #d5cec0;
+    --tertiary-color: #a3abba;
+    --primary-color: #09b5a5; /* Updated to the brand color */
+    --primary-color: #50ffff; /* Updated to the brand color */
+    --error-color: #ff3c74;
+    --progress-bar-background: #3f3f44;
+    --progress-bar-fill: #09b5a5; /* Updated to the brand color */
+    --code-bg-color: #3f3f44;
+    --input-style: solid;
+    --display-h1-decoration: none;
+
+    --display-h1-decoration: none;
+}
+
+/* body {
+    background-color: var(--background-color);
+    color: var(--font-color);
+}
+
+a {
+    color: var(--primary-color);
+}
+
+a:hover {
+    background-color: var(--primary-color);
+    color: var(--invert-font-color);
+}
+
+blockquote::after {
+    color: #444; 
+}
+
+pre, code {
+    background-color: var(--code-bg-color);
+    color: var(--font-color);
+}
+
+.terminal-nav:first-child {
+    border-bottom: 1px dashed var(--secondary-color);
+} */
+
+.terminal-mkdocs-main-content {
+    line-height: var(--global-line-height);
+}
+
+strong,
+.highlight {
+    /* background: url(//s2.svgbox.net/pen-brushes.svg?ic=brush-1&color=50ffff); */
+    background-color: #50ffff33;
+}
+
+.terminal-card > header {
+    color: var(--font-color);
+    text-align: center;
+    background-color: var(--progress-bar-background);
+    padding: 0.3em 0.5em;
+}
+.btn.btn-sm {
+    color: var(--font-color);
+    padding: 0.2em 0.5em;
+    font-size: 0.8em;
+}
+
+.loading-message {
+    display: none;
+    margin-top: 20px;
+}
+
+.response-section {
+    display: none;
+    padding-top: 20px;
+}
+
+.tabs {
+    display: flex;
+    flex-direction: column;
+}
+.tab-list {
+    display: flex;
+    padding: 0;
+    margin: 0;
+    list-style-type: none;
+    border-bottom: 1px solid var(--font-color);
+}
+.tab-item {
+    cursor: pointer;
+    padding: 10px;
+    border: 1px solid var(--font-color);
+    margin-right: -1px;
+    border-bottom: none;
+}
+.tab-item:hover,
+.tab-item:focus,
+.tab-item:active {
+    background-color: var(--progress-bar-background);
+}
+.tab-content {
+    display: none;
+    border: 1px solid var(--font-color);
+    border-top: none;
+}
+.tab-content:first-of-type {
+    display: block;
+}
+
+.tab-content header {
+    padding: 0.5em;
+    display: flex; 
+    justify-content: end; 
+    align-items: center;
+    background-color: var(--progress-bar-background);
+}
+.tab-content pre {
+    margin: 0;
+    max-height: 300px; overflow: auto; border:none;
+}

docs/md _sync/changelog.md

@@ -0,0 +1,102 @@
+# Changelog
+
+## [v0.2.77] - 2024-08-04
+
+Significant improvements in text processing and performance:
+
+- 🚀 **Dependency reduction**: Removed dependency on spaCy model for text chunk labeling in cosine extraction strategy.
+- 🤖 **Transformer upgrade**: Implemented text sequence classification using a transformer model for labeling text chunks.
+- ⚡ **Performance enhancement**: Improved model loading speed due to removal of spaCy dependency.
+- 🔧 **Future-proofing**: Laid groundwork for potential complete removal of spaCy dependency in future versions.
+
+These changes address issue #68 and provide a foundation for faster, more efficient text processing in Crawl4AI.
+
+## [v0.2.76] - 2024-08-02
+
+Major improvements in functionality, performance, and cross-platform compatibility! 🚀
+
+- 🐳 **Docker enhancements**: Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
+- 🌐 **Official Docker Hub image**: Launched our first official image on Docker Hub for streamlined deployment.
+- 🔧 **Selenium upgrade**: Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
+- 🖼️ **Image description**: Implemented ability to generate textual descriptions for extracted images from web pages.
+- ⚡ **Performance boost**: Various improvements to enhance overall speed and performance.
+
+A big shoutout to our amazing community contributors:
+- [@aravindkarnam](https://github.com/aravindkarnam) for developing the textual description extraction feature.
+- [@FractalMind](https://github.com/FractalMind) for creating the first official Docker Hub image and fixing Dockerfile errors.
+- [@ketonkss4](https://github.com/ketonkss4) for identifying Selenium's new capabilities, helping us reduce dependencies.
+
+Your contributions are driving Crawl4AI forward! 🙌
+
+## [v0.2.75] - 2024-07-19
+
+Minor improvements for a more maintainable codebase:
+
+- 🔄 Fixed typos in `chunking_strategy.py` and `crawler_strategy.py` to improve code readability
+- 🔄 Removed `.test_pads/` directory from `.gitignore` to keep our repository clean and organized
+
+These changes may seem small, but they contribute to a more stable and sustainable codebase. By fixing typos and updating our `.gitignore` settings, we're ensuring that our code is easier to maintain and scale in the long run.
+
+
+## v0.2.74 - 2024-07-08
+A slew of exciting updates to improve the crawler's stability and robustness! 🎉
+
+- 💻 **UTF encoding fix**: Resolved the Windows \"charmap\" error by adding UTF encoding.
+- 🛡️ **Error handling**: Implemented MaxRetryError exception handling in LocalSeleniumCrawlerStrategy.
+- 🧹 **Input sanitization**: Improved input sanitization and handled encoding issues in LLMExtractionStrategy.
+- 🚮 **Database cleanup**: Removed existing database file and initialized a new one.
+
+## [v0.2.73] - 2024-07-03
+
+💡 In this release, we've bumped the version to v0.2.73 and refreshed our documentation to ensure you have the best experience with our project.
+
+* Supporting website need "with-head" mode to crawl the website with head.
+* Fixing the installation issues for setup.py and dockerfile.
+* Resolve multiple issues.
+
+## [v0.2.72] - 2024-06-30
+
+This release brings exciting updates and improvements to our project! 🎉
+
+* 📚 **Documentation Updates**: Our documentation has been revamped to reflect the latest changes and additions.
+* 🚀 **New Modes in setup.py**: We've added support for three new modes in setup.py: default, torch, and transformers. This enhances the project's flexibility and usability.
+* 🐳 **Docker File Updates**: The Docker file has been updated to ensure seamless compatibility with the new modes and improvements.
+* 🕷️ **Temporary Solution for Headless Crawling**: We've implemented a temporary solution to overcome issues with crawling websites in headless mode.
+
+These changes aim to improve the overall user experience, provide more flexibility, and enhance the project's performance. We're thrilled to share these updates with you and look forward to continuing to evolve and improve our project!
+
+## [0.2.71] - 2024-06-26
+
+**Improved Error Handling and Performance** 🚧
+
+* 🚫 Refactored `crawler_strategy.py` to handle exceptions and provide better error messages, making it more robust and reliable.
+* 💻 Optimized the `get_content_of_website_optimized` function in `utils.py` for improved performance, reducing potential bottlenecks.
+* 💻 Updated `utils.py` with the latest changes, ensuring consistency and accuracy.
+* 🚫 Migrated to `ChromeDriverManager` to resolve Chrome driver download issues, providing a smoother user experience.
+
+These changes focus on refining the existing codebase, resulting in a more stable, efficient, and user-friendly experience. With these improvements, you can expect fewer errors and better performance in the crawler strategy and utility functions.
+
+## [0.2.71] - 2024-06-25
+### Fixed
+- Speed up twice the extraction function.
+
+## [0.2.6] - 2024-06-22
+### Fixed
+- Fix issue #19: Update Dockerfile to ensure compatibility across multiple platforms.
+
+## [0.2.5] - 2024-06-18
+### Added
+- Added five important hooks to the crawler:
+  - on_driver_created: Called when the driver is ready for initializations.
+  - before_get_url: Called right before Selenium fetches the URL.
+  - after_get_url: Called after Selenium fetches the URL.
+  - before_return_html: Called when the data is parsed and ready.
+  - on_user_agent_updated: Called when the user changes the user_agent, causing the driver to reinitialize.
+- Added an example in `quickstart.py` in the example folder under the docs.
+- Enhancement issue #24: Replaced inline HTML tags (e.g., DEL, INS, SUB, ABBR) with textual format for better context handling in LLM.
+- Maintaining the semantic context of inline tags (e.g., abbreviation, DEL, INS) for improved LLM-friendliness.
+- Updated Dockerfile to ensure compatibility across multiple platforms (Hopefully!).
+
+## [0.2.4] - 2024-06-17
+### Fixed
+- Fix issue #22: Use MD5 hash for caching HTML files to handle long URLs

docs/md _sync/contact.md

@@ -0,0 +1,25 @@
+# Contact
+If you have any questions, suggestions, or feedback, please feel free to reach out to us:
+
+- GitHub: [unclecode](https://github.com/unclecode)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+
+## Contributing 🤝
+
+We welcome contributions from the open-source community to help improve Crawl4AI and make it even more valuable for AI enthusiasts and developers. To contribute, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch for your feature or bug fix.
+3. Make your changes and commit them with descriptive messages.
+4. Push your changes to your forked repository.
+5. Submit a pull request to the main repository.
+
+For more information on contributing, please see our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md).
+
+## License 📄
+
+Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
+
+Let's work together to make the web more accessible and useful for AI applications! 💪🌐🤖

docs/md _sync/demo.md

@@ -0,0 +1,231 @@
+# Interactive Demo for Crowler
+<div id="demo">
+    <form id="crawlForm" class="terminal-form">
+        <fieldset>
+            <legend>Enter URL and Options</legend>
+            <div class="form-group">
+                <label for="url">Enter URL:</label>
+                <input type="text" id="url" name="url" required>
+            </div>
+            <div class="form-group">
+                <label for="screenshot">Get Screenshot:</label>
+                <input type="checkbox" id="screenshot" name="screenshot">
+            </div>
+            <div class="form-group">
+                <button class="btn btn-default" type="submit">Submit</button>
+            </div>
+
+        </fieldset>
+    </form>
+
+    <div id="loading" class="loading-message">
+        <div class="terminal-alert terminal-alert-primary">Loading... Please wait.</div>
+    </div>
+
+    <section id="response" class="response-section">
+        <h2>Response</h2>
+        <div class="tabs">
+            <ul class="tab-list">
+                <li class="tab-item" onclick="showTab('markdown')">Markdown</li>
+                <li class="tab-item" onclick="showTab('cleanedHtml')">Cleaned HTML</li>
+                <li class="tab-item" onclick="showTab('media')">Media</li>
+                <li class="tab-item" onclick="showTab('extractedContent')">Extracted Content</li>
+                <li class="tab-item" onclick="showTab('screenshot')">Screenshot</li>
+                <li class="tab-item" onclick="showTab('pythonCode')">Python Code</li>
+            </ul>
+            <div class="tab-content" id="tab-markdown">
+                <header>
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('markdownContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('markdownContent', 'markdown.md')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="markdownContent" class="language-markdown hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-cleanedHtml" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('cleanedHtmlContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('cleanedHtmlContent', 'cleaned.html')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="cleanedHtmlContent" class="language-html hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-media" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('mediaContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('mediaContent', 'media.json')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="mediaContent" class="language-json hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-extractedContent" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('extractedContentContent')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('extractedContentContent', 'extracted_content.json')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="extractedContentContent" class="language-json hljs"></code></pre>
+            </div>
+
+            <div class="tab-content" id="tab-screenshot" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadImage('screenshotContent', 'screenshot.png')">Download</button>
+                    </div>
+                </header>
+                <pre><img id="screenshotContent" /></pre>
+            </div>
+
+            <div class="tab-content" id="tab-pythonCode" style="display: none;">
+                <header >
+                    <div>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="copyToClipboard('pythonCode')">Copy</button>
+                        <button class="btn btn-default btn-ghost btn-sm" onclick="downloadContent('pythonCode', 'example.py')">Download</button>
+                    </div>
+                </header>
+                <pre><code id="pythonCode" class="language-python hljs"></code></pre>
+            </div>
+        </div>
+    </section>
+
+    <div id="error" class="error-message" style="display: none; margin-top:1em;">
+        <div class="terminal-alert terminal-alert-error"></div>
+    </div>
+
+    <script>
+        function showTab(tabId) {
+            const tabs = document.querySelectorAll('.tab-content');
+            tabs.forEach(tab => tab.style.display = 'none');
+            document.getElementById(`tab-${tabId}`).style.display = 'block';
+        }
+
+        function redo(codeBlock, codeText){
+            codeBlock.classList.remove('hljs');
+            codeBlock.removeAttribute('data-highlighted');
+
+            // Set new code and re-highlight
+            codeBlock.textContent = codeText;
+            hljs.highlightBlock(codeBlock);
+        }
+
+        function copyToClipboard(elementId) {
+            const content = document.getElementById(elementId).textContent;
+            navigator.clipboard.writeText(content).then(() => {
+                alert('Copied to clipboard');
+            });
+        }
+
+        function downloadContent(elementId, filename) {
+            const content = document.getElementById(elementId).textContent;
+            const blob = new Blob([content], { type: 'text/plain' });
+            const url = window.URL.createObjectURL(blob);
+            const a = document.createElement('a');
+            a.style.display = 'none';
+            a.href = url;
+            a.download = filename;
+            document.body.appendChild(a);
+            a.click();
+            window.URL.revokeObjectURL(url);
+            document.body.removeChild(a);
+        }
+
+        function downloadImage(elementId, filename) {
+            const content = document.getElementById(elementId).src;
+            const a = document.createElement('a');
+            a.style.display = 'none';
+            a.href = content;
+            a.download = filename;
+            document.body.appendChild(a);
+            a.click();
+            document.body.removeChild(a);
+        }
+
+        document.getElementById('crawlForm').addEventListener('submit', function(event) {
+            event.preventDefault();
+            document.getElementById('loading').style.display = 'block';
+            document.getElementById('response').style.display = 'none';
+
+            const url = document.getElementById('url').value;
+            const screenshot = document.getElementById('screenshot').checked;
+            const data = {
+                urls: [url],
+                bypass_cache: false,
+                word_count_threshold: 5,
+                screenshot: screenshot
+            };
+
+            fetch('/crawl', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify(data)
+            })
+            .then(response => {
+                if (!response.ok) {
+                    if (response.status === 429) {
+                        return response.json().then(err => { 
+                            throw Object.assign(new Error('Rate limit exceeded'), { status: 429, details: err });
+                        });
+                    }
+                    throw new Error('Network response was not ok');
+                }
+                return response.json();
+            })
+            .then(data => {
+                data = data.results[0]; // Only one URL is requested
+                document.getElementById('loading').style.display = 'none';
+                document.getElementById('response').style.display = 'block';
+                redo(document.getElementById('markdownContent'), data.markdown);
+                redo(document.getElementById('cleanedHtmlContent'), data.cleaned_html);
+                redo(document.getElementById('mediaContent'), JSON.stringify(data.media, null, 2));
+                redo(document.getElementById('extractedContentContent'), data.extracted_content);
+                if (screenshot) {
+                    document.getElementById('screenshotContent').src = `data:image/png;base64,${data.screenshot}`;
+                }
+                const pythonCode = `
+from crawl4ai.web_crawler import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+    url='${url}',
+    screenshot=${screenshot}
+)
+print(result)
+                `;
+                redo(document.getElementById('pythonCode'), pythonCode);
+                document.getElementById('error').style.display = 'none';
+            })
+            .catch(error => {
+                document.getElementById('loading').style.display = 'none';
+                document.getElementById('error').style.display = 'block';
+                let errorMessage = 'An unexpected error occurred. Please try again later.';
+                
+                if (error.status === 429) {
+                    const details = error.details;
+                    if (details.retry_after) {
+                        errorMessage = `Rate limit exceeded. Please wait ${parseFloat(details.retry_after).toFixed(1)} seconds before trying again.`;
+                    } else if (details.reset_at) {
+                        const resetTime = new Date(details.reset_at);
+                        const waitTime = Math.ceil((resetTime - new Date()) / 1000);
+                        errorMessage = `Rate limit exceeded. Please try again after ${waitTime} seconds.`;
+                    } else {
+                        errorMessage = `Rate limit exceeded. Please try again later.`;
+                    }
+                } else if (error.message) {
+                    errorMessage = error.message;
+                }
+                
+                document.querySelector('#error .terminal-alert').textContent = errorMessage;
+            });
+        });
+    </script>
+</div>

docs/md _sync/examples/hooks_auth.md

@@ -0,0 +1,100 @@
+# Hooks & Auth
+
+Crawl4AI allows you to customize the behavior of the web crawler using hooks. Hooks are functions that are called at specific points in the crawling process, allowing you to modify the crawler's behavior or perform additional actions. This example demonstrates how to use various hooks to customize the crawling process.
+
+## Example: Using Crawler Hooks
+
+Let's see how we can customize the crawler using hooks! In this example, we'll:
+
+1. Maximize the browser window and log in to a website when the driver is created.
+2. Add a custom header before fetching the URL.
+3. Log the current URL after fetching it.
+4. Log the length of the HTML before returning it.
+
+### Hook Definitions
+
+```python
+from crawl4ai.web_crawler import WebCrawler
+from crawl4ai.crawler_strategy import *
+
+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
+```
+
+### Using the Hooks with the WebCrawler
+
+```python
+print("\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")
+
+print("[LOG] 📦 [bold yellow]Crawler Hooks result:[/bold yellow]")
+print(result)
+```
+
+### Explanation
+
+- `on_driver_created`: This hook is called when the Selenium driver is created. In this example, it maximizes the window, logs in to a website, and adds a custom cookie.
+- `before_get_url`: This hook is called right before Selenium fetches the URL. In this example, it adds a custom HTTP header.
+- `after_get_url`: This hook is called after Selenium fetches the URL. In this example, it logs the current URL.
+- `before_return_html`: This hook is called before returning the HTML content. In this example, it logs the length of the HTML content.
+
+### Additional Ideas
+
+- **Add custom headers to requests**: You can add custom headers to the requests using the `before_get_url` hook.
+- **Perform safety checks**: Use the hooks to perform safety checks before the crawling process starts.
+- **Modify the HTML content**: Use the `before_return_html` hook to modify the HTML content before it is returned.
+- **Log additional information**: Use the hooks to log additional information for debugging or monitoring purposes.
+
+By using these hooks, you can customize the behavior of the crawler to suit your specific needs.

docs/md _sync/examples/index.md

@@ -0,0 +1,29 @@
+# Examples
+
+Welcome to the examples section of Crawl4AI documentation! In this section, you will find practical examples demonstrating how to use Crawl4AI for various web crawling and data extraction tasks. Each example is designed to showcase different features and capabilities of the library.
+
+## Examples Index
+
+### [LLM Extraction](llm_extraction.md)
+
+This example demonstrates how to use Crawl4AI to extract information using Large Language Models (LLMs). You will learn how to configure the `LLMExtractionStrategy` to get structured data from web pages.
+
+### [JS Execution & CSS Filtering](js_execution_css_filtering.md)
+
+Learn how to execute custom JavaScript code and filter data using CSS selectors. This example shows how to perform complex web interactions and extract specific content from web pages.
+
+### [Hooks & Auth](hooks_auth.md)
+
+This example covers the use of custom hooks for authentication and other pre-crawling tasks. You will see how to set up hooks to modify headers, authenticate sessions, and perform other preparatory actions before crawling.
+
+### [Summarization](summarization.md)
+
+Discover how to use Crawl4AI to summarize web page content. This example demonstrates the summarization capabilities of the library, helping you extract concise information from lengthy web pages.
+
+### [Research Assistant](research_assistant.md)
+
+In this example, Crawl4AI is used as a research assistant to gather and organize information from multiple sources. You will learn how to use various extraction and chunking strategies to compile a comprehensive report.
+
+---
+
+Each example includes detailed explanations and code snippets to help you understand and implement the features in your projects. Click on the links to explore each example and start making the most of Crawl4AI!

docs/md _sync/examples/js_execution_css_filtering.md

@@ -0,0 +1,44 @@
+# JS Execution & CSS Filtering
+
+In this example, we'll demonstrate how to use Crawl4AI to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
+
+## Example: Extracting Structured Data
+
+```python
+# Import necessary modules
+from crawl4ai import WebCrawler
+from crawl4ai.chunking_strategy import *
+from crawl4ai.extraction_strategy import *
+from crawl4ai.crawler_strategy import *
+
+# Define the JavaScript code to click the "Load More" button
+js_code = ["""
+const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+loadMoreButton && loadMoreButton.click();
+"""]
+
+crawler = WebCrawler(verbose=True)
+crawler.warmup()
+# Run the crawler with keyword filtering and CSS selector
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    js=js_code,
+    css_selector="p",
+    extraction_strategy=CosineStrategy(
+        semantic_filter="technology",
+    ),
+)
+
+# Display the extracted result
+print(result)
+```
+
+### Explanation
+
+1. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button. This is useful for loading additional content dynamically.
+2. **CSS Selector**: The `css_selector="p"` parameter ensures that only paragraph (`<p>`) tags are extracted from the web page.
+3. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
+
+## Try It Yourself
+
+This example demonstrates the power and flexibility of Crawl4AI in handling complex web interactions and extracting meaningful data. You can customize the JavaScript code, CSS selectors, and extraction strategies to suit your specific requirements.

docs/md _sync/examples/llm_extraction.md

@@ -0,0 +1,90 @@
+# LLM Extraction
+
+Crawl4AI allows you to use Language Models (LLMs) to extract structured data or relevant content from web pages. Below are two examples demonstrating how to use LLMExtractionStrategy for different purposes.
+
+## Example 1: Extract Structured Data
+
+In this example, we use the `LLMExtractionStrategy` to extract structured data (model names and their fees) from the OpenAI pricing page.
+
+```python
+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 *
+
+url = r'https://openai.com/api/pricing/'
+
+crawler = WebCrawler()
+crawler.warmup()
+
+from pydantic import BaseModel, Field
+
+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.")
+
+result = crawler.run(
+    url=url,
+    word_count_threshold=1,
+    extraction_strategy= LLMExtractionStrategy(
+        provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
+        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. Make sure not to miss anything 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" }'
+    ),
+    bypass_cache=True,
+)
+
+model_fees = json.loads(result.extracted_content)
+
+print(len(model_fees))
+
+with open(".data/data.json", "w", encoding="utf-8") as f:
+    f.write(result.extracted_content)
+```
+
+## Example 2: Extract Relevant Content
+
+In this example, we instruct the LLM to extract only content related to technology from the NBC News business page.
+
+```python
+crawler = WebCrawler()
+crawler.warmup()
+
+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"
+        ),
+    bypass_cache=True,
+    )
+
+model_fees = json.loads(result.extracted_content)
+
+print(len(model_fees))
+
+with open(".data/data.json", "w", encoding="utf-8") as f:
+    f.write(result.extracted_content)
+```
+
+## Customizing LLM Provider
+
+Under the hood, Crawl4AI uses the `litellm` library, which allows you to use any LLM provider you want. Just pass the correct model name and API token.
+
+```python
+extraction_strategy=LLMExtractionStrategy(
+    provider="your_llm_provider/model_name",
+    api_token="your_api_token",
+    instruction="Your extraction instruction"
+)
+```
+
+This flexibility allows you to integrate with various LLM providers and tailor the extraction process to your specific needs.

docs/md _sync/examples/research_assistant.md

@@ -0,0 +1,248 @@
+## Research Assistant Example
+
+This example demonstrates how to build a research assistant using `Chainlit` and `Crawl4AI`. The assistant will be capable of crawling web pages for information and answering questions based on the crawled content. Additionally, it integrates speech-to-text functionality for audio inputs.
+
+### Step-by-Step Guide
+
+1. **Install Required Packages**
+
+    Ensure you have the necessary packages installed. You need `chainlit`, `groq`, `requests`, and `openai`.
+
+    ```bash
+    pip install chainlit groq requests openai
+    ```
+
+2. **Import Libraries**
+
+    Import all the necessary modules and initialize the OpenAI client.
+
+    ```python
+    import os
+    import time
+    from openai import AsyncOpenAI
+    import chainlit as cl
+    import re
+    import requests
+    from io import BytesIO
+    from chainlit.element import ElementBased
+    from groq import Groq
+
+    from concurrent.futures import ThreadPoolExecutor
+
+    client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
+
+    # Instrument the OpenAI client
+    cl.instrument_openai()
+    ```
+
+3. **Set Configuration**
+
+    Define the model settings for the assistant.
+
+    ```python
+    settings = {
+        "model": "llama3-8b-8192",
+        "temperature": 0.5,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
+    ```
+
+4. **Define Utility Functions**
+
+    - **Extract URLs from Text**: Use regex to find URLs in messages.
+
+        ```python
+        def extract_urls(text):
+            url_pattern = re.compile(r'(https?://\S+)')
+            return url_pattern.findall(text)
+        ```
+
+    - **Crawl URL**: Send a request to `Crawl4AI` to fetch the content of a URL.
+
+        ```python
+        def crawl_url(url):
+            data = {
+                "urls": [url],
+                "include_raw_html": True,
+                "word_count_threshold": 10,
+                "extraction_strategy": "NoExtractionStrategy",
+                "chunking_strategy": "RegexChunking"
+            }
+            response = requests.post("https://crawl4ai.com/crawl", json=data)
+            response_data = response.json()
+            response_data = response_data['results'][0]
+            return response_data['markdown']
+        ```
+
+5. **Initialize Chat Start Event**
+
+    Set up the initial chat message and user session.
+
+    ```python
+    @cl.on_chat_start
+    async def on_chat_start():
+        cl.user_session.set("session", {
+            "history": [],
+            "context": {}
+        })  
+        await cl.Message(
+            content="Welcome to the chat! How can I assist you today?"
+        ).send()
+    ```
+
+6. **Handle Incoming Messages**
+
+    Process user messages, extract URLs, and crawl them concurrently. Update the chat history and system message.
+
+    ```python
+    @cl.on_message
+    async def on_message(message: cl.Message):
+        user_session = cl.user_session.get("session")
+
+        # Extract URLs from the user's message
+        urls = extract_urls(message.content)
+
+        futures = []
+        with ThreadPoolExecutor() as executor:
+            for url in urls:
+                futures.append(executor.submit(crawl_url, url))
+
+        results = [future.result() for future in futures]
+
+        for url, result in zip(urls, results):
+            ref_number = f"REF_{len(user_session['context']) + 1}"
+            user_session["context"][ref_number] = {
+                "url": url,
+                "content": result
+            }    
+
+        user_session["history"].append({
+            "role": "user",
+            "content": message.content
+        })
+
+        # Create a system message that includes the context
+        context_messages = [
+            f'<appendix ref="{ref}">\n{data["content"]}\n</appendix>'
+            for ref, data in user_session["context"].items()
+        ]
+        if context_messages:
+            system_message = {
+                "role": "system",
+                "content": (
+                    "You are a helpful bot. Use the following context for answering questions. "
+                    "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
+                    "If the question requires any information from the provided appendices or context, refer to the sources. "
+                    "If not, there is no need to add a references section. "
+                    "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
+                    "\n\n".join(context_messages)
+                )
+            }
+        else:
+            system_message = {
+                "role": "system",
+                "content": "You are a helpful assistant."
+            }
+
+        msg = cl.Message(content="")
+        await msg.send()
+
+        # Get response from the LLM
+        stream = await client.chat.completions.create(
+            messages=[
+                system_message,
+                *user_session["history"]
+            ],
+            stream=True,
+            **settings
+        )
+
+        assistant_response = ""
+        async for part in stream:
+            if token := part.choices[0].delta.content:
+                assistant_response += token
+                await msg.stream_token(token)
+
+        # Add assistant message to the history
+        user_session["history"].append({
+            "role": "assistant",
+            "content": assistant_response
+        })
+        await msg.update()
+
+        # Append the reference section to the assistant's response
+        reference_section = "\n\nReferences:\n"
+        for ref, data in user_session["context"].items():
+            reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
+
+        msg.content += reference_section
+        await msg.update()
+    ```
+
+7. **Handle Audio Input**
+
+    Capture and transcribe audio input. Store the audio buffer and transcribe it when the audio ends.
+
+    ```python
+    @cl.on_audio_chunk
+    async def on_audio_chunk(chunk: cl.AudioChunk):
+        if chunk.isStart:
+            buffer = BytesIO()
+            buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
+            cl.user_session.set("audio_buffer", buffer)
+            cl.user_session.set("audio_mime_type", chunk.mimeType)
+
+        cl.user_session.get("audio_buffer").write(chunk.data)
+
+    @cl.step(type="tool")
+    async def speech_to_text(audio_file):
+        cli = Groq()
+        response = await client.audio.transcriptions.create(
+            model="whisper-large-v3", file=audio_file
+        )
+        return response.text
+
+    @cl.on_audio_end
+    async def on_audio_end(elements: list[ElementBased]):
+        audio_buffer: BytesIO = cl.user_session.get("audio_buffer")
+        audio_buffer.seek(0)
+        audio_file = audio_buffer.read()
+        audio_mime_type: str = cl.user_session.get("audio_mime_type")
+        
+        start_time = time.time()
+        transcription = await speech_to_text((audio_buffer.name, audio_file, audio_mime_type))
+        end_time = time.time()
+        print(f"Transcription took {end_time - start_time} seconds")
+        
+        user_msg = cl.Message(
+            author="You", 
+            type="user_message",
+            content=transcription
+        )
+        await user_msg.send()
+        await on_message(user_msg)
+    ```
+
+8. **Run the Chat Application**
+
+    Start the Chainlit application.
+
+    ```python
+    if __name__ == "__main__":
+        from chainlit.cli import run_chainlit
+        run_chainlit(__file__)
+    ```
+
+### Explanation
+
+- **Libraries and Configuration**: Import necessary libraries and configure the OpenAI client.
+- **Utility Functions**: Define functions to extract URLs and crawl them.
+- **Chat Start Event**: Initialize chat session and welcome message.
+- **Message Handling**: Extract URLs, crawl them concurrently, and update chat history and context.
+- **Audio Handling**: Capture, buffer, and transcribe audio input, then process the transcription as text.
+- **Running the Application**: Start the Chainlit server to interact with the assistant.
+
+This example showcases how to create an interactive research assistant that can fetch, process, and summarize web content, along with handling audio inputs for a seamless user experience.

docs/md _sync/examples/summarization.md

@@ -0,0 +1,108 @@
+## Summarization Example
+
+This example demonstrates how to use `Crawl4AI` to extract a summary from a web page. The goal is to obtain the title, a detailed summary, a brief summary, and a list of keywords from the given page.
+
+### Step-by-Step Guide
+
+1. **Import Necessary Modules**
+
+    First, import the necessary modules and classes.
+
+    ```python
+    import os
+    import time
+    import json
+    from crawl4ai.web_crawler import WebCrawler
+    from crawl4ai.chunking_strategy import *
+    from crawl4ai.extraction_strategy import *
+    from crawl4ai.crawler_strategy import *
+    from pydantic import BaseModel, Field
+    ```
+
+2. **Define the URL to be Crawled**
+
+    Set the URL of the web page you want to summarize.
+
+    ```python
+    url = r'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot'
+    ```
+
+3. **Initialize the WebCrawler**
+
+    Create an instance of the `WebCrawler` and call the `warmup` method.
+
+    ```python
+    crawler = WebCrawler()
+    crawler.warmup()
+    ```
+
+4. **Define the Data Model**
+
+    Use Pydantic to define the structure of the extracted data.
+
+    ```python
+    class PageSummary(BaseModel):
+        title: str = Field(..., description="Title of the page.")
+        summary: str = Field(..., description="Summary of the page.")
+        brief_summary: str = Field(..., description="Brief summary of the page.")
+        keywords: list = Field(..., description="Keywords assigned to the page.")
+    ```
+
+5. **Run the Crawler**
+
+    Set up and run the crawler with the `LLMExtractionStrategy`. Provide the necessary parameters, including the schema for the extracted data and the instruction for the LLM.
+
+    ```python
+    result = crawler.run(
+        url=url,
+        word_count_threshold=1,
+        extraction_strategy=LLMExtractionStrategy(
+            provider="openai/gpt-4o", 
+            api_token=os.getenv('OPENAI_API_KEY'), 
+            schema=PageSummary.model_json_schema(),
+            extraction_type="schema",
+            apply_chunking=False,
+            instruction=(
+                "From the crawled content, extract the following details: "
+                "1. Title of the page "
+                "2. Summary of the page, which is a detailed summary "
+                "3. Brief summary of the page, which is a paragraph text "
+                "4. Keywords assigned to the page, which is a list of keywords. "
+                'The extracted JSON format should look like this: '
+                '{ "title": "Page Title", "summary": "Detailed summary of the page.", '
+                '"brief_summary": "Brief summary in a paragraph.", "keywords": ["keyword1", "keyword2", "keyword3"] }'
+            )
+        ),
+        bypass_cache=True,
+    )
+    ```
+
+6. **Process the Extracted Data**
+
+    Load the extracted content into a JSON object and print it.
+
+    ```python
+    page_summary = json.loads(result.extracted_content)
+    print(page_summary)
+    ```
+
+7. **Save the Extracted Data**
+
+    Save the extracted data to a file for further use.
+
+    ```python
+    with open(".data/page_summary.json", "w", encoding="utf-8") as f:
+        f.write(result.extracted_content)
+    ```
+
+### Explanation
+
+- **Importing Modules**: Import the necessary modules, including `WebCrawler` and `LLMExtractionStrategy` from `Crawl4AI`.
+- **URL Definition**: Set the URL of the web page you want to crawl and summarize.
+- **WebCrawler Initialization**: Create an instance of `WebCrawler` and call the `warmup` method to prepare the crawler.
+- **Data Model Definition**: Define the structure of the data you want to extract using Pydantic's `BaseModel`.
+- **Crawler Execution**: Run the crawler with the `LLMExtractionStrategy`, providing the schema and detailed instructions for the extraction process.
+- **Data Processing**: Load the extracted content into a JSON object and print it to verify the results.
+- **Data Saving**: Save the extracted data to a file for further use.
+
+This example demonstrates how to harness the power of `Crawl4AI` to perform advanced web crawling and data extraction tasks with minimal code.

docs/md _sync/full_details/advanced_features.md

@@ -0,0 +1,138 @@
+# Advanced Features
+
+Crawl4AI offers a range of advanced features that allow you to fine-tune your web crawling and data extraction process. This section will cover some of these advanced features, including taking screenshots, extracting media and links, customizing the user agent, using custom hooks, and leveraging CSS selectors.
+
+## Taking Screenshots 📸
+
+One of the cool features of Crawl4AI is the ability to take screenshots of the web pages you're crawling. This can be particularly useful for visual verification or for capturing the state of dynamic content.
+
+Here's how you can take a screenshot:
+
+```python
+from crawl4ai import WebCrawler
+import base64
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+crawler.warmup()
+
+# Run the crawler with the screenshot parameter
+result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
+
+# Save the screenshot to a file
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result.screenshot))
+
+print("Screenshot saved to 'screenshot.png'!")
+```
+
+In this example, we create a `WebCrawler` instance, warm it up, and then run it with the `screenshot` parameter set to `True`. The screenshot is saved as a base64 encoded string in the result, which we then decode and save as a PNG file.
+
+## Extracting Media and Links 🎨🔗
+
+Crawl4AI can extract all media tags (images, audio, and video) and links (both internal and external) from a web page. This feature is useful for collecting multimedia content or analyzing link structures.
+
+Here's an example:
+
+```python
+from crawl4ai import WebCrawler
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+crawler.warmup()
+
+# Run the crawler
+result = crawler.run(url="https://www.nbcnews.com/business")
+
+print("Extracted media:", result.media)
+print("Extracted links:", result.links)
+```
+
+In this example, the `result` object contains dictionaries for media and links, which you can access and use as needed.
+
+## Customizing the User Agent 🕵️‍♂️
+
+Crawl4AI allows you to set a custom user agent for your HTTP requests. This can help you avoid detection by web servers or simulate different browsing environments.
+
+Here's how to set a custom user agent:
+
+```python
+from crawl4ai import WebCrawler
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+crawler.warmup()
+
+# Run the crawler with a custom user agent
+result = crawler.run(url="https://www.nbcnews.com/business", user_agent="Mozilla/5.0 (compatible; MyCrawler/1.0)")
+
+print("Crawl result:", result)
+```
+
+In this example, we specify a custom user agent string when running the crawler.
+
+## Using Custom Hooks 🪝
+
+Hooks are a powerful feature in Crawl4AI that allow you to customize the crawling process at various stages. You can define hooks for actions such as driver initialization, before and after URL fetching, and before returning the HTML.
+
+Here's an example of using hooks:
+
+```python
+from crawl4ai import WebCrawler
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+
+# Define the hooks
+def on_driver_created(driver):
+    driver.maximize_window()
+    driver.get('https://example.com/login')
+    WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.NAME, 'username'))).send_keys('testuser')
+    driver.find_element(By.NAME, 'password').send_keys('password123')
+    driver.find_element(By.NAME, 'login').click()
+    return driver
+
+def before_get_url(driver):
+    driver.execute_cdp_cmd('Network.setExtraHTTPHeaders', {'headers': {'X-Test-Header': 'test'}})
+    return driver
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+crawler.warmup()
+
+# Set the hooks
+crawler.set_hook('on_driver_created', on_driver_created)
+crawler.set_hook('before_get_url', before_get_url)
+
+# Run the crawler
+result = crawler.run(url="https://example.com")
+
+print("Crawl result:", result)
+```
+
+In this example, we define hooks to handle driver initialization and custom headers before fetching the URL.
+
+## Using CSS Selectors 🎯
+
+CSS selectors allow you to target specific elements on a web page for extraction. This can be useful for scraping structured content, such as articles or product details.
+
+Here's an example of using a CSS selector:
+
+```python
+from crawl4ai import WebCrawler
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+crawler.warmup()
+
+# Run the crawler with a CSS selector to extract only H2 tags
+result = crawler.run(url="https://www.nbcnews.com/business", css_selector="h2")
+
+print("Extracted H2 tags:", result.extracted_content)
+```
+
+In this example, we use the `css_selector` parameter to extract only the H2 tags from the web page.
+
+---
+
+With these advanced features, you can leverage Crawl4AI to perform sophisticated web crawling and data extraction tasks. Whether you need to take screenshots, extract specific elements, customize the crawling process, or set custom headers, Crawl4AI provides the flexibility and power to meet your needs. Happy crawling! 🕷️🚀

docs/md _sync/full_details/chunking_strategies.md

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+## Chunking Strategies 📚
+
+Crawl4AI provides several powerful chunking strategies to divide text into manageable parts for further processing. Each strategy has unique characteristics and is suitable for different scenarios. Let's explore them one by one.
+
+### RegexChunking
+
+`RegexChunking` splits text using regular expressions. This is ideal for creating chunks based on specific patterns like paragraphs or sentences.
+
+#### When to Use
+- Great for structured text with consistent delimiters.
+- Suitable for documents where specific patterns (e.g., double newlines, periods) indicate logical chunks.
+
+#### Parameters
+- `patterns` (list, optional): Regular expressions used to split the text. Default is to split by double newlines (`['\n\n']`).
+
+#### Example
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+# Define patterns for splitting text
+patterns = [r'\n\n', r'\. ']
+chunker = RegexChunking(patterns=patterns)
+
+# Sample text
+text = "This is a sample text. It will be split into chunks.\n\nThis is another paragraph."
+
+# Chunk the text
+chunks = chunker.chunk(text)
+print(chunks)
+```
+
+### NlpSentenceChunking
+
+`NlpSentenceChunking` uses NLP models to split text into sentences, ensuring accurate sentence boundaries.
+
+#### When to Use
+- Ideal for texts where sentence boundaries are crucial.
+- Useful for creating chunks that preserve grammatical structures.
+
+#### Parameters
+- None.
+
+#### Example
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+chunker = NlpSentenceChunking()
+
+# Sample text
+text = "This is a sample text. It will be split into sentences. Here's another sentence."
+
+# Chunk the text
+chunks = chunker.chunk(text)
+print(chunks)
+```
+
+### TopicSegmentationChunking
+
+`TopicSegmentationChunking` employs the TextTiling algorithm to segment text into topic-based chunks. This method identifies thematic boundaries.
+
+#### When to Use
+- Perfect for long documents with distinct topics.
+- Useful when preserving topic continuity is more important than maintaining text order.
+
+#### Parameters
+- `num_keywords` (int, optional): Number of keywords for each topic segment. Default is `3`.
+
+#### Example
+```python
+from crawl4ai.chunking_strategy import TopicSegmentationChunking
+
+chunker = TopicSegmentationChunking(num_keywords=3)
+
+# Sample text
+text = "This document contains several topics. Topic one discusses AI. Topic two covers machine learning."
+
+# Chunk the text
+chunks = chunker.chunk(text)
+print(chunks)
+```
+
+### FixedLengthWordChunking
+
+`FixedLengthWordChunking` splits text into chunks based on a fixed number of words. This ensures each chunk has approximately the same length.
+
+#### When to Use
+- Suitable for processing large texts where uniform chunk size is important.
+- Useful when the number of words per chunk needs to be controlled.
+
+#### Parameters
+- `chunk_size` (int, optional): Number of words per chunk. Default is `100`.
+
+#### Example
+```python
+from crawl4ai.chunking_strategy import FixedLengthWordChunking
+
+chunker = FixedLengthWordChunking(chunk_size=10)
+
+# Sample text
+text = "This is a sample text. It will be split into chunks of fixed length."
+
+# Chunk the text
+chunks = chunker.chunk(text)
+print(chunks)
+```
+
+### SlidingWindowChunking
+
+`SlidingWindowChunking` uses a sliding window approach to create overlapping chunks. Each chunk has a fixed length, and the window slides by a specified step size.
+
+#### When to Use
+- Ideal for creating overlapping chunks to preserve context.
+- Useful for tasks where context from adjacent chunks is needed.
+
+#### Parameters
+- `window_size` (int, optional): Number of words in each chunk. Default is `100`.
+- `step` (int, optional): Number of words to slide the window. Default is `50`.
+
+#### Example
+```python
+from crawl4ai.chunking_strategy import SlidingWindowChunking
+
+chunker = SlidingWindowChunking(window_size=10, step=5)
+
+# Sample text
+text = "This is a sample text. It will be split using a sliding window approach to preserve context."
+
+# Chunk the text
+chunks = chunker.chunk(text)
+print(chunks)
+```
+
+With these chunking strategies, you can choose the best method to divide your text based on your specific needs. Whether you need precise sentence boundaries, topic-based segmentation, or uniform chunk sizes, Crawl4AI has you covered. Happy chunking! 📝✨

docs/md _sync/full_details/crawl_request_parameters.md

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+# Crawl Request Parameters
+
+The `run` function in Crawl4AI is designed to be highly configurable, allowing you to customize the crawling and extraction process to suit your needs. Below are the parameters you can use with the `run` function, along with their descriptions, possible values, and examples.
+
+## Parameters
+
+### url (str)
+**Description:** The URL of the webpage to crawl.
+**Required:** Yes
+**Example:**
+```python
+url = "https://www.nbcnews.com/business"
+```
+
+### word_count_threshold (int)
+**Description:** The minimum number of words a block must contain to be considered meaningful. The default value is `5`.
+**Required:** No
+**Default Value:** `5`
+**Example:**
+```python
+word_count_threshold = 10
+```
+
+### extraction_strategy (ExtractionStrategy)
+**Description:** The strategy to use for extracting content from the HTML. It must be an instance of `ExtractionStrategy`. If not provided, the default is `NoExtractionStrategy`.
+**Required:** No
+**Default Value:** `NoExtractionStrategy()`
+**Example:**
+```python
+extraction_strategy = CosineStrategy(semantic_filter="finance")
+```
+
+### chunking_strategy (ChunkingStrategy)
+**Description:** The strategy to use for chunking the text before processing. It must be an instance of `ChunkingStrategy`. The default value is `RegexChunking()`.
+**Required:** No
+**Default Value:** `RegexChunking()`
+**Example:**
+```python
+chunking_strategy = NlpSentenceChunking()
+```
+
+### bypass_cache (bool)
+**Description:** Whether to force a fresh crawl even if the URL has been previously crawled. The default value is `False`.
+**Required:** No
+**Default Value:** `False`
+**Example:**
+```python
+bypass_cache = True
+```
+
+### css_selector (str)
+**Description:** The CSS selector to target specific parts of the HTML for extraction. If not provided, the entire HTML will be processed.
+**Required:** No
+**Default Value:** `None`
+**Example:**
+```python
+css_selector = "div.article-content"
+```
+
+### screenshot (bool)
+**Description:** Whether to take screenshots of the page. The default value is `False`.
+**Required:** No
+**Default Value:** `False`
+**Example:**
+```python
+screenshot = True
+```
+
+### user_agent (str)
+**Description:** The user agent to use for the HTTP requests. If not provided, a default user agent will be used.
+**Required:** No
+**Default Value:** `None`
+**Example:**
+```python
+user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
+```
+
+### verbose (bool)
+**Description:** Whether to enable verbose logging. The default value is `True`.
+**Required:** No
+**Default Value:** `True`
+**Example:**
+```python
+verbose = True
+```
+
+### **kwargs
+Additional keyword arguments that can be passed to customize the crawling process further. Some notable options include:
+
+- **only_text (bool):** Whether to extract only text content, excluding HTML tags. Default is `False`.
+
+**Example:**
+```python
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    css_selector="p",
+    only_text=True
+)
+```
+
+## Example Usage
+
+Here's an example of how to use the `run` function with various parameters:
+
+```python
+from crawl4ai import WebCrawler
+from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+# Create the WebCrawler instance 
+crawler = WebCrawler() 
+
+# Run the crawler with custom parameters
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    word_count_threshold=10,
+    extraction_strategy=CosineStrategy(semantic_filter="finance"),
+    chunking_strategy=NlpSentenceChunking(),
+    bypass_cache=True,
+    css_selector="div.article-content",
+    screenshot=True,
+    user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3",
+    verbose=True,
+    only_text=True
+)
+
+print(result)
+```
+
+This example demonstrates how to configure various parameters to customize the crawling and extraction process using Crawl4AI.

docs/md _sync/full_details/crawl_result_class.md

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+# Crawl Result
+
+The `CrawlResult` class is the heart of Crawl4AI's output, encapsulating all the data extracted from a crawling session. This class contains various fields that store the results of the web crawling and extraction process. Let's break down each field and see what it holds. 🎉
+
+## Class Definition
+
+```python
+class CrawlResult(BaseModel):
+    url: str
+    html: str
+    success: bool
+    cleaned_html: Optional[str] = None
+    media: Dict[str, List[Dict]] = {}
+    links: Dict[str, List[Dict]] = {}
+    screenshot: Optional[str] = None
+    markdown: Optional[str] = None
+    extracted_content: Optional[str] = None
+    metadata: Optional[dict] = None
+    error_message: Optional[str] = None
+```
+
+## Fields Explanation
+
+### `url: str`
+The URL that was crawled. This field simply stores the URL of the web page that was processed.
+
+### `html: str`
+The raw HTML content of the web page. This is the unprocessed HTML source as retrieved by the crawler.
+
+### `success: bool`
+A flag indicating whether the crawling and extraction were successful. If any error occurs during the process, this will be `False`.
+
+### `cleaned_html: Optional[str]`
+The cleaned HTML content of the web page. This field holds the HTML after removing unwanted tags like `<script>`, `<style>`, and others that do not contribute to the useful content.
+
+### `media: Dict[str, List[Dict]]`
+A dictionary containing lists of extracted media elements from the web page. The media elements are categorized into images, videos, and audios. Here’s how they are structured:
+
+- **Images**: Each image is represented as a dictionary with `src` (source URL) and `alt` (alternate text).
+- **Videos**: Each video is represented similarly with `src` and `alt`.
+- **Audios**: Each audio is represented with `src` and `alt`.
+
+```python
+media = {
+    'images': [
+        {'src': 'image_url1', 'alt': 'description1', "type": "image"},
+        {'src': 'image_url2', 'alt': 'description2', "type": "image"}
+    ],
+    'videos': [
+        {'src': 'video_url1', 'alt': 'description1', "type": "video"}
+    ],
+    'audios': [
+        {'src': 'audio_url1', 'alt': 'description1', "type": "audio"}
+    ]
+}
+```
+
+### `links: Dict[str, List[Dict]]`
+A dictionary containing lists of internal and external links extracted from the web page. Each link is represented as a dictionary with `href` (URL) and `text` (link text).
+
+- **Internal Links**: Links pointing to the same domain.
+- **External Links**: Links pointing to different domains.
+
+```python
+links = {
+    'internal': [
+        {'href': 'internal_link1', 'text': 'link_text1'},
+        {'href': 'internal_link2', 'text': 'link_text2'}
+    ],
+    'external': [
+        {'href': 'external_link1', 'text': 'link_text1'}
+    ]
+}
+```
+
+### `screenshot: Optional[str]`
+A base64-encoded screenshot of the web page. This field stores the screenshot data if the crawling was configured to take a screenshot.
+
+### `markdown: Optional[str]`
+The content of the web page converted to Markdown format. This is useful for generating clean, readable text that retains the structure of the original HTML.
+
+### `extracted_content: Optional[str]`
+The content extracted based on the specified extraction strategy. This field holds the meaningful content blocks extracted from the web page, ready for your AI and data processing needs.
+
+### `metadata: Optional[dict]`
+A dictionary containing metadata extracted from the web page, such as title, description, keywords, and other meta tags.
+
+### `error_message: Optional[str]`
+If an error occurs during crawling, this field will contain the error message, helping you debug and understand what went wrong. 🚨
+
+## Example Usage
+
+Here's a quick example to illustrate how you might use the `CrawlResult` in your code:
+
+```python
+from crawl4ai import WebCrawler
+
+# Create the WebCrawler instance
+crawler = WebCrawler()
+
+# Run the crawler on a URL
+result = crawler.run(url="https://www.example.com")
+
+# Check if the crawl was successful
+if result.success:
+    print("Crawl succeeded!")
+    print("URL:", result.url)
+    print("HTML:", result.html[:100])  # Print the first 100 characters of the HTML
+    print("Cleaned HTML:", result.cleaned_html[:100])
+    print("Media:", result.media)
+    print("Links:", result.links)
+    print("Screenshot:", result.screenshot)
+    print("Markdown:", result.markdown[:100])
+    print("Extracted Content:", result.extracted_content)
+    print("Metadata:", result.metadata)
+else:
+    print("Crawl failed with error:", result.error_message)
+```
+
+With this setup, you can easily access all the valuable data extracted from the web page and integrate it into your applications. Happy crawling! 🕷️🤖

docs/md _sync/full_details/extraction_strategies.md

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+## Extraction Strategies 🧠
+
+Crawl4AI offers powerful extraction strategies to derive meaningful information from web content. Let's dive into two of the most important strategies: `CosineStrategy` and `LLMExtractionStrategy`.
+
+### CosineStrategy
+
+`CosineStrategy` uses hierarchical clustering based on cosine similarity to group text chunks into meaningful clusters. This method converts each chunk into its embedding and then clusters them to form semantical chunks.
+
+#### When to Use
+- Ideal for fast, accurate semantic segmentation of text.
+- Perfect for scenarios where LLMs might be overkill or too slow.
+- Suitable for narrowing down content based on specific queries or keywords.
+
+#### Parameters
+- `semantic_filter` (str, optional): Keywords for filtering relevant documents before clustering. Documents are filtered based on their cosine similarity to the keyword filter embedding. Default is `None`.
+- `word_count_threshold` (int, optional): Minimum number of words per cluster. Default is `20`.
+- `max_dist` (float, optional): Maximum cophenetic distance on the dendrogram to form clusters. Default is `0.2`.
+- `linkage_method` (str, optional): Linkage method for hierarchical clustering. Default is `'ward'`.
+- `top_k` (int, optional): Number of top categories to extract. Default is `3`.
+- `model_name` (str, optional): Model name for embedding generation. Default is `'BAAI/bge-small-en-v1.5'`.
+
+#### Example
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy
+strategy = CosineStrategy(
+    semantic_filter="finance economy stock market",
+    word_count_threshold=10,
+    max_dist=0.2,
+    linkage_method='ward',
+    top_k=3,
+    model_name='BAAI/bge-small-en-v1.5'
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+### LLMExtractionStrategy
+
+`LLMExtractionStrategy` leverages a Language Model (LLM) to extract meaningful content from HTML. This strategy uses an external provider for LLM completions to perform extraction based on instructions.
+
+#### When to Use
+- Suitable for complex extraction tasks requiring nuanced understanding.
+- Ideal for scenarios where detailed instructions can guide the extraction process.
+- Perfect for extracting specific types of information or content with precise guidelines.
+
+#### Parameters
+- `provider` (str, optional): Provider for language model completions (e.g., openai/gpt-4). Default is `DEFAULT_PROVIDER`.
+- `api_token` (str, optional): API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.
+- `instruction` (str, optional): Instructions to guide the LLM on how to perform the extraction. Default is `None`.
+
+#### Example Without Instructions
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy without instructions
+strategy = LLMExtractionStrategy(
+    provider='openai',
+    api_token='your_api_token'
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+#### Example With Instructions
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from crawl4ai import WebCrawler
+
+crawler = WebCrawler()
+crawler.warmup()
+
+# Define extraction strategy with instructions
+strategy = LLMExtractionStrategy(
+    provider='openai',
+    api_token='your_api_token',
+    instruction="Extract only financial news and summarize key points."
+)
+
+# Sample URL
+url = "https://www.nbcnews.com/business"
+
+# Run the crawler with the extraction strategy
+result = crawler.run(url=url, extraction_strategy=strategy)
+print(result.extracted_content)
+```
+
+#### Use Cases for LLMExtractionStrategy
+- Extracting specific data types from structured or semi-structured content.
+- Generating summaries, extracting key information, or transforming content into different formats.
+- Performing detailed extractions based on custom instructions.
+
+For more detailed examples, please refer to the [Examples section](../examples/index.md) of the documentation.
+
+---
+
+By choosing the right extraction strategy, you can effectively extract the most relevant and useful information from web content. Whether you need fast, accurate semantic segmentation with `CosineStrategy` or nuanced, instruction-based extraction with `LLMExtractionStrategy`, Crawl4AI has you covered. Happy extracting! 🕵️‍♂️✨

docs/md _sync/index.md

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+# Crawl4AI v0.2.77
+
+Welcome to the official documentation for Crawl4AI! 🕷️🤖 Crawl4AI is an open-source Python library designed to simplify web crawling and extract useful information from web pages. This documentation will guide you through the features, usage, and customization of Crawl4AI.
+
+
+## Try the [Demo](demo.md)
+
+Just try it now and crawl different pages to see how it works. You can set the links, see the structures of the output, and also view the Python sample code on how to run it. The old demo is available at [/old_demo](/old) where you can see more details.
+
+## Introduction
+
+Crawl4AI has one clear task: to make crawling and data extraction from web pages easy and efficient, especially for large language models (LLMs) and AI applications. Whether you are using it as a REST API or a Python library, Crawl4AI offers a robust and flexible solution.
+
+## Quick Start
+
+Here's a quick example to show you how easy it is to use Crawl4AI:
+
+```python
+from crawl4ai import WebCrawler
+
+# Create an instance of WebCrawler
+crawler = WebCrawler()
+
+# Warm up the crawler (load necessary models)
+crawler.warmup()
+
+# Run the crawler on a URL
+result = crawler.run(url="https://www.nbcnews.com/business")
+
+# Print the extracted content
+print(result.extracted_content)
+```
+
+### Explanation
+
+1. **Importing the Library**: We start by importing the `WebCrawler` class from the `crawl4ai` library.
+2. **Creating an Instance**: An instance of `WebCrawler` is created.
+3. **Warming Up**: The `warmup()` method prepares the crawler by loading necessary models and settings.
+4. **Running the Crawler**: The `run()` method is used to crawl the specified URL and extract meaningful content.
+5. **Printing the Result**: The extracted content is printed, showcasing the data extracted from the web page.
+
+## Documentation Structure
+
+This documentation is organized into several sections to help you navigate and find the information you need quickly:
+
+### [Home](index.md)
+
+An introduction to Crawl4AI, including a quick start guide and an overview of the documentation structure.
+
+### [Installation](installation.md)
+
+Instructions on how to install Crawl4AI and its dependencies.
+
+### [Introduction](introduction.md)
+
+A detailed introduction to Crawl4AI, its features, and how it can be used for various web crawling and data extraction tasks.
+
+### [Quick Start](quickstart.md)
+
+A step-by-step guide to get you up and running with Crawl4AI, including installation instructions and basic usage examples.
+
+### [Examples](examples/index.md)
+
+This section contains practical examples demonstrating different use cases of Crawl4AI:
+
+- [LLM Extraction](examples/llm_extraction.md)
+- [JS Execution & CSS Filtering](examples/js_execution_css_filtering.md)
+- [Hooks & Auth](examples/hooks_auth.md)
+- [Summarization](examples/summarization.md)
+- [Research Assistant](examples/research_assistant.md)
+
+### [Full Details of Using Crawler](full_details/crawl_request_parameters.md)
+
+Comprehensive details on using the crawler, including:
+
+- [Crawl Request Parameters](full_details/crawl_request_parameters.md)
+- [Crawl Result Class](full_details/crawl_result_class.md)
+- [Advanced Features](full_details/advanced_features.md)
+- [Chunking Strategies](full_details/chunking_strategies.md)
+- [Extraction Strategies](full_details/extraction_strategies.md)
+
+### [API Reference](api/core_classes_and_functions.md)
+
+Detailed documentation of the API, covering:
+
+- [Core Classes and Functions](api/core_classes_and_functions.md)
+- [Detailed API Documentation](api/detailed_api_documentation.md)
+
+### [Change Log](changelog.md)
+
+A log of all changes, updates, and improvements made to Crawl4AI.
+
+### [Contact](contact.md)
+
+Information on how to get in touch with the developers, report issues, and contribute to the project.
+
+## Get Started
+
+To get started with Crawl4AI, follow the quick start guide above or explore the detailed sections of this documentation. Whether you are a beginner or an advanced user, Crawl4AI has something to offer to make your web crawling and data extraction tasks easier and more efficient.
+
+Happy Crawling! 🕸️🚀

docs/md _sync/installation.md

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+# Installation 💻
+
+There are three ways to use Crawl4AI:
+
+1. As a library (Recommended).
+2. As a local server (Docker) or using the REST API.
+3. As a local server (Docker) using the pre-built image from Docker Hub.
+
+## Option 1: Library Installation
+
+You can try this Colab for a quick start: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sJPAmeLj5PMrg2VgOwMJ2ubGIcK0cJeX#scrollTo=g1RrmI4W_rPk)
+
+Crawl4AI offers flexible installation options to suit various use cases. Choose the option that best fits your needs:
+
+- **Default Installation** (Basic functionality):
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+```
+Use this for basic web crawling and scraping tasks.
+
+- **Installation with PyTorch** (For advanced text clustering):
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai[torch] @ git+https://github.com/unclecode/crawl4ai.git"
+```
+Choose this if you need the CosineSimilarity cluster strategy.
+
+- **Installation with Transformers** (For summarization and Hugging Face models):
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai[transformer] @ git+https://github.com/unclecode/crawl4ai.git"
+```
+Opt for this if you require text summarization or plan to use Hugging Face models.
+
+- **Full Installation** (All features):
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai[all] @ git+https://github.com/unclecode/crawl4ai.git"
+```
+This installs all dependencies for full functionality.
+
+- **Development Installation** (For contributors):
+```bash
+virtualenv venv
+source venv/bin/activate
+git clone https://github.com/unclecode/crawl4ai.git
+cd crawl4ai
+pip install -e ".[all]"
+```
+Use this if you plan to modify the source code.
+
+💡 After installation, if you have used "torch", "transformer" or "all", it's recommended to run the following CLI command to load the required models. This is optional but will boost the performance and speed of the crawler. You need to do this only once, this is only for when you install using []
+```bash
+crawl4ai-download-models
+```
+
+## Option 2: Using Docker for Local Server
+
+Crawl4AI can be run as a local server using Docker. The Dockerfile supports different installation options to cater to various use cases. Here's how you can build and run the Docker image:
+
+### Default Installation
+
+The default installation includes the basic Crawl4AI package without additional dependencies or pre-downloaded models.
+
+```bash
+# For Mac users (M1/M2)
+docker build --platform linux/amd64 -t crawl4ai .
+
+# For other users
+docker build -t crawl4ai .
+
+# Run the container
+docker run -d -p 8000:80 crawl4ai
+```
+
+### Full Installation (All Dependencies and Models)
+
+This option installs all dependencies and downloads the models.
+
+```bash
+# For Mac users (M1/M2)
+docker build --platform linux/amd64 --build-arg INSTALL_OPTION=all -t crawl4ai:all .
+
+# For other users
+docker build --build-arg INSTALL_OPTION=all -t crawl4ai:all .
+
+# Run the container
+docker run -d -p 8000:80 crawl4ai:all
+```
+
+### Torch Installation
+
+This option installs torch-related dependencies and downloads the models.
+
+```bash
+# For Mac users (M1/M2)
+docker build --platform linux/amd64 --build-arg INSTALL_OPTION=torch -t crawl4ai:torch .
+
+# For other users
+docker build --build-arg INSTALL_OPTION=torch -t crawl4ai:torch .
+
+# Run the container
+docker run -d -p 8000:80 crawl4ai:torch
+```
+
+### Transformer Installation
+
+This option installs transformer-related dependencies and downloads the models.
+
+```bash
+# For Mac users (M1/M2)
+docker build --platform linux/amd64 --build-arg INSTALL_OPTION=transformer -t crawl4ai:transformer .
+
+# For other users
+docker build --build-arg INSTALL_OPTION=transformer -t crawl4ai:transformer .
+
+# Run the container
+docker run -d -p 8000:80 crawl4ai:transformer
+```
+
+### Notes
+
+- The `--platform linux/amd64` flag is necessary for Mac users with M1/M2 chips to ensure compatibility.
+- The `-t` flag tags the image with a name (and optionally a tag in the 'name:tag' format).
+- The `-d` flag runs the container in detached mode.
+- The `-p 8000:80` flag maps port 8000 on the host to port 80 in the container.
+
+Choose the installation option that best suits your needs. The default installation is suitable for basic usage, while the other options provide additional capabilities for more advanced use cases.
+
+## Option 3: Using the Pre-built Image from Docker Hub
+
+You can use pre-built Crawl4AI images from Docker Hub, which are available for all platforms (Mac, Linux, Windows). We have official images as well as a community-contributed image (Thanks to https://github.com/FractalMind):
+
+### Default Installation
+
+```bash
+
+# Pull the image
+
+docker pull unclecode/crawl4ai:latest
+
+# Run the container
+
+docker run -d -p 8000:80 unclecode/crawl4ai:latest
+
+```
+
+### Community-Contributed Image
+
+A stable version of Crawl4AI is also available, created and maintained by a community member:
+
+```bash
+
+# Pull the community-contributed image
+
+docker pull ryser007/crawl4ai:stable
+
+# Run the container
+
+docker run -d -p 8000:80 ryser007/crawl4ai:stable
+
+```
+
+We'd like to express our gratitude to GitHub user [@FractalMind](https://github.com/FractalMind) for creating and maintaining this stable version of the Crawl4AI Docker image. Community contributions like this are invaluable to the project.
+
+
+### Testing the Installation
+
+After running the container, you can test if it's working correctly:
+
+- On Mac and Linux:
+
+  ```bash
+
+  curl http://localhost:8000
+
+  ```
+
+- On Windows (PowerShell):
+
+  ```powershell
+
+  Invoke-WebRequest -Uri http://localhost:8000
+
+  ```
+
+  Or open a web browser and navigate to http://localhost:8000
+

docs/md _sync/interactive_content.html

@@ -0,0 +1,28 @@
+<h1>Try Our Library</h1>
+<form id="apiForm">
+    <label for="inputField">Enter some input:</label>
+    <input type="text" id="inputField" name="inputField" required>
+    <button type="submit">Submit</button>
+</form>
+<div id="result"></div>
+
+<script>
+    document.getElementById('apiForm').addEventListener('submit', function(event) {
+        event.preventDefault();
+        const input = document.getElementById('inputField').value;
+        fetch('https://your-api-endpoint.com/api', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({ input: input })
+        })
+        .then(response => response.json())
+        .then(data => {
+            document.getElementById('result').textContent = JSON.stringify(data);
+        })
+        .catch(error => {
+            document.getElementById('result').textContent = 'Error: ' + error;
+        });
+    });
+</script>

docs/md _sync/introduction.md

@@ -0,0 +1,29 @@
+# Introduction
+
+Welcome to the documentation for Crawl4AI v0.2.5! 🕷️🤖
+
+Crawl4AI is designed to simplify the process of crawling web pages and extracting useful information for large language models (LLMs) and AI applications. Whether you're using it as a REST API, a Python library, or through a Google Colab notebook, Crawl4AI provides powerful features to make web data extraction easier and more efficient.
+
+## Key Features ✨
+
+- **🆓 Completely Free and Open-Source**: Crawl4AI is free to use and open-source, making it accessible for everyone.
+- **🤖 LLM-Friendly Output Formats**: Supports JSON, cleaned HTML, and markdown formats.
+- **🌍 Concurrent Crawling**: Crawl multiple URLs simultaneously to save time.
+- **🎨 Media Extraction**: Extract all media tags including images, audio, and video.
+- **🔗 Link Extraction**: Extract all external and internal links from web pages.
+- **📚 Metadata Extraction**: Extract metadata from web pages for additional context.
+- **🔄 Custom Hooks**: Define custom hooks for authentication, headers, and page modifications before crawling.
+- **🕵️ User Agent Support**: Customize the user agent for HTTP requests.
+- **🖼️ Screenshot Capability**: Take screenshots of web pages during crawling.
+- **📜 JavaScript Execution**: Execute custom JavaScripts before crawling.
+- **📚 Advanced Chunking and Extraction Strategies**: Utilize topic-based, regex, sentence chunking, cosine clustering, and LLM extraction strategies.
+- **🎯 CSS Selector Support**: Extract specific content using CSS selectors.
+- **📝 Instruction/Keyword Refinement**: Pass instructions or keywords to refine the extraction process.
+
+Check the [Changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for more details.
+
+## Power and Simplicity of Crawl4AI 🚀
+
+Crawl4AI provides an easy way to crawl and extract data from web pages without installing any library. You can use the REST API on our server or run the local server on your machine. For more advanced control, use the Python library to customize your crawling and extraction strategies.
+
+Explore the documentation to learn more about the features, installation process, usage examples, and how to contribute to Crawl4AI. Let's make the web more accessible and useful for AI applications! 💪🌐🤖

docs/md _sync/quickstart.md

@@ -0,0 +1,204 @@
+# Quick Start Guide 🚀
+
+Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies. Let's dive in! 🌟
+
+## Getting Started 🛠️
+
+First, let's create an instance of `WebCrawler` and call the `warmup()` function. This might take a few seconds the first time you run Crawl4AI, as it loads the required model files.
+
+```python
+from crawl4ai import WebCrawler
+
+def create_crawler():
+    crawler = WebCrawler(verbose=True)
+    crawler.warmup()
+    return crawler
+
+crawler = create_crawler()
+```
+
+### Basic Usage
+
+Simply provide a URL and let Crawl4AI do the magic!
+
+```python
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(f"Basic crawl result: {result}")
+```
+
+### Taking Screenshots 📸
+
+Let's take a screenshot of the page!
+
+```python
+result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result.screenshot))
+print("Screenshot saved to 'screenshot.png'!")
+```
+
+### Understanding Parameters 🧠
+
+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 (caches the result):
+```python
+result = crawler.run(url="https://www.nbcnews.com/business")
+print(f"First crawl result: {result}")
+```
+
+Force to crawl again:
+```python
+result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
+print(f"Second crawl result: {result}")
+```
+
+### Adding a Chunking Strategy 🧩
+
+Let's add a chunking strategy: `RegexChunking`! This strategy splits the text based on a given regex pattern.
+
+```python
+from crawl4ai.chunking_strategy import RegexChunking
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    chunking_strategy=RegexChunking(patterns=["\n\n"])
+)
+print(f"RegexChunking result: {result}")
+```
+
+You can also use `NlpSentenceChunking` which splits the text into sentences using NLP techniques.
+
+```python
+from crawl4ai.chunking_strategy import NlpSentenceChunking
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    chunking_strategy=NlpSentenceChunking()
+)
+print(f"NlpSentenceChunking result: {result}")
+```
+
+### Adding an Extraction Strategy 🧠
+
+Let's get smarter with an extraction strategy: `CosineStrategy`! This strategy uses cosine similarity to extract semantically similar blocks of text.
+
+```python
+from crawl4ai.extraction_strategy import CosineStrategy
+
+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
+    )
+)
+print(f"CosineStrategy result: {result}")
+```
+
+You can also pass other parameters like `semantic_filter` to extract specific content.
+
+```python
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=CosineStrategy(
+        semantic_filter="inflation rent prices"
+    )
+)
+print(f"CosineStrategy result with semantic filter: {result}")
+```
+
+### Using LLMExtractionStrategy 🤖
+
+Time to bring in the big guns: `LLMExtractionStrategy` without instructions! This strategy uses a large language model to extract relevant information from the web page.
+
+```python
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+import os
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=LLMExtractionStrategy(
+        provider="openai/gpt-4o", 
+        api_token=os.getenv('OPENAI_API_KEY')
+    )
+)
+print(f"LLMExtractionStrategy (no instructions) result: {result}")
+```
+
+You can also provide specific instructions to guide the extraction.
+
+```python
+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"
+    )
+)
+print(f"LLMExtractionStrategy (with instructions) result: {result}")
+```
+
+### Targeted Extraction 🎯
+
+Let's use a CSS selector to extract only H2 tags!
+
+```python
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    css_selector="h2"
+)
+print(f"CSS Selector (H2 tags) result: {result}")
+```
+
+### Interactive Extraction 🖱️
+
+Passing JavaScript code to click the 'Load More' button!
+
+```python
+js_code = """
+const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+loadMoreButton && loadMoreButton.click();
+"""
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    js=js_code
+)
+print(f"JavaScript Code (Load More button) result: {result}")
+```
+
+### Using Crawler Hooks 🔗
+
+Let's see how we can customize the crawler using hooks!
+
+```python
+import time
+
+from crawl4ai.web_crawler import WebCrawler
+from crawl4ai.crawler_strategy import *
+
+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
+
+crawler = create_crawler()
+result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
+```
+
+check [Hooks](examples/hooks_auth.md) for more examples.
+
+## Congratulations! 🎉
+
+You've made it through the Crawl4AI Quickstart Guide! Now go forth and crawl the web like a pro! 🕸️

docs/md/examples/hooks_auth.md

@@ -1,100 +1,110 @@
-# Hooks & Auth
+# Hooks & Auth for AsyncWebCrawler
 
-Crawl4AI allows you to customize the behavior of the web crawler using hooks. Hooks are functions that are called at specific points in the crawling process, allowing you to modify the crawler's behavior or perform additional actions. This example demonstrates how to use various hooks to customize the crawling process.
+Crawl4AI's AsyncWebCrawler allows you to customize the behavior of the web crawler using hooks. Hooks are asynchronous functions that are called at specific points in the crawling process, allowing you to modify the crawler's behavior or perform additional actions. This example demonstrates how to use various hooks to customize the asynchronous crawling process.
 
-## Example: Using Crawler Hooks
+## Example: Using Crawler Hooks with AsyncWebCrawler
 
-Let's see how we can customize the crawler using hooks! In this example, we'll:
+Let's see how we can customize the AsyncWebCrawler using hooks! In this example, we'll:
 
-1. Maximize the browser window and log in to a website when the driver is created.
-2. Add a custom header before fetching the URL.
-3. Log the current URL after fetching it.
-4. Log the length of the HTML before returning it.
+1. Configure the browser when it's created.
+2. Add custom headers before navigating to the URL.
+3. Log the current URL after navigation.
+4. Perform actions after JavaScript execution.
+5. Log the length of the HTML before returning it.
 
 ### Hook Definitions
 
 ```python
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.crawler_strategy import *
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
+from playwright.async_api import Page, Browser
 
-def on_driver_created(driver):
-    print("[HOOK] on_driver_created")
-    # Example customization: maximize the window
-    driver.maximize_window()
+async def on_browser_created(browser: Browser):
+    print("[HOOK] on_browser_created")
+    # Example customization: set browser viewport size
+    context = await browser.new_context(viewport={'width': 1920, 'height': 1080})
+    page = await context.new_page()
     
     # Example customization: logging in to a hypothetical website
-    driver.get('https://example.com/login')
+    await page.goto('https://example.com/login')
+    await page.fill('input[name="username"]', 'testuser')
+    await page.fill('input[name="password"]', 'password123')
+    await page.click('button[type="submit"]')
+    await page.wait_for_selector('#welcome')
     
-    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        
+    await context.add_cookies([{'name': 'test_cookie', 'value': 'cookie_value', 'url': 'https://example.com'}])
     
+    await page.close()
+    await context.close()
 
-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
+async def before_goto(page: Page):
+    print("[HOOK] before_goto")
+    # Example customization: add custom headers
+    await page.set_extra_http_headers({'X-Test-Header': 'test'})
 
-def after_get_url(driver):
-    print("[HOOK] after_get_url")
+async def after_goto(page: Page):
+    print("[HOOK] after_goto")
     # Example customization: log the URL
-    print(driver.current_url)
-    return driver
+    print(f"Current URL: {page.url}")
 
-def before_return_html(driver, html):
+async def on_execution_started(page: Page):
+    print("[HOOK] on_execution_started")
+    # Example customization: perform actions after JS execution
+    await page.evaluate("console.log('Custom JS executed')")
+
+async def before_return_html(page: Page, html: str):
     print("[HOOK] before_return_html")
-    # Example customization: log the HTML
-    print(len(html))
-    return driver
+    # Example customization: log the HTML length
+    print(f"HTML length: {len(html)}")
+    return page
 ```
 
-### Using the Hooks with the WebCrawler
+### Using the Hooks with the AsyncWebCrawler
 
 ```python
-print("\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()
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
 
-result = crawler.run(url="https://example.com")
+async def main():
+    print("\n🔗 Using Crawler Hooks: Let's see how we can customize the AsyncWebCrawler using hooks!")
     
-print("[LOG] 📦 [bold yellow]Crawler Hooks result:[/bold yellow]")
-print(result)
+    crawler_strategy = AsyncPlaywrightCrawlerStrategy(verbose=True)
+    crawler_strategy.set_hook('on_browser_created', on_browser_created)
+    crawler_strategy.set_hook('before_goto', before_goto)
+    crawler_strategy.set_hook('after_goto', after_goto)
+    crawler_strategy.set_hook('on_execution_started', on_execution_started)
+    crawler_strategy.set_hook('before_return_html', before_return_html)
+    
+    async with AsyncWebCrawler(verbose=True, crawler_strategy=crawler_strategy) as crawler:
+        result = await crawler.arun(
+            url="https://example.com",
+            js_code="window.scrollTo(0, document.body.scrollHeight);",
+            wait_for="footer"
+        )
+
+    print("📦 Crawler Hooks result:")
+    print(result)
+
+asyncio.run(main())
 ```
 
 ### Explanation
 
-- `on_driver_created`: This hook is called when the Selenium driver is created. In this example, it maximizes the window, logs in to a website, and adds a custom cookie.
-- `before_get_url`: This hook is called right before Selenium fetches the URL. In this example, it adds a custom HTTP header.
-- `after_get_url`: This hook is called after Selenium fetches the URL. In this example, it logs the current URL.
-- `before_return_html`: This hook is called before returning the HTML content. In this example, it logs the length of the HTML content.
+- `on_browser_created`: This hook is called when the Playwright browser is created. It sets up the browser context, logs in to a website, and adds a custom cookie.
+- `before_goto`: This hook is called right before Playwright navigates to the URL. It adds custom HTTP headers.
+- `after_goto`: This hook is called after Playwright navigates to the URL. It logs the current URL.
+- `on_execution_started`: This hook is called after any custom JavaScript is executed. It performs additional JavaScript actions.
+- `before_return_html`: This hook is called before returning the HTML content. It logs the length of the HTML content.
 
 ### Additional Ideas
 
-- **Add custom headers to requests**: You can add custom headers to the requests using the `before_get_url` hook.
-- **Perform safety checks**: Use the hooks to perform safety checks before the crawling process starts.
-- **Modify the HTML content**: Use the `before_return_html` hook to modify the HTML content before it is returned.
-- **Log additional information**: Use the hooks to log additional information for debugging or monitoring purposes.
+- **Handling authentication**: Use the `on_browser_created` hook to handle login processes or set authentication tokens.
+- **Dynamic header modification**: Modify headers based on the target URL or other conditions in the `before_goto` hook.
+- **Content verification**: Use the `after_goto` hook to verify that the expected content is present on the page.
+- **Custom JavaScript injection**: Inject and execute custom JavaScript using the `on_execution_started` hook.
+- **Content preprocessing**: Modify or analyze the HTML content in the `before_return_html` hook before it's returned.
 
-By using these hooks, you can customize the behavior of the crawler to suit your specific needs.
+By using these hooks, you can customize the behavior of the AsyncWebCrawler to suit your specific needs, including handling authentication, modifying requests, and preprocessing content.

docs/md/examples/index.md

@@ -8,6 +8,10 @@ Welcome to the examples section of Crawl4AI documentation! In this section, you
 
 This example demonstrates how to use Crawl4AI to extract information using Large Language Models (LLMs). You will learn how to configure the `LLMExtractionStrategy` to get structured data from web pages.
 
+### [JSON CSS Extraction](json_css_extraction.md)
+
+This example demonstrates how to use Crawl4AI to extract structured data without using LLM, and just focusing on page structure. You will learn how to use the `JsonCssExtractionStrategy` to extract data using CSS selectors.
+
 ### [JS Execution & CSS Filtering](js_execution_css_filtering.md)
 
 Learn how to execute custom JavaScript code and filter data using CSS selectors. This example shows how to perform complex web interactions and extract specific content from web pages.

docs/md/examples/js_execution_css_filtering.md

@@ -1,44 +1,104 @@
-# JS Execution & CSS Filtering
+# JS Execution & CSS Filtering with AsyncWebCrawler
 
-In this example, we'll demonstrate how to use Crawl4AI to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
+In this example, we'll demonstrate how to use Crawl4AI's AsyncWebCrawler to execute JavaScript, filter data with CSS selectors, and use a cosine similarity strategy to extract relevant content. This approach is particularly useful when you need to interact with dynamic content on web pages, such as clicking "Load More" buttons.
 
-## Example: Extracting Structured Data
+## Example: Extracting Structured Data Asynchronously
 
 ```python
-# Import necessary modules
-from crawl4ai import WebCrawler
-from crawl4ai.chunking_strategy import *
-from crawl4ai.extraction_strategy import *
-from crawl4ai.crawler_strategy import *
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.chunking_strategy import RegexChunking
+from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
 
-# Define the JavaScript code to click the "Load More" button
-js_code = ["""
-const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
-loadMoreButton && loadMoreButton.click();
-"""]
+async def main():
+    # Define the JavaScript code to click the "Load More" button
+    js_code = """
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+    if (loadMoreButton) {
+        loadMoreButton.click();
+        // Wait for new content to load
+        await new Promise(resolve => setTimeout(resolve, 2000));
+    }
+    """
 
-crawler = WebCrawler(verbose=True)
-crawler.warmup()
-# Run the crawler with keyword filtering and CSS selector
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    js=js_code,
-    css_selector="p",
-    extraction_strategy=CosineStrategy(
-        semantic_filter="technology",
-    ),
-)
+    # Define a wait_for function to ensure content is loaded
+    wait_for = """
+    () => {
+        const articles = document.querySelectorAll('article.tease-card');
+        return articles.length > 10;
+    }
+    """
 
-# Display the extracted result
-print(result)
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Run the crawler with keyword filtering and CSS selector
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            extraction_strategy=CosineStrategy(
+                semantic_filter="technology",
+            ),
+            chunking_strategy=RegexChunking(),
+        )
+
+    # Display the extracted result
+    print(result.extracted_content)
+
+# Run the async function
+asyncio.run(main())
 ```
 
 ### Explanation
 
-1. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button. This is useful for loading additional content dynamically.
-2. **CSS Selector**: The `css_selector="p"` parameter ensures that only paragraph (`<p>`) tags are extracted from the web page.
-3. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
+1. **Asynchronous Execution**: We use `AsyncWebCrawler` with async/await syntax for non-blocking execution.
+
+2. **JavaScript Execution**: The `js_code` variable contains JavaScript code that simulates clicking a "Load More" button and waits for new content to load.
+
+3. **Wait Condition**: The `wait_for` function ensures that the page has loaded more than 10 articles before proceeding with the extraction.
+
+4. **CSS Selector**: The `css_selector="article.tease-card"` parameter ensures that only article cards are extracted from the web page.
+
+5. **Extraction Strategy**: The `CosineStrategy` is used with a semantic filter for "technology" to extract relevant content based on cosine similarity.
+
+6. **Chunking Strategy**: We use `RegexChunking()` to split the content into manageable chunks for processing.
+
+## Advanced Usage: Custom Session and Multiple Requests
+
+For more complex scenarios where you need to maintain state across multiple requests or execute additional JavaScript after the initial page load, you can use a custom session:
+
+```python
+async def advanced_crawl():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Initial crawl with custom session
+        result1 = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            session_id="business_session"
+        )
+
+        # Execute additional JavaScript in the same session
+        result2 = await crawler.crawler_strategy.execute_js(
+            session_id="business_session",
+            js_code="window.scrollTo(0, document.body.scrollHeight);",
+            wait_for_js="() => window.innerHeight + window.scrollY >= document.body.offsetHeight"
+        )
+
+        # Process results
+        print("Initial crawl result:", result1.extracted_content)
+        print("Additional JS execution result:", result2.html)
+
+asyncio.run(advanced_crawl())
+```
+
+This advanced example demonstrates how to:
+1. Use a custom session to maintain state across requests.
+2. Execute additional JavaScript after the initial page load.
+3. Wait for specific conditions using JavaScript functions.
 
 ## Try It Yourself
 
-This example demonstrates the power and flexibility of Crawl4AI in handling complex web interactions and extracting meaningful data. You can customize the JavaScript code, CSS selectors, and extraction strategies to suit your specific requirements.
+These examples demonstrate the power and flexibility of Crawl4AI's AsyncWebCrawler in handling complex web interactions and extracting meaningful data asynchronously. You can customize the JavaScript code, CSS selectors, extraction strategies, and waiting conditions to suit your specific requirements.

docs/md/examples/json_css_extraction.md

@@ -0,0 +1,142 @@
+# JSON CSS Extraction Strategy with AsyncWebCrawler
+
+The `JsonCssExtractionStrategy` is a powerful feature of Crawl4AI that allows you to extract structured data from web pages using CSS selectors. This method is particularly useful when you need to extract specific data points from a consistent HTML structure, such as tables or repeated elements. Here's how to use it with the AsyncWebCrawler.
+
+## Overview
+
+The `JsonCssExtractionStrategy` works by defining a schema that specifies:
+1. A base CSS selector for the repeating elements
+2. Fields to extract from each element, each with its own CSS selector
+
+This strategy is fast and efficient, as it doesn't rely on external services like LLMs for extraction.
+
+## Example: Extracting Cryptocurrency Prices from Coinbase
+
+Let's look at an example that extracts cryptocurrency prices from the Coinbase explore page.
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def extract_structured_data_using_css_extractor():
+    print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")
+    
+    # Define the extraction schema
+    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",
+            }
+        ],
+    }
+
+    # Create the extraction strategy
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    # Use the AsyncWebCrawler with the extraction strategy
+    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"
+
+        # Parse the extracted content
+        crypto_prices = json.loads(result.extracted_content)
+        print(f"Successfully extracted {len(crypto_prices)} cryptocurrency prices")
+        print(json.dumps(crypto_prices[0], indent=2))
+
+    return crypto_prices
+
+# Run the async function
+asyncio.run(extract_structured_data_using_css_extractor())
+```
+
+## Explanation of the Schema
+
+The schema defines how to extract the data:
+
+- `name`: A descriptive name for the extraction task.
+- `baseSelector`: The CSS selector for the repeating elements (in this case, table rows).
+- `fields`: An array of fields to extract from each element:
+  - `name`: The name to give the extracted data.
+  - `selector`: The CSS selector to find the specific data within the base element.
+  - `type`: The type of data to extract (usually "text" for textual content).
+
+## Advantages of JsonCssExtractionStrategy
+
+1. **Speed**: CSS selectors are fast to execute, making this method efficient for large datasets.
+2. **Precision**: You can target exactly the elements you need.
+3. **Structured Output**: The result is already structured as JSON, ready for further processing.
+4. **No External Dependencies**: Unlike LLM-based strategies, this doesn't require any API calls to external services.
+
+## Tips for Using JsonCssExtractionStrategy
+
+1. **Inspect the Page**: Use browser developer tools to identify the correct CSS selectors.
+2. **Test Selectors**: Verify your selectors in the browser console before using them in the script.
+3. **Handle Dynamic Content**: If the page uses JavaScript to load content, you may need to combine this with JS execution (see the Advanced Usage section).
+4. **Error Handling**: Always check the `result.success` flag and handle potential failures.
+
+## Advanced Usage: Combining with JavaScript Execution
+
+For pages that load data dynamically, you can combine the `JsonCssExtractionStrategy` with JavaScript execution:
+
+```python
+async def extract_dynamic_structured_data():
+    schema = {
+        "name": "Dynamic Crypto Prices",
+        "baseSelector": ".crypto-row",
+        "fields": [
+            {"name": "name", "selector": ".crypto-name", "type": "text"},
+            {"name": "price", "selector": ".crypto-price", "type": "text"},
+        ]
+    }
+
+    js_code = """
+    window.scrollTo(0, document.body.scrollHeight);
+    await new Promise(resolve => setTimeout(resolve, 2000));  // Wait for 2 seconds
+    """
+
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://example.com/crypto-prices",
+            extraction_strategy=extraction_strategy,
+            js_code=js_code,
+            wait_for=".crypto-row:nth-child(20)",  # Wait for 20 rows to load
+            bypass_cache=True,
+        )
+
+        crypto_data = json.loads(result.extracted_content)
+        print(f"Extracted {len(crypto_data)} cryptocurrency entries")
+
+asyncio.run(extract_dynamic_structured_data())
+```
+
+This advanced example demonstrates how to:
+1. Execute JavaScript to trigger dynamic content loading.
+2. Wait for a specific condition (20 rows loaded) before extraction.
+3. Extract data from the dynamically loaded content.
+
+By mastering the `JsonCssExtractionStrategy`, you can efficiently extract structured data from a wide variety of web pages, making it a valuable tool in your web scraping toolkit.
+
+For more details on schema definitions and advanced extraction strategies, check out the[Advanced JsonCssExtraction](../full_details/advanced_jsoncss_extraction.md).

docs/md/examples/llm_extraction.md

@@ -1,6 +1,6 @@
-# LLM Extraction
+# LLM Extraction with AsyncWebCrawler
 
-Crawl4AI allows you to use Language Models (LLMs) to extract structured data or relevant content from web pages. Below are two examples demonstrating how to use LLMExtractionStrategy for different purposes.
+Crawl4AI's AsyncWebCrawler allows you to use Language Models (LLMs) to extract structured data or relevant content from web pages asynchronously. Below are two examples demonstrating how to use `LLMExtractionStrategy` for different purposes with the AsyncWebCrawler.
 
 ## Example 1: Extract Structured Data
 
@@ -8,17 +8,10 @@ In this example, we use the `LLMExtractionStrategy` to extract structured data (
 
 ```python
 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 *
-
-url = r'https://openai.com/api/pricing/'
-
-crawler = WebCrawler()
-crawler.warmup()
-
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
 from pydantic import BaseModel, Field
 
 class OpenAIModelFee(BaseModel):
@@ -26,27 +19,33 @@ class OpenAIModelFee(BaseModel):
     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.")
 
-result = crawler.run(
-    url=url,
-    word_count_threshold=1,
-    extraction_strategy= LLMExtractionStrategy(
-        provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
-        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. Make sure not to miss anything 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" }'
-    ),
-    bypass_cache=True,
-)
+async def extract_openai_fees():
+    url = 'https://openai.com/api/pricing/'
 
-model_fees = json.loads(result.extracted_content)
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url=url,
+            word_count_threshold=1,
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o",
+                api_token=os.getenv('OPENAI_API_KEY'),
+                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. Make sure not to miss anything 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" }'
+            ),
+            bypass_cache=True,
+        )
 
-print(len(model_fees))
+    model_fees = json.loads(result.extracted_content)
+    print(f"Number of models extracted: {len(model_fees)}")
 
-with open(".data/data.json", "w", encoding="utf-8") as f:
-    f.write(result.extracted_content)
+    with open(".data/openai_fees.json", "w", encoding="utf-8") as f:
+        json.dump(model_fees, f, indent=2)
+
+asyncio.run(extract_openai_fees())
 ```
 
 ## Example 2: Extract Relevant Content
@@ -54,30 +53,80 @@ with open(".data/data.json", "w", encoding="utf-8") as f:
 In this example, we instruct the LLM to extract only content related to technology from the NBC News business page.
 
 ```python
-crawler = WebCrawler()
-crawler.warmup()
+import os
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
 
-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"
-        ),
-    bypass_cache=True,
-    )
+async def extract_tech_content():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            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"
+            ),
+            bypass_cache=True,
+        )
 
-model_fees = json.loads(result.extracted_content)
+    tech_content = json.loads(result.extracted_content)
+    print(f"Number of tech-related items extracted: {len(tech_content)}")
 
-print(len(model_fees))
+    with open(".data/tech_content.json", "w", encoding="utf-8") as f:
+        json.dump(tech_content, f, indent=2)
 
-with open(".data/data.json", "w", encoding="utf-8") as f:
-    f.write(result.extracted_content)
+asyncio.run(extract_tech_content())
+```
+
+## Advanced Usage: Combining JS Execution with LLM Extraction
+
+This example demonstrates how to combine JavaScript execution with LLM extraction to handle dynamic content:
+
+```python
+async def extract_dynamic_content():
+    js_code = """
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+    if (loadMoreButton) {
+        loadMoreButton.click();
+        await new Promise(resolve => setTimeout(resolve, 2000));
+    }
+    """
+
+    wait_for = """
+    () => {
+        const articles = document.querySelectorAll('article.tease-card');
+        return articles.length > 10;
+    }
+    """
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            extraction_strategy=LLMExtractionStrategy(
+                provider="openai/gpt-4o",
+                api_token=os.getenv('OPENAI_API_KEY'),
+                instruction="Summarize each article, focusing on technology-related content"
+            ),
+            bypass_cache=True,
+        )
+
+    summaries = json.loads(result.extracted_content)
+    print(f"Number of summarized articles: {len(summaries)}")
+
+    with open(".data/tech_summaries.json", "w", encoding="utf-8") as f:
+        json.dump(summaries, f, indent=2)
+
+asyncio.run(extract_dynamic_content())
 ```
 
 ## Customizing LLM Provider
 
-Under the hood, Crawl4AI uses the `litellm` library, which allows you to use any LLM provider you want. Just pass the correct model name and API token.
+Crawl4AI uses the `litellm` library under the hood, which allows you to use any LLM provider you want. Just pass the correct model name and API token:
 
 ```python
 extraction_strategy=LLMExtractionStrategy(
@@ -88,3 +137,43 @@ extraction_strategy=LLMExtractionStrategy(
 ```
 
 This flexibility allows you to integrate with various LLM providers and tailor the extraction process to your specific needs.
+
+## Error Handling and Retries
+
+When working with external LLM APIs, it's important to handle potential errors and implement retry logic. Here's an example of how you might do this:
+
+```python
+import asyncio
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+class LLMExtractionError(Exception):
+    pass
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+async def extract_with_retry(crawler, url, extraction_strategy):
+    try:
+        result = await crawler.arun(url=url, extraction_strategy=extraction_strategy, bypass_cache=True)
+        return json.loads(result.extracted_content)
+    except Exception as e:
+        raise LLMExtractionError(f"Failed to extract content: {str(e)}")
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        try:
+            content = await extract_with_retry(
+                crawler,
+                "https://www.example.com",
+                LLMExtractionStrategy(
+                    provider="openai/gpt-4o",
+                    api_token=os.getenv('OPENAI_API_KEY'),
+                    instruction="Extract and summarize main points"
+                )
+            )
+            print("Extracted content:", content)
+        except LLMExtractionError as e:
+            print(f"Extraction failed after retries: {e}")
+
+asyncio.run(main())
+```
+
+This example uses the `tenacity` library to implement a retry mechanism with exponential backoff, which can help handle temporary failures or rate limiting from the LLM API.

docs/md/examples/research_assistant.md

@@ -1,33 +1,32 @@
-## Research Assistant Example
+# Research Assistant Example with AsyncWebCrawler
 
-This example demonstrates how to build a research assistant using `Chainlit` and `Crawl4AI`. The assistant will be capable of crawling web pages for information and answering questions based on the crawled content. Additionally, it integrates speech-to-text functionality for audio inputs.
+This example demonstrates how to build an advanced research assistant using `Chainlit`, `Crawl4AI`'s `AsyncWebCrawler`, and various AI services. The assistant can crawl web pages asynchronously, answer questions based on the crawled content, and handle audio inputs.
 
-### Step-by-Step Guide
+## Step-by-Step Guide
 
 1. **Install Required Packages**
 
-    Ensure you have the necessary packages installed. You need `chainlit`, `groq`, `requests`, and `openai`.
+    Ensure you have the necessary packages installed:
 
     ```bash
-    pip install chainlit groq requests openai
+    pip install chainlit groq openai crawl4ai
     ```
 
 2. **Import Libraries**
 
-    Import all the necessary modules and initialize the OpenAI client.
-
     ```python
     import os
     import time
+    import asyncio
     from openai import AsyncOpenAI
     import chainlit as cl
     import re
-    import requests
     from io import BytesIO
     from chainlit.element import ElementBased
     from groq import Groq
-
-    from concurrent.futures import ThreadPoolExecutor
+    from crawl4ai import AsyncWebCrawler
+    from crawl4ai.extraction_strategy import NoExtractionStrategy
+    from crawl4ai.chunking_strategy import RegexChunking
 
     client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
 
@@ -37,8 +36,6 @@ This example demonstrates how to build a research assistant using `Chainlit` and
 
 3. **Set Configuration**
 
-    Define the model settings for the assistant.
-
     ```python
     settings = {
         "model": "llama3-8b-8192",
@@ -52,35 +49,25 @@ This example demonstrates how to build a research assistant using `Chainlit` and
 
 4. **Define Utility Functions**
 
-    - **Extract URLs from Text**: Use regex to find URLs in messages.
+    ```python
+    def extract_urls(text):
+        url_pattern = re.compile(r'(https?://\S+)')
+        return url_pattern.findall(text)
 
-        ```python
-        def extract_urls(text):
-            url_pattern = re.compile(r'(https?://\S+)')
-            return url_pattern.findall(text)
-        ```
-
-    - **Crawl URL**: Send a request to `Crawl4AI` to fetch the content of a URL.
-
-        ```python
-        def crawl_url(url):
-            data = {
-                "urls": [url],
-                "include_raw_html": True,
-                "word_count_threshold": 10,
-                "extraction_strategy": "NoExtractionStrategy",
-                "chunking_strategy": "RegexChunking"
-            }
-            response = requests.post("https://crawl4ai.com/crawl", json=data)
-            response_data = response.json()
-            response_data = response_data['results'][0]
-            return response_data['markdown']
-        ```
+    async def crawl_urls(urls):
+        async with AsyncWebCrawler(verbose=True) as crawler:
+            results = await crawler.arun_many(
+                urls=urls,
+                word_count_threshold=10,
+                extraction_strategy=NoExtractionStrategy(),
+                chunking_strategy=RegexChunking(),
+                bypass_cache=True
+            )
+        return [result.markdown for result in results if result.success]
+    ```
 
 5. **Initialize Chat Start Event**
 
-    Set up the initial chat message and user session.
-
     ```python
     @cl.on_chat_start
     async def on_chat_start():
@@ -88,15 +75,11 @@ This example demonstrates how to build a research assistant using `Chainlit` and
             "history": [],
             "context": {}
         })  
-        await cl.Message(
-            content="Welcome to the chat! How can I assist you today?"
-        ).send()
+        await cl.Message(content="Welcome to the chat! How can I assist you today?").send()
     ```
 
 6. **Handle Incoming Messages**
 
-    Process user messages, extract URLs, and crawl them concurrently. Update the chat history and system message.
-
     ```python
     @cl.on_message
     async def on_message(message: cl.Message):
@@ -105,19 +88,14 @@ This example demonstrates how to build a research assistant using `Chainlit` and
         # Extract URLs from the user's message
         urls = extract_urls(message.content)
 
-        futures = []
-        with ThreadPoolExecutor() as executor:
-            for url in urls:
-                futures.append(executor.submit(crawl_url, url))
-
-        results = [future.result() for future in futures]
-
-        for url, result in zip(urls, results):
-            ref_number = f"REF_{len(user_session['context']) + 1}"
-            user_session["context"][ref_number] = {
-                "url": url,
-                "content": result
-            }    
+        if urls:
+            crawled_contents = await crawl_urls(urls)
+            for url, content in zip(urls, crawled_contents):
+                ref_number = f"REF_{len(user_session['context']) + 1}"
+                user_session["context"][ref_number] = {
+                    "url": url,
+                    "content": content
+                }
 
         user_session["history"].append({
             "role": "user",
@@ -129,33 +107,24 @@ This example demonstrates how to build a research assistant using `Chainlit` and
             f'<appendix ref="{ref}">\n{data["content"]}\n</appendix>'
             for ref, data in user_session["context"].items()
         ]
-        if context_messages:
-            system_message = {
-                "role": "system",
-                "content": (
-                    "You are a helpful bot. Use the following context for answering questions. "
-                    "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
-                    "If the question requires any information from the provided appendices or context, refer to the sources. "
-                    "If not, there is no need to add a references section. "
-                    "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
-                    "\n\n".join(context_messages)
-                )
-            }
-        else:
-            system_message = {
-                "role": "system",
-                "content": "You are a helpful assistant."
-            }
+        system_message = {
+            "role": "system",
+            "content": (
+                "You are a helpful bot. Use the following context for answering questions. "
+                "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
+                "If the question requires any information from the provided appendices or context, refer to the sources. "
+                "If not, there is no need to add a references section. "
+                "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
+                "\n\n".join(context_messages)
+            ) if context_messages else "You are a helpful assistant."
+        }
 
         msg = cl.Message(content="")
         await msg.send()
 
         # Get response from the LLM
         stream = await client.chat.completions.create(
-            messages=[
-                system_message,
-                *user_session["history"]
-            ],
+            messages=[system_message, *user_session["history"]],
             stream=True,
             **settings
         )
@@ -174,18 +143,16 @@ This example demonstrates how to build a research assistant using `Chainlit` and
         await msg.update()
 
         # Append the reference section to the assistant's response
-        reference_section = "\n\nReferences:\n"
-        for ref, data in user_session["context"].items():
-            reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
-
-        msg.content += reference_section
-        await msg.update()
+        if user_session["context"]:
+            reference_section = "\n\nReferences:\n"
+            for ref, data in user_session["context"].items():
+                reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
+            msg.content += reference_section
+            await msg.update()
     ```
 
 7. **Handle Audio Input**
 
-    Capture and transcribe audio input. Store the audio buffer and transcribe it when the audio ends.
-
     ```python
     @cl.on_audio_chunk
     async def on_audio_chunk(chunk: cl.AudioChunk):
@@ -194,12 +161,10 @@ This example demonstrates how to build a research assistant using `Chainlit` and
             buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
             cl.user_session.set("audio_buffer", buffer)
             cl.user_session.set("audio_mime_type", chunk.mimeType)
-
         cl.user_session.get("audio_buffer").write(chunk.data)
 
     @cl.step(type="tool")
     async def speech_to_text(audio_file):
-        cli = Groq()
         response = await client.audio.transcriptions.create(
             model="whisper-large-v3", file=audio_file
         )
@@ -217,32 +182,39 @@ This example demonstrates how to build a research assistant using `Chainlit` and
         end_time = time.time()
         print(f"Transcription took {end_time - start_time} seconds")
         
-        user_msg = cl.Message(
-            author="You", 
-            type="user_message",
-            content=transcription
-        )
+        user_msg = cl.Message(author="You", type="user_message", content=transcription)
         await user_msg.send()
         await on_message(user_msg)
     ```
 
 8. **Run the Chat Application**
 
-    Start the Chainlit application.
-
     ```python
     if __name__ == "__main__":
         from chainlit.cli import run_chainlit
         run_chainlit(__file__)
     ```
 
-### Explanation
+## Explanation
 
-- **Libraries and Configuration**: Import necessary libraries and configure the OpenAI client.
-- **Utility Functions**: Define functions to extract URLs and crawl them.
-- **Chat Start Event**: Initialize chat session and welcome message.
-- **Message Handling**: Extract URLs, crawl them concurrently, and update chat history and context.
-- **Audio Handling**: Capture, buffer, and transcribe audio input, then process the transcription as text.
-- **Running the Application**: Start the Chainlit server to interact with the assistant.
+- **Libraries and Configuration**: We import necessary libraries, including `AsyncWebCrawler` from `crawl4ai`.
+- **Utility Functions**: 
+  - `extract_urls`: Uses regex to find URLs in messages.
+  - `crawl_urls`: An asynchronous function that uses `AsyncWebCrawler` to fetch content from multiple URLs concurrently.
+- **Chat Start Event**: Initializes the chat session and sends a welcome message.
+- **Message Handling**: 
+  - Extracts URLs from user messages.
+  - Asynchronously crawls the URLs using `AsyncWebCrawler`.
+  - Updates chat history and context with crawled content.
+  - Generates a response using the LLM, incorporating the crawled context.
+- **Audio Handling**: Captures, buffers, and transcribes audio input, then processes the transcription as text.
+- **Running the Application**: Starts the Chainlit server for interaction with the assistant.
 
-This example showcases how to create an interactive research assistant that can fetch, process, and summarize web content, along with handling audio inputs for a seamless user experience.
+## Key Improvements
+
+1. **Asynchronous Web Crawling**: Using `AsyncWebCrawler` allows for efficient, concurrent crawling of multiple URLs.
+2. **Improved Context Management**: The assistant now maintains a context of crawled content, allowing for more informed responses.
+3. **Dynamic Reference System**: The assistant can refer to specific sources in its responses and provide a reference section.
+4. **Seamless Audio Integration**: The ability to handle audio inputs makes the assistant more versatile and user-friendly.
+
+This updated Research Assistant showcases how to create a powerful, interactive tool that can efficiently fetch and process web content, handle various input types, and provide informed responses based on the gathered information.

docs/md/examples/summarization.md

@@ -1,44 +1,34 @@
-## Summarization Example
+# Summarization Example with AsyncWebCrawler
 
-This example demonstrates how to use `Crawl4AI` to extract a summary from a web page. The goal is to obtain the title, a detailed summary, a brief summary, and a list of keywords from the given page.
+This example demonstrates how to use Crawl4AI's `AsyncWebCrawler` to extract a summary from a web page asynchronously. The goal is to obtain the title, a detailed summary, a brief summary, and a list of keywords from the given page.
 
-### Step-by-Step Guide
+## Step-by-Step Guide
 
 1. **Import Necessary Modules**
 
-    First, import the necessary modules and classes.
+    First, import the necessary modules and classes:
 
     ```python
     import os
-    import time
     import json
-    from crawl4ai.web_crawler import WebCrawler
-    from crawl4ai.chunking_strategy import *
-    from crawl4ai.extraction_strategy import *
-    from crawl4ai.crawler_strategy import *
+    import asyncio
+    from crawl4ai import AsyncWebCrawler
+    from crawl4ai.extraction_strategy import LLMExtractionStrategy
+    from crawl4ai.chunking_strategy import RegexChunking
     from pydantic import BaseModel, Field
     ```
 
 2. **Define the URL to be Crawled**
 
-    Set the URL of the web page you want to summarize.
+    Set the URL of the web page you want to summarize:
 
     ```python
-    url = r'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot'
+    url = 'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot'
     ```
 
-3. **Initialize the WebCrawler**
+3. **Define the Data Model**
 
-    Create an instance of the `WebCrawler` and call the `warmup` method.
-
-    ```python
-    crawler = WebCrawler()
-    crawler.warmup()
-    ```
-
-4. **Define the Data Model**
-
-    Use Pydantic to define the structure of the extracted data.
+    Use Pydantic to define the structure of the extracted data:
 
     ```python
     class PageSummary(BaseModel):
@@ -48,61 +38,116 @@ This example demonstrates how to use `Crawl4AI` to extract a summary from a web
         keywords: list = Field(..., description="Keywords assigned to the page.")
     ```
 
-5. **Run the Crawler**
+4. **Create the Extraction Strategy**
 
-    Set up and run the crawler with the `LLMExtractionStrategy`. Provide the necessary parameters, including the schema for the extracted data and the instruction for the LLM.
+    Set up the `LLMExtractionStrategy` with the necessary parameters:
 
     ```python
-    result = crawler.run(
-        url=url,
-        word_count_threshold=1,
-        extraction_strategy=LLMExtractionStrategy(
-            provider="openai/gpt-4o", 
-            api_token=os.getenv('OPENAI_API_KEY'), 
-            schema=PageSummary.model_json_schema(),
-            extraction_type="schema",
-            apply_chunking=False,
-            instruction=(
-                "From the crawled content, extract the following details: "
-                "1. Title of the page "
-                "2. Summary of the page, which is a detailed summary "
-                "3. Brief summary of the page, which is a paragraph text "
-                "4. Keywords assigned to the page, which is a list of keywords. "
-                'The extracted JSON format should look like this: '
-                '{ "title": "Page Title", "summary": "Detailed summary of the page.", '
-                '"brief_summary": "Brief summary in a paragraph.", "keywords": ["keyword1", "keyword2", "keyword3"] }'
-            )
-        ),
-        bypass_cache=True,
+    extraction_strategy = LLMExtractionStrategy(
+        provider="openai/gpt-4o", 
+        api_token=os.getenv('OPENAI_API_KEY'), 
+        schema=PageSummary.model_json_schema(),
+        extraction_type="schema",
+        apply_chunking=False,
+        instruction=(
+            "From the crawled content, extract the following details: "
+            "1. Title of the page "
+            "2. Summary of the page, which is a detailed summary "
+            "3. Brief summary of the page, which is a paragraph text "
+            "4. Keywords assigned to the page, which is a list of keywords. "
+            'The extracted JSON format should look like this: '
+            '{ "title": "Page Title", "summary": "Detailed summary of the page.", '
+            '"brief_summary": "Brief summary in a paragraph.", "keywords": ["keyword1", "keyword2", "keyword3"] }'
+        )
     )
     ```
 
-6. **Process the Extracted Data**
+5. **Define the Async Crawl Function**
 
-    Load the extracted content into a JSON object and print it.
+    Create an asynchronous function to run the crawler:
 
     ```python
-    page_summary = json.loads(result.extracted_content)
-    print(page_summary)
+    async def crawl_and_summarize(url):
+        async with AsyncWebCrawler(verbose=True) as crawler:
+            result = await crawler.arun(
+                url=url,
+                word_count_threshold=1,
+                extraction_strategy=extraction_strategy,
+                chunking_strategy=RegexChunking(),
+                bypass_cache=True,
+            )
+            return result
     ```
 
-7. **Save the Extracted Data**
+6. **Run the Crawler and Process Results**
 
-    Save the extracted data to a file for further use.
+    Use asyncio to run the crawler and process the results:
 
     ```python
-    with open(".data/page_summary.json", "w", encoding="utf-8") as f:
-        f.write(result.extracted_content)
+    async def main():
+        result = await crawl_and_summarize(url)
+        
+        if result.success:
+            page_summary = json.loads(result.extracted_content)
+            print("Extracted Page Summary:")
+            print(json.dumps(page_summary, indent=2))
+            
+            # Save the extracted data
+            with open(".data/page_summary.json", "w", encoding="utf-8") as f:
+                json.dump(page_summary, f, indent=2)
+            print("Page summary saved to .data/page_summary.json")
+        else:
+            print(f"Failed to crawl and summarize the page. Error: {result.error_message}")
+
+    # Run the async main function
+    asyncio.run(main())
     ```
 
-### Explanation
+## Explanation
 
-- **Importing Modules**: Import the necessary modules, including `WebCrawler` and `LLMExtractionStrategy` from `Crawl4AI`.
-- **URL Definition**: Set the URL of the web page you want to crawl and summarize.
-- **WebCrawler Initialization**: Create an instance of `WebCrawler` and call the `warmup` method to prepare the crawler.
-- **Data Model Definition**: Define the structure of the data you want to extract using Pydantic's `BaseModel`.
-- **Crawler Execution**: Run the crawler with the `LLMExtractionStrategy`, providing the schema and detailed instructions for the extraction process.
-- **Data Processing**: Load the extracted content into a JSON object and print it to verify the results.
-- **Data Saving**: Save the extracted data to a file for further use.
+- **Importing Modules**: We import the necessary modules, including `AsyncWebCrawler` and `LLMExtractionStrategy` from Crawl4AI.
+- **URL Definition**: We set the URL of the web page to crawl and summarize.
+- **Data Model Definition**: We define the structure of the data to extract using Pydantic's `BaseModel`.
+- **Extraction Strategy Setup**: We create an instance of `LLMExtractionStrategy` with the schema and detailed instructions for the extraction process.
+- **Async Crawl Function**: We define an asynchronous function `crawl_and_summarize` that uses `AsyncWebCrawler` to perform the crawling and extraction.
+- **Main Execution**: In the `main` function, we run the crawler, process the results, and save the extracted data.
 
-This example demonstrates how to harness the power of `Crawl4AI` to perform advanced web crawling and data extraction tasks with minimal code.
+## Advanced Usage: Crawling Multiple URLs
+
+To demonstrate the power of `AsyncWebCrawler`, here's how you can summarize multiple pages concurrently:
+
+```python
+async def crawl_multiple_urls(urls):
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        tasks = [crawler.arun(
+            url=url,
+            word_count_threshold=1,
+            extraction_strategy=extraction_strategy,
+            chunking_strategy=RegexChunking(),
+            bypass_cache=True
+        ) for url in urls]
+        results = await asyncio.gather(*tasks)
+    return results
+
+async def main():
+    urls = [
+        'https://marketplace.visualstudio.com/items?itemName=Unclecode.groqopilot',
+        'https://marketplace.visualstudio.com/items?itemName=GitHub.copilot',
+        'https://marketplace.visualstudio.com/items?itemName=ms-python.python'
+    ]
+    results = await crawl_multiple_urls(urls)
+    
+    for i, result in enumerate(results):
+        if result.success:
+            page_summary = json.loads(result.extracted_content)
+            print(f"\nSummary for URL {i+1}:")
+            print(json.dumps(page_summary, indent=2))
+        else:
+            print(f"\nFailed to summarize URL {i+1}. Error: {result.error_message}")
+
+asyncio.run(main())
+```
+
+This advanced example shows how to use `AsyncWebCrawler` to efficiently summarize multiple web pages concurrently, significantly reducing the total processing time compared to sequential crawling.
+
+By leveraging the asynchronous capabilities of Crawl4AI, you can perform advanced web crawling and data extraction tasks with improved efficiency and scalability.

docs/md/full_details/advanced_jsoncss_extraction.md

@@ -0,0 +1,282 @@
+# Advanced Usage of JsonCssExtractionStrategy
+
+While the basic usage of JsonCssExtractionStrategy is powerful for simple structures, its true potential shines when dealing with complex, nested HTML structures. This section will explore advanced usage scenarios, demonstrating how to extract nested objects, lists, and nested lists.
+
+## Hypothetical Website Example
+
+Let's consider a hypothetical e-commerce website that displays product categories, each containing multiple products. Each product has details, reviews, and related items. This complex structure will allow us to demonstrate various advanced features of JsonCssExtractionStrategy.
+
+Assume the HTML structure looks something like this:
+
+```html
+<div class="category">
+  <h2 class="category-name">Electronics</h2>
+  <div class="product">
+    <h3 class="product-name">Smartphone X</h3>
+    <p class="product-price">$999</p>
+    <div class="product-details">
+      <span class="brand">TechCorp</span>
+      <span class="model">X-2000</span>
+    </div>
+    <ul class="product-features">
+      <li>5G capable</li>
+      <li>6.5" OLED screen</li>
+      <li>128GB storage</li>
+    </ul>
+    <div class="product-reviews">
+      <div class="review">
+        <span class="reviewer">John D.</span>
+        <span class="rating">4.5</span>
+        <p class="review-text">Great phone, love the camera!</p>
+      </div>
+      <div class="review">
+        <span class="reviewer">Jane S.</span>
+        <span class="rating">5</span>
+        <p class="review-text">Best smartphone I've ever owned.</p>
+      </div>
+    </div>
+    <ul class="related-products">
+      <li>
+        <span class="related-name">Phone Case</span>
+        <span class="related-price">$29.99</span>
+      </li>
+      <li>
+        <span class="related-name">Screen Protector</span>
+        <span class="related-price">$9.99</span>
+      </li>
+    </ul>
+  </div>
+  <!-- More products... -->
+</div>
+```
+
+Now, let's create a schema to extract this complex structure:
+
+```python
+schema = {
+    "name": "E-commerce Product Catalog",
+    "baseSelector": "div.category",
+    "fields": [
+        {
+            "name": "category_name",
+            "selector": "h2.category-name",
+            "type": "text"
+        },
+        {
+            "name": "products",
+            "selector": "div.product",
+            "type": "nested_list",
+            "fields": [
+                {
+                    "name": "name",
+                    "selector": "h3.product-name",
+                    "type": "text"
+                },
+                {
+                    "name": "price",
+                    "selector": "p.product-price",
+                    "type": "text"
+                },
+                {
+                    "name": "details",
+                    "selector": "div.product-details",
+                    "type": "nested",
+                    "fields": [
+                        {
+                            "name": "brand",
+                            "selector": "span.brand",
+                            "type": "text"
+                        },
+                        {
+                            "name": "model",
+                            "selector": "span.model",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "features",
+                    "selector": "ul.product-features li",
+                    "type": "list",
+                    "fields": [
+                        {
+                            "name": "feature",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "reviews",
+                    "selector": "div.review",
+                    "type": "nested_list",
+                    "fields": [
+                        {
+                            "name": "reviewer",
+                            "selector": "span.reviewer",
+                            "type": "text"
+                        },
+                        {
+                            "name": "rating",
+                            "selector": "span.rating",
+                            "type": "text"
+                        },
+                        {
+                            "name": "comment",
+                            "selector": "p.review-text",
+                            "type": "text"
+                        }
+                    ]
+                },
+                {
+                    "name": "related_products",
+                    "selector": "ul.related-products li",
+                    "type": "list",
+                    "fields": [
+                        {
+                            "name": "name",
+                            "selector": "span.related-name",
+                            "type": "text"
+                        },
+                        {
+                            "name": "price",
+                            "selector": "span.related-price",
+                            "type": "text"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
+```
+
+This schema demonstrates several advanced features:
+
+1. **Nested Objects**: The `details` field is a nested object within each product.
+2. **Simple Lists**: The `features` field is a simple list of text items.
+3. **Nested Lists**: The `products` field is a nested list, where each item is a complex object.
+4. **Lists of Objects**: The `reviews` and `related_products` fields are lists of objects.
+
+Let's break down the key concepts:
+
+### Nested Objects
+
+To create a nested object, use `"type": "nested"` and provide a `fields` array for the nested structure:
+
+```python
+{
+    "name": "details",
+    "selector": "div.product-details",
+    "type": "nested",
+    "fields": [
+        {
+            "name": "brand",
+            "selector": "span.brand",
+            "type": "text"
+        },
+        {
+            "name": "model",
+            "selector": "span.model",
+            "type": "text"
+        }
+    ]
+}
+```
+
+### Simple Lists
+
+For a simple list of identical items, use `"type": "list"`:
+
+```python
+{
+    "name": "features",
+    "selector": "ul.product-features li",
+    "type": "list",
+    "fields": [
+        {
+            "name": "feature",
+            "type": "text"
+        }
+    ]
+}
+```
+
+### Nested Lists
+
+For a list of complex objects, use `"type": "nested_list"`:
+
+```python
+{
+    "name": "products",
+    "selector": "div.product",
+    "type": "nested_list",
+    "fields": [
+        // ... fields for each product
+    ]
+}
+```
+
+### Lists of Objects
+
+Similar to nested lists, but typically used for simpler objects within the list:
+
+```python
+{
+    "name": "related_products",
+    "selector": "ul.related-products li",
+    "type": "list",
+    "fields": [
+        {
+            "name": "name",
+            "selector": "span.related-name",
+            "type": "text"
+        },
+        {
+            "name": "price",
+            "selector": "span.related-price",
+            "type": "text"
+        }
+    ]
+}
+```
+
+## Using the Advanced Schema
+
+To use this advanced schema with AsyncWebCrawler:
+
+```python
+import json
+import asyncio
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def extract_complex_product_data():
+    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://gist.githubusercontent.com/githubusercontent/2d7b8ba3cd8ab6cf3c8da771ddb36878/raw/1ae2f90c6861ce7dd84cc50d3df9920dee5e1fd2/sample_ecommerce.html",
+            extraction_strategy=extraction_strategy,
+            bypass_cache=True,
+        )
+
+        assert result.success, "Failed to crawl the page"
+
+        product_data = json.loads(result.extracted_content)
+        print(json.dumps(product_data, indent=2))
+
+asyncio.run(extract_complex_product_data())
+```
+
+This will produce a structured JSON output that captures the complex hierarchy of the product catalog, including nested objects, lists, and nested lists.
+
+## Tips for Advanced Usage
+
+1. **Start Simple**: Begin with a basic schema and gradually add complexity.
+2. **Test Incrementally**: Test each part of your schema separately before combining them.
+3. **Use Chrome DevTools**: The Element Inspector is invaluable for identifying the correct selectors.
+4. **Handle Missing Data**: Use the `default` key in your field definitions to handle cases where data might be missing.
+5. **Leverage Transforms**: Use the `transform` key to clean or format extracted data (e.g., converting prices to numbers).
+6. **Consider Performance**: Very complex schemas might slow down extraction. Balance complexity with performance needs.
+
+By mastering these advanced techniques, you can use JsonCssExtractionStrategy to extract highly structured data from even the most complex web pages, making it a powerful tool for web scraping and data analysis tasks.

docs/md/full_details/crawl_request_parameters.md

@@ -1,6 +1,6 @@
-# Crawl Request Parameters
+# Crawl Request Parameters for AsyncWebCrawler
 
-The `run` function in Crawl4AI is designed to be highly configurable, allowing you to customize the crawling and extraction process to suit your needs. Below are the parameters you can use with the `run` function, along with their descriptions, possible values, and examples.
+The `arun` method in Crawl4AI's `AsyncWebCrawler` is designed to be highly configurable, allowing you to customize the crawling and extraction process to suit your needs. Below are the parameters you can use with the `arun` method, along with their descriptions, possible values, and examples.
 
 ## Parameters
 
@@ -13,9 +13,9 @@ url = "https://www.nbcnews.com/business"
 ```
 
 ### word_count_threshold (int)
-**Description:** The minimum number of words a block must contain to be considered meaningful. The default value is `5`.
+**Description:** The minimum number of words a block must contain to be considered meaningful. The default value is defined by `MIN_WORD_THRESHOLD`.
 **Required:** No
-**Default Value:** `5`
+**Default Value:** `MIN_WORD_THRESHOLD`
 **Example:**
 ```python
 word_count_threshold = 10
@@ -88,43 +88,92 @@ verbose = True
 Additional keyword arguments that can be passed to customize the crawling process further. Some notable options include:
 
 - **only_text (bool):** Whether to extract only text content, excluding HTML tags. Default is `False`.
+- **session_id (str):** A unique identifier for the crawling session. This is useful for maintaining state across multiple requests.
+- **js_code (str or list):** JavaScript code to be executed on the page before extraction.
+- **wait_for (str):** A CSS selector or JavaScript function to wait for before considering the page load complete.
 
 **Example:**
 ```python
-result = crawler.run(
+result = await crawler.arun(
     url="https://www.nbcnews.com/business",
     css_selector="p",
-    only_text=True
+    only_text=True,
+    session_id="unique_session_123",
+    js_code="window.scrollTo(0, document.body.scrollHeight);",
+    wait_for="article.main-article"
 )
 ```
 
 ## Example Usage
 
-Here's an example of how to use the `run` function with various parameters:
+Here's an example of how to use the `arun` method with various parameters:
 
 ```python
-from crawl4ai import WebCrawler
+import asyncio
+from crawl4ai import AsyncWebCrawler
 from crawl4ai.extraction_strategy import CosineStrategy
 from crawl4ai.chunking_strategy import NlpSentenceChunking
 
-# Create the WebCrawler instance 
-crawler = WebCrawler() 
+async def main():
+    # Create the AsyncWebCrawler instance 
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Run the crawler with custom parameters
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            word_count_threshold=10,
+            extraction_strategy=CosineStrategy(semantic_filter="finance"),
+            chunking_strategy=NlpSentenceChunking(),
+            bypass_cache=True,
+            css_selector="div.article-content",
+            screenshot=True,
+            user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3",
+            verbose=True,
+            only_text=True,
+            session_id="business_news_session",
+            js_code="window.scrollTo(0, document.body.scrollHeight);",
+            wait_for="footer"
+        )
 
-# Run the crawler with custom parameters
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    word_count_threshold=10,
-    extraction_strategy=CosineStrategy(semantic_filter="finance"),
-    chunking_strategy=NlpSentenceChunking(),
-    bypass_cache=True,
-    css_selector="div.article-content",
-    screenshot=True,
-    user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3",
-    verbose=True,
-    only_text=True
-)
+        print(result)
 
-print(result)
+# Run the async function
+asyncio.run(main())
 ```
 
-This example demonstrates how to configure various parameters to customize the crawling and extraction process using Crawl4AI.
+This example demonstrates how to configure various parameters to customize the crawling and extraction process using the asynchronous version of Crawl4AI.
+
+## Additional Asynchronous Methods
+
+The `AsyncWebCrawler` class also provides other useful asynchronous methods:
+
+### arun_many
+**Description:** Crawl multiple URLs concurrently.
+**Example:**
+```python
+urls = ["https://example1.com", "https://example2.com", "https://example3.com"]
+results = await crawler.arun_many(urls, word_count_threshold=10, bypass_cache=True)
+```
+
+### aclear_cache
+**Description:** Clear the crawler's cache.
+**Example:**
+```python
+await crawler.aclear_cache()
+```
+
+### aflush_cache
+**Description:** Completely flush the crawler's cache.
+**Example:**
+```python
+await crawler.aflush_cache()
+```
+
+### aget_cache_size
+**Description:** Get the current size of the cache.
+**Example:**
+```python
+cache_size = await crawler.aget_cache_size()
+print(f"Current cache size: {cache_size}")
+```
+
+These asynchronous methods allow for efficient and flexible use of the AsyncWebCrawler in various scenarios.

docs/md/full_details/crawl_result_class.md

@@ -5,6 +5,9 @@ The `CrawlResult` class is the heart of Crawl4AI's output, encapsulating all the
 ## Class Definition
 
 ```python
+from pydantic import BaseModel
+from typing import Dict, List, Optional
+
 class CrawlResult(BaseModel):
     url: str
     html: str
@@ -17,6 +20,9 @@ class CrawlResult(BaseModel):
     extracted_content: Optional[str] = None
     metadata: Optional[dict] = None
     error_message: Optional[str] = None
+    session_id: Optional[str] = None
+    responser_headers: Optional[dict] = None
+    status_code: Optional[int] = None
 ```
 
 ## Fields Explanation
@@ -34,7 +40,7 @@ A flag indicating whether the crawling and extraction were successful. If any er
 The cleaned HTML content of the web page. This field holds the HTML after removing unwanted tags like `<script>`, `<style>`, and others that do not contribute to the useful content.
 
 ### `media: Dict[str, List[Dict]]`
-A dictionary containing lists of extracted media elements from the web page. The media elements are categorized into images, videos, and audios. Here’s how they are structured:
+A dictionary containing lists of extracted media elements from the web page. The media elements are categorized into images, videos, and audios. Here's how they are structured:
 
 - **Images**: Each image is represented as a dictionary with `src` (source URL) and `alt` (alternate text).
 - **Videos**: Each video is represented similarly with `src` and `alt`.
@@ -88,33 +94,11 @@ A dictionary containing metadata extracted from the web page, such as title, des
 ### `error_message: Optional[str]`
 If an error occurs during crawling, this field will contain the error message, helping you debug and understand what went wrong. 🚨
 
-## Example Usage
+### `session_id: Optional[str]`
+A unique identifier for the crawling session. This can be useful for tracking and managing multiple crawling sessions.
 
-Here's a quick example to illustrate how you might use the `CrawlResult` in your code:
+### `responser_headers: Optional[dict]`
+A dictionary containing the response headers from the web server. This can provide additional information about the server and the response.
 
-```python
-from crawl4ai import WebCrawler
-
-# Create the WebCrawler instance
-crawler = WebCrawler()
-
-# Run the crawler on a URL
-result = crawler.run(url="https://www.example.com")
-
-# Check if the crawl was successful
-if result.success:
-    print("Crawl succeeded!")
-    print("URL:", result.url)
-    print("HTML:", result.html[:100])  # Print the first 100 characters of the HTML
-    print("Cleaned HTML:", result.cleaned_html[:100])
-    print("Media:", result.media)
-    print("Links:", result.links)
-    print("Screenshot:", result.screenshot)
-    print("Markdown:", result.markdown[:100])
-    print("Extracted Content:", result.extracted_content)
-    print("Metadata:", result.metadata)
-else:
-    print("Crawl failed with error:", result.error_message)
-```
-
-With this setup, you can easily access all the valuable data extracted from the web page and integrate it into your applications. Happy crawling! 🕷️🤖
+### `status_code: Optional[int]`
+The HTTP status code of the response. This indicates the success or failure of the HTTP request (e.g., 200 for success, 404 for not found, etc.).

docs/md/full_details/extraction_strategies.md

@@ -1,6 +1,143 @@
 ## Extraction Strategies 🧠
 
-Crawl4AI offers powerful extraction strategies to derive meaningful information from web content. Let's dive into two of the most important strategies: `CosineStrategy` and `LLMExtractionStrategy`.
+Crawl4AI offers powerful extraction strategies to derive meaningful information from web content. Let's dive into three of the most important strategies: `CosineStrategy`, `LLMExtractionStrategy`, and the new `JsonCssExtractionStrategy`.
+
+### LLMExtractionStrategy
+
+`LLMExtractionStrategy` leverages a Language Model (LLM) to extract meaningful content from HTML. This strategy uses an external provider for LLM completions to perform extraction based on instructions.
+
+#### When to Use
+- Suitable for complex extraction tasks requiring nuanced understanding.
+- Ideal for scenarios where detailed instructions can guide the extraction process.
+- Perfect for extracting specific types of information or content with precise guidelines.
+
+#### Parameters
+- `provider` (str, optional): Provider for language model completions (e.g., openai/gpt-4). Default is `DEFAULT_PROVIDER`.
+- `api_token` (str, optional): API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.
+- `instruction` (str, optional): Instructions to guide the LLM on how to perform the extraction. Default is `None`.
+
+#### Example Without Instructions
+```python
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Define extraction strategy without instructions
+        strategy = LLMExtractionStrategy(
+            provider='openai',
+            api_token=os.getenv('OPENAI_API_KEY')
+        )
+
+        # Sample URL
+        url = "https://www.nbcnews.com/business"
+
+        # Run the crawler with the extraction strategy
+        result = await crawler.arun(url=url, extraction_strategy=strategy)
+        print(result.extracted_content)
+
+asyncio.run(main())
+```
+
+#### Example With Instructions
+```python
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Define extraction strategy with instructions
+        strategy = LLMExtractionStrategy(
+            provider='openai',
+            api_token=os.getenv('OPENAI_API_KEY'),
+            instruction="Extract only financial news and summarize key points."
+        )
+
+        # Sample URL
+        url = "https://www.nbcnews.com/business"
+
+        # Run the crawler with the extraction strategy
+        result = await crawler.arun(url=url, extraction_strategy=strategy)
+        print(result.extracted_content)
+
+asyncio.run(main())
+```
+
+### JsonCssExtractionStrategy
+
+`JsonCssExtractionStrategy` is a powerful tool for extracting structured data from HTML using CSS selectors. It allows you to define a schema that maps CSS selectors to specific fields, enabling precise and efficient data extraction.
+
+#### When to Use
+- Ideal for extracting structured data from websites with consistent HTML structures.
+- Perfect for scenarios where you need to extract specific elements or attributes from a webpage.
+- Suitable for creating datasets from web pages with tabular or list-based information.
+
+#### Parameters
+- `schema` (Dict[str, Any]): A dictionary defining the extraction schema, including base selector and field definitions.
+
+#### Example
+```python
+import asyncio
+import json
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Define the extraction schema
+        schema = {
+            "name": "News Articles",
+            "baseSelector": "article.tease-card",
+            "fields": [
+                {
+                    "name": "title",
+                    "selector": "h2",
+                    "type": "text",
+                },
+                {
+                    "name": "summary",
+                    "selector": "div.tease-card__info",
+                    "type": "text",
+                },
+                {
+                    "name": "link",
+                    "selector": "a",
+                    "type": "attribute",
+                    "attribute": "href"
+                }
+            ],
+        }
+
+        # Create the extraction strategy
+        strategy = JsonCssExtractionStrategy(schema, verbose=True)
+
+        # Sample URL
+        url = "https://www.nbcnews.com/business"
+
+        # Run the crawler with the extraction strategy
+        result = await crawler.arun(url=url, extraction_strategy=strategy)
+        
+        # Parse and print the extracted content
+        extracted_data = json.loads(result.extracted_content)
+        print(json.dumps(extracted_data, indent=2))
+
+asyncio.run(main())
+```
+
+#### Use Cases for JsonCssExtractionStrategy
+- Extracting product information from e-commerce websites.
+- Gathering news articles and their metadata from news portals.
+- Collecting user reviews and ratings from review websites.
+- Extracting job listings from job boards.
+
+By choosing the right extraction strategy, you can effectively extract the most relevant and useful information from web content. Whether you need fast, accurate semantic segmentation with `CosineStrategy`, nuanced, instruction-based extraction with `LLMExtractionStrategy`, or precise structured data extraction with `JsonCssExtractionStrategy`, Crawl4AI has you covered. Happy extracting! 🕵️‍♂️✨
+
+For more details on schema definitions and advanced extraction strategies, check out the[Advanced JsonCssExtraction](../full_details/advanced_jsoncss_extraction.md).
+
 
 ### CosineStrategy
 
@@ -21,96 +158,28 @@ Crawl4AI offers powerful extraction strategies to derive meaningful information
 
 #### Example
 ```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
 from crawl4ai.extraction_strategy import CosineStrategy
-from crawl4ai import WebCrawler
 
-crawler = WebCrawler()
-crawler.warmup()
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Define extraction strategy
+        strategy = CosineStrategy(
+            semantic_filter="finance economy stock market",
+            word_count_threshold=10,
+            max_dist=0.2,
+            linkage_method='ward',
+            top_k=3,
+            model_name='BAAI/bge-small-en-v1.5'
+        )
 
-# Define extraction strategy
-strategy = CosineStrategy(
-    semantic_filter="finance economy stock market",
-    word_count_threshold=10,
-    max_dist=0.2,
-    linkage_method='ward',
-    top_k=3,
-    model_name='BAAI/bge-small-en-v1.5'
-)
+        # Sample URL
+        url = "https://www.nbcnews.com/business"
 
-# Sample URL
-url = "https://www.nbcnews.com/business"
+        # Run the crawler with the extraction strategy
+        result = await crawler.arun(url=url, extraction_strategy=strategy)
+        print(result.extracted_content)
 
-# Run the crawler with the extraction strategy
-result = crawler.run(url=url, extraction_strategy=strategy)
-print(result.extracted_content)
+asyncio.run(main())
 ```
-
-### LLMExtractionStrategy
-
-`LLMExtractionStrategy` leverages a Language Model (LLM) to extract meaningful content from HTML. This strategy uses an external provider for LLM completions to perform extraction based on instructions.
-
-#### When to Use
-- Suitable for complex extraction tasks requiring nuanced understanding.
-- Ideal for scenarios where detailed instructions can guide the extraction process.
-- Perfect for extracting specific types of information or content with precise guidelines.
-
-#### Parameters
-- `provider` (str, optional): Provider for language model completions (e.g., openai/gpt-4). Default is `DEFAULT_PROVIDER`.
-- `api_token` (str, optional): API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.
-- `instruction` (str, optional): Instructions to guide the LLM on how to perform the extraction. Default is `None`.
-
-#### Example Without Instructions
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-from crawl4ai import WebCrawler
-
-crawler = WebCrawler()
-crawler.warmup()
-
-# Define extraction strategy without instructions
-strategy = LLMExtractionStrategy(
-    provider='openai',
-    api_token='your_api_token'
-)
-
-# Sample URL
-url = "https://www.nbcnews.com/business"
-
-# Run the crawler with the extraction strategy
-result = crawler.run(url=url, extraction_strategy=strategy)
-print(result.extracted_content)
-```
-
-#### Example With Instructions
-```python
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-from crawl4ai import WebCrawler
-
-crawler = WebCrawler()
-crawler.warmup()
-
-# Define extraction strategy with instructions
-strategy = LLMExtractionStrategy(
-    provider='openai',
-    api_token='your_api_token',
-    instruction="Extract only financial news and summarize key points."
-)
-
-# Sample URL
-url = "https://www.nbcnews.com/business"
-
-# Run the crawler with the extraction strategy
-result = crawler.run(url=url, extraction_strategy=strategy)
-print(result.extracted_content)
-```
-
-#### Use Cases for LLMExtractionStrategy
-- Extracting specific data types from structured or semi-structured content.
-- Generating summaries, extracting key information, or transforming content into different formats.
-- Performing detailed extractions based on custom instructions.
-
-For more detailed examples, please refer to the [Examples section](../examples/index.md) of the documentation.
-
----
-
-By choosing the right extraction strategy, you can effectively extract the most relevant and useful information from web content. Whether you need fast, accurate semantic segmentation with `CosineStrategy` or nuanced, instruction-based extraction with `LLMExtractionStrategy`, Crawl4AI has you covered. Happy extracting! 🕵️‍♂️✨

docs/md/index.md

@@ -1,43 +1,39 @@
-# Crawl4AI v0.2.77
+# Crawl4AI
 
 Welcome to the official documentation for Crawl4AI! 🕷️🤖 Crawl4AI is an open-source Python library designed to simplify web crawling and extract useful information from web pages. This documentation will guide you through the features, usage, and customization of Crawl4AI.
 
-
-## Try the [Demo](demo.md)
-
-Just try it now and crawl different pages to see how it works. You can set the links, see the structures of the output, and also view the Python sample code on how to run it. The old demo is available at [/old_demo](/old) where you can see more details.
-
 ## Introduction
 
-Crawl4AI has one clear task: to make crawling and data extraction from web pages easy and efficient, especially for large language models (LLMs) and AI applications. Whether you are using it as a REST API or a Python library, Crawl4AI offers a robust and flexible solution.
+Crawl4AI has one clear task: to make crawling and data extraction from web pages easy and efficient, especially for large language models (LLMs) and AI applications. Whether you are using it as a REST API or a Python library, Crawl4AI offers a robust and flexible solution with full asynchronous support.
 
 ## Quick Start
 
-Here's a quick example to show you how easy it is to use Crawl4AI:
+Here's a quick example to show you how easy it is to use Crawl4AI with its new asynchronous capabilities:
 
 ```python
-from crawl4ai import WebCrawler
+import asyncio
+from crawl4ai import AsyncWebCrawler
 
-# Create an instance of WebCrawler
-crawler = WebCrawler()
+async def main():
+    # Create an instance of AsyncWebCrawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Run the crawler on a URL
+        result = await crawler.arun(url="https://www.nbcnews.com/business")
 
-# Warm up the crawler (load necessary models)
-crawler.warmup()
+        # Print the extracted content
+        print(result.markdown)
 
-# Run the crawler on a URL
-result = crawler.run(url="https://www.nbcnews.com/business")
-
-# Print the extracted content
-print(result.extracted_content)
+# Run the async main function
+asyncio.run(main())
 ```
 
 ### Explanation
 
-1. **Importing the Library**: We start by importing the `WebCrawler` class from the `crawl4ai` library.
-2. **Creating an Instance**: An instance of `WebCrawler` is created.
-3. **Warming Up**: The `warmup()` method prepares the crawler by loading necessary models and settings.
-4. **Running the Crawler**: The `run()` method is used to crawl the specified URL and extract meaningful content.
-5. **Printing the Result**: The extracted content is printed, showcasing the data extracted from the web page.
+1. **Importing the Library**: We start by importing the `AsyncWebCrawler` class from the `crawl4ai` library and the `asyncio` module.
+2. **Creating an Async Context**: We use an async context manager to create an instance of `AsyncWebCrawler`.
+3. **Running the Crawler**: The `arun()` method is used to asynchronously crawl the specified URL and extract meaningful content.
+4. **Printing the Result**: The extracted content is printed, showcasing the data extracted from the web page.
+5. **Running the Async Function**: We use `asyncio.run()` to execute our async main function.
 
 ## Documentation Structure
 
@@ -63,6 +59,7 @@ A step-by-step guide to get you up and running with Crawl4AI, including installa
 
 This section contains practical examples demonstrating different use cases of Crawl4AI:
 
+- [Structured Data Extraction](examples/json_css_extraction.md)
 - [LLM Extraction](examples/llm_extraction.md)
 - [JS Execution & CSS Filtering](examples/js_execution_css_filtering.md)
 - [Hooks & Auth](examples/hooks_auth.md)
@@ -76,16 +73,10 @@ Comprehensive details on using the crawler, including:
 - [Crawl Request Parameters](full_details/crawl_request_parameters.md)
 - [Crawl Result Class](full_details/crawl_result_class.md)
 - [Advanced Features](full_details/advanced_features.md)
+- [Advanced JsonCssExtraction](full_details/advanced_jsoncss_extraction.md)
 - [Chunking Strategies](full_details/chunking_strategies.md)
 - [Extraction Strategies](full_details/extraction_strategies.md)
 
-### [API Reference](api/core_classes_and_functions.md)
-
-Detailed documentation of the API, covering:
-
-- [Core Classes and Functions](api/core_classes_and_functions.md)
-- [Detailed API Documentation](api/detailed_api_documentation.md)
-
 ### [Change Log](changelog.md)
 
 A log of all changes, updates, and improvements made to Crawl4AI.
@@ -96,6 +87,6 @@ Information on how to get in touch with the developers, report issues, and contr
 
 ## Get Started
 
-To get started with Crawl4AI, follow the quick start guide above or explore the detailed sections of this documentation. Whether you are a beginner or an advanced user, Crawl4AI has something to offer to make your web crawling and data extraction tasks easier and more efficient.
+To get started with Crawl4AI, follow the quick start guide above or explore the detailed sections of this documentation. Whether you are a beginner or an advanced user, Crawl4AI has something to offer to make your web crawling and data extraction tasks easier, more efficient, and now fully asynchronous.
 
 Happy Crawling! 🕸️🚀

docs/md/installation.md

@@ -1,193 +1,92 @@
 # Installation 💻
 
-There are three ways to use Crawl4AI:
+Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package, use it with Docker, or run it as a local server.
 
-1. As a library (Recommended).
-2. As a local server (Docker) or using the REST API.
-3. As a local server (Docker) using the pre-built image from Docker Hub.
+## Option 1: Python Package Installation (Recommended)
 
-## Option 1: Library Installation
+Crawl4AI is now available on PyPI, making installation easier than ever. Choose the option that best fits your needs:
 
-You can try this Colab for a quick start: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sJPAmeLj5PMrg2VgOwMJ2ubGIcK0cJeX#scrollTo=g1RrmI4W_rPk)
+### Basic Installation
 
-Crawl4AI offers flexible installation options to suit various use cases. Choose the option that best fits your needs:
+For basic web crawling and scraping tasks:
 
-- **Default Installation** (Basic functionality):
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai
+playwright install # Install Playwright dependencies
 ```
-Use this for basic web crawling and scraping tasks.
 
-- **Installation with PyTorch** (For advanced text clustering):
+### Installation with PyTorch
+
+For advanced text clustering (includes CosineSimilarity cluster strategy):
+
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai[torch] @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai[torch]
 ```
-Choose this if you need the CosineSimilarity cluster strategy.
 
-- **Installation with Transformers** (For summarization and Hugging Face models):
+### Installation with Transformers
+
+For text summarization and Hugging Face models:
+
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai[transformer] @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai[transformer]
 ```
-Opt for this if you require text summarization or plan to use Hugging Face models.
 
-- **Full Installation** (All features):
+### Full Installation
+
+For all features:
+
 ```bash
-virtualenv venv
-source venv/bin/activate
-pip install "crawl4ai[all] @ git+https://github.com/unclecode/crawl4ai.git"
+pip install crawl4ai[all]
 ```
-This installs all dependencies for full functionality.
 
-- **Development Installation** (For contributors):
+### Development Installation
+
+For contributors who plan to modify the source code:
+
 ```bash
-virtualenv venv
-source venv/bin/activate
 git clone https://github.com/unclecode/crawl4ai.git
 cd crawl4ai
 pip install -e ".[all]"
+playwright install # Install Playwright dependencies
 ```
-Use this if you plan to modify the source code.
 
-💡 After installation, if you have used "torch", "transformer" or "all", it's recommended to run the following CLI command to load the required models. This is optional but will boost the performance and speed of the crawler. You need to do this only once, this is only for when you install using []
+💡 After installation with "torch", "transformer", or "all" options, it's recommended to run the following CLI command to load the required models:
+
 ```bash
 crawl4ai-download-models
 ```
 
-## Option 2: Using Docker for Local Server
+This is optional but will boost the performance and speed of the crawler. You only need to do this once after installation.
 
-Crawl4AI can be run as a local server using Docker. The Dockerfile supports different installation options to cater to various use cases. Here's how you can build and run the Docker image:
+## Option 2: Using Docker (Coming Soon)
 
-### Default Installation
+Docker support for Crawl4AI is currently in progress and will be available soon. This will allow you to run Crawl4AI in a containerized environment, ensuring consistency across different systems.
 
-The default installation includes the basic Crawl4AI package without additional dependencies or pre-downloaded models.
+## Option 3: Local Server Installation
 
-```bash
-# For Mac users (M1/M2)
-docker build --platform linux/amd64 -t crawl4ai .
+For those who prefer to run Crawl4AI as a local server, instructions will be provided once the Docker implementation is complete.
 
-# For other users
-docker build -t crawl4ai .
+## Verifying Your Installation
 
-# Run the container
-docker run -d -p 8000:80 crawl4ai
+After installation, you can verify that Crawl4AI is working correctly by running a simple Python script:
+
+```python
+import asyncio
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(url="https://www.example.com")
+        print(result.markdown[:500])  # Print first 500 characters
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
-### Full Installation (All Dependencies and Models)
+This script should successfully crawl the example website and print the first 500 characters of the extracted content.
 
-This option installs all dependencies and downloads the models.
+## Getting Help
 
-```bash
-# For Mac users (M1/M2)
-docker build --platform linux/amd64 --build-arg INSTALL_OPTION=all -t crawl4ai:all .
-
-# For other users
-docker build --build-arg INSTALL_OPTION=all -t crawl4ai:all .
-
-# Run the container
-docker run -d -p 8000:80 crawl4ai:all
-```
-
-### Torch Installation
-
-This option installs torch-related dependencies and downloads the models.
-
-```bash
-# For Mac users (M1/M2)
-docker build --platform linux/amd64 --build-arg INSTALL_OPTION=torch -t crawl4ai:torch .
-
-# For other users
-docker build --build-arg INSTALL_OPTION=torch -t crawl4ai:torch .
-
-# Run the container
-docker run -d -p 8000:80 crawl4ai:torch
-```
-
-### Transformer Installation
-
-This option installs transformer-related dependencies and downloads the models.
-
-```bash
-# For Mac users (M1/M2)
-docker build --platform linux/amd64 --build-arg INSTALL_OPTION=transformer -t crawl4ai:transformer .
-
-# For other users
-docker build --build-arg INSTALL_OPTION=transformer -t crawl4ai:transformer .
-
-# Run the container
-docker run -d -p 8000:80 crawl4ai:transformer
-```
-
-### Notes
-
-- The `--platform linux/amd64` flag is necessary for Mac users with M1/M2 chips to ensure compatibility.
-- The `-t` flag tags the image with a name (and optionally a tag in the 'name:tag' format).
-- The `-d` flag runs the container in detached mode.
-- The `-p 8000:80` flag maps port 8000 on the host to port 80 in the container.
-
-Choose the installation option that best suits your needs. The default installation is suitable for basic usage, while the other options provide additional capabilities for more advanced use cases.
-
-## Option 3: Using the Pre-built Image from Docker Hub
-
-You can use pre-built Crawl4AI images from Docker Hub, which are available for all platforms (Mac, Linux, Windows). We have official images as well as a community-contributed image (Thanks to https://github.com/FractalMind):
-
-### Default Installation
-
-```bash
-
-# Pull the image
-
-docker pull unclecode/crawl4ai:latest
-
-# Run the container
-
-docker run -d -p 8000:80 unclecode/crawl4ai:latest
-
-```
-
-### Community-Contributed Image
-
-A stable version of Crawl4AI is also available, created and maintained by a community member:
-
-```bash
-
-# Pull the community-contributed image
-
-docker pull ryser007/crawl4ai:stable
-
-# Run the container
-
-docker run -d -p 8000:80 ryser007/crawl4ai:stable
-
-```
-
-We'd like to express our gratitude to GitHub user [@FractalMind](https://github.com/FractalMind) for creating and maintaining this stable version of the Crawl4AI Docker image. Community contributions like this are invaluable to the project.
-
-
-### Testing the Installation
-
-After running the container, you can test if it's working correctly:
-
-- On Mac and Linux:
-
-  ```bash
-
-  curl http://localhost:8000
-
-  ```
-
-- On Windows (PowerShell):
-
-  ```powershell
-
-  Invoke-WebRequest -Uri http://localhost:8000
-
-  ```
-
-  Or open a web browser and navigate to http://localhost:8000
+If you encounter any issues during installation or usage, please check the [documentation](https://crawl4ai.com/mkdocs/) or raise an issue on the [GitHub repository](https://github.com/unclecode/crawl4ai/issues).
 
+Happy crawling! 🕷️🤖

docs/md/quickstart.md

@@ -1,20 +1,22 @@
 # Quick Start Guide 🚀
 
-Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies. Let's dive in! 🌟
+Welcome to the Crawl4AI Quickstart Guide! In this tutorial, we'll walk you through the basic usage of Crawl4AI with a friendly and humorous tone. We'll cover everything from basic usage to advanced features like chunking and extraction strategies, all with the power of asynchronous programming. Let's dive in! 🌟
 
 ## Getting Started 🛠️
 
-First, let's create an instance of `WebCrawler` and call the `warmup()` function. This might take a few seconds the first time you run Crawl4AI, as it loads the required model files.
+First, let's import the necessary modules and create an instance of `AsyncWebCrawler`. We'll use an async context manager, which handles the setup and teardown of the crawler for us.
 
 ```python
-from crawl4ai import WebCrawler
+import asyncio
+from crawl4ai import AsyncWebCrawler
 
-def create_crawler():
-    crawler = WebCrawler(verbose=True)
-    crawler.warmup()
-    return crawler
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # We'll add our crawling code here
+        pass
 
-crawler = create_crawler()
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 ### Basic Usage
@@ -22,8 +24,12 @@ crawler = create_crawler()
 Simply provide a URL and let Crawl4AI do the magic!
 
 ```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(f"Basic crawl result: {result}")
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(url="https://www.nbcnews.com/business")
+        print(f"Basic crawl result: {result.markdown[:500]}")  # Print first 500 characters
+
+asyncio.run(main())
 ```
 
 ### Taking Screenshots 📸
@@ -31,26 +37,34 @@ print(f"Basic crawl result: {result}")
 Let's take a screenshot of the page!
 
 ```python
-result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
-with open("screenshot.png", "wb") as f:
-    f.write(base64.b64decode(result.screenshot))
-print("Screenshot saved to 'screenshot.png'!")
+import base64
+
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(url="https://www.nbcnews.com/business", screenshot=True)
+        with open("screenshot.png", "wb") as f:
+            f.write(base64.b64decode(result.screenshot))
+        print("Screenshot saved to 'screenshot.png'!")
+
+asyncio.run(main())
 ```
 
 ### Understanding Parameters 🧠
 
 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 (caches the result):
 ```python
-result = crawler.run(url="https://www.nbcnews.com/business")
-print(f"First crawl result: {result}")
-```
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # First crawl (caches the result)
+        result1 = await crawler.arun(url="https://www.nbcnews.com/business")
+        print(f"First crawl result: {result1.markdown[:100]}...")
 
-Force to crawl again:
-```python
-result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
-print(f"Second crawl result: {result}")
+        # Force to crawl again
+        result2 = await crawler.arun(url="https://www.nbcnews.com/business", bypass_cache=True)
+        print(f"Second crawl result: {result2.markdown[:100]}...")
+
+asyncio.run(main())
 ```
 
 ### Adding a Chunking Strategy 🧩
@@ -60,145 +74,212 @@ Let's add a chunking strategy: `RegexChunking`! This strategy splits the text ba
 ```python
 from crawl4ai.chunking_strategy import RegexChunking
 
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    chunking_strategy=RegexChunking(patterns=["\n\n"])
-)
-print(f"RegexChunking result: {result}")
-```
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            chunking_strategy=RegexChunking(patterns=["\n\n"])
+        )
+        print(f"RegexChunking result: {result.extracted_content[:200]}...")
 
-You can also use `NlpSentenceChunking` which splits the text into sentences using NLP techniques.
-
-```python
-from crawl4ai.chunking_strategy import NlpSentenceChunking
-
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    chunking_strategy=NlpSentenceChunking()
-)
-print(f"NlpSentenceChunking result: {result}")
+asyncio.run(main())
 ```
 
 ### Adding an Extraction Strategy 🧠
 
-Let's get smarter with an extraction strategy: `CosineStrategy`! This strategy uses cosine similarity to extract semantically similar blocks of text.
+Let's get smarter with an extraction strategy: `JsonCssExtractionStrategy`! This strategy extracts structured data from HTML using CSS selectors.
 
 ```python
-from crawl4ai.extraction_strategy import CosineStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+import json
 
-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
-    )
-)
-print(f"CosineStrategy result: {result}")
-```
+async def main():
+    schema = {
+        "name": "News Articles",
+        "baseSelector": "article.tease-card",
+        "fields": [
+            {
+                "name": "title",
+                "selector": "h2",
+                "type": "text",
+            },
+            {
+                "name": "summary",
+                "selector": "div.tease-card__info",
+                "type": "text",
+            }
+        ],
+    }
 
-You can also pass other parameters like `semantic_filter` to extract specific content.
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True)
+        )
+        extracted_data = json.loads(result.extracted_content)
+        print(f"Extracted {len(extracted_data)} articles")
+        print(json.dumps(extracted_data[0], indent=2))
 
-```python
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    extraction_strategy=CosineStrategy(
-        semantic_filter="inflation rent prices"
-    )
-)
-print(f"CosineStrategy result with semantic filter: {result}")
+asyncio.run(main())
 ```
 
 ### Using LLMExtractionStrategy 🤖
 
-Time to bring in the big guns: `LLMExtractionStrategy` without instructions! This strategy uses a large language model to extract relevant information from the web page.
+Time to bring in the big guns: `LLMExtractionStrategy`! This strategy uses a large language model to extract relevant information from the web page.
 
 ```python
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 import os
+from pydantic import BaseModel, Field
 
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    extraction_strategy=LLMExtractionStrategy(
-        provider="openai/gpt-4o", 
-        api_token=os.getenv('OPENAI_API_KEY')
-    )
-)
-print(f"LLMExtractionStrategy (no instructions) result: {result}")
-```
+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.")
 
-You can also provide specific instructions to guide the extraction.
+async def main():
+    if not os.getenv("OPENAI_API_KEY"):
+        print("OpenAI API key not found. Skipping this example.")
+        return
 
-```python
-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"
-    )
-)
-print(f"LLMExtractionStrategy (with instructions) result: {result}")
-```
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://openai.com/api/pricing/",
+            word_count_threshold=1,
+            extraction_strategy=LLMExtractionStrategy(
+                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"}.""",
+            ),
+            bypass_cache=True,
+        )
+        print(result.extracted_content)
 
-### Targeted Extraction 🎯
-
-Let's use a CSS selector to extract only H2 tags!
-
-```python
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    css_selector="h2"
-)
-print(f"CSS Selector (H2 tags) result: {result}")
+asyncio.run(main())
 ```
 
 ### Interactive Extraction 🖱️
 
-Passing JavaScript code to click the 'Load More' button!
+Let's use JavaScript to interact with the page before extraction!
 
 ```python
-js_code = """
-const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
-loadMoreButton && loadMoreButton.click();
-"""
+async def main():
+    js_code = """
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
+    loadMoreButton && loadMoreButton.click();
+    """
 
-result = crawler.run(
-    url="https://www.nbcnews.com/business",
-    js=js_code
-)
-print(f"JavaScript Code (Load More button) result: {result}")
+    wait_for = """() => {
+        return Array.from(document.querySelectorAll('article.tease-card')).length > 10;
+    }"""
+
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        result = await crawler.arun(
+            url="https://www.nbcnews.com/business",
+            js_code=js_code,
+            wait_for=wait_for,
+            css_selector="article.tease-card",
+            bypass_cache=True,
+        )
+        print(f"JavaScript interaction result: {result.extracted_content[:500]}")
+
+asyncio.run(main())
 ```
 
-### Using Crawler Hooks 🔗
+### Advanced Session-Based Crawling with Dynamic Content 🔄
 
-Let's see how we can customize the crawler using hooks!
+In modern web applications, content is often loaded dynamically without changing the URL. This is common in single-page applications (SPAs) or websites using infinite scrolling. Traditional crawling methods that rely on URL changes won't work here. That's where Crawl4AI's advanced session-based crawling comes in handy!
+
+Here's what makes this approach powerful:
+
+1. **Session Preservation**: By using a `session_id`, we can maintain the state of our crawling session across multiple interactions with the page. This is crucial for navigating through dynamically loaded content.
+
+2. **Asynchronous JavaScript Execution**: We can execute custom JavaScript to trigger content loading or navigation. In this example, we'll click a "Load More" button to fetch the next page of commits.
+
+3. **Dynamic Content Waiting**: The `wait_for` parameter allows us to specify a condition that must be met before considering the page load complete. This ensures we don't extract data before the new content is fully loaded.
+
+Let's see how this works with a real-world example: crawling multiple pages of commits on a GitHub repository. The URL doesn't change as we load more commits, so we'll use these advanced techniques to navigate and extract data.
 
 ```python
-import time
+import json
+from bs4 import BeautifulSoup
+from crawl4ai import AsyncWebCrawler
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
 
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.crawler_strategy import *
+async def main():
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        url = "https://github.com/microsoft/TypeScript/commits/main"
+        session_id = "typescript_commits_session"
+        all_commits = []
 
-def delay(driver):
-    print("Delaying for 5 seconds...")
-    time.sleep(5)
-    print("Resuming...")
+        js_next_page = """
+        const button = document.querySelector('a[data-testid="pagination-next-button"]');
+        if (button) button.click();
+        """
 
-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
+        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;
+        }"""
 
-crawler = create_crawler()
-result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)
+        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")
+
+asyncio.run(main())
 ```
 
-check [Hooks](examples/hooks_auth.md) for more examples.
+In this example, we're crawling multiple pages of commits from a GitHub repository. The URL doesn't change as we load more commits, so we use JavaScript to click the "Load More" button and a `wait_for` condition to ensure the new content is loaded before extraction. This powerful combination allows us to navigate and extract data from complex, dynamically-loaded web applications with ease!
 
 ## Congratulations! 🎉
 
-You've made it through the Crawl4AI Quickstart Guide! Now go forth and crawl the web like a pro! 🕸️
+You've made it through the Crawl4AI Quickstart Guide! Now go forth and crawl the web asynchronously like a pro! 🕸️
+
+Remember, these are just a few examples of what Crawl4AI can do. For more advanced usage, check out our other documentation pages:
+
+- [LLM Extraction](examples/llm_extraction.md)
+- [JS Execution & CSS Filtering](examples/js_execution_css_filtering.md)
+- [Hooks & Auth](examples/hooks_auth.md)
+- [Summarization](examples/summarization.md)
+- [Research Assistant](examples/research_assistant.md)
+
+Happy crawling! 🚀

mkdocs.yml

@@ -2,13 +2,13 @@ site_name: Crawl4AI Documentation
 docs_dir: docs/md
 nav:
   - Home: index.md
-  - Demo: demo.md  # Add this line
   - First Steps:
       - Introduction: introduction.md
       - Installation: installation.md
       - Quick Start: quickstart.md
   - Examples:
       - Intro: examples/index.md
+      - Structured Data Extraction: examples/json_css_extraction.md
       - LLM Extraction: examples/llm_extraction.md
       - JS Execution & CSS Filtering: examples/js_execution_css_filtering.md
       - Hooks & Auth: examples/hooks_auth.md
@@ -18,11 +18,9 @@ nav:
       - Crawl Request Parameters: full_details/crawl_request_parameters.md
       - Crawl Result Class: full_details/crawl_result_class.md
       - Advanced Features: full_details/advanced_features.md
+      - Advanced JsonCssExtraction: full_details/advanced_jsoncss_extraction.md
       - Chunking Strategies: full_details/chunking_strategies.md
       - Extraction Strategies: full_details/extraction_strategies.md
-  - API Reference:
-      - Core Classes and Functions: api/core_classes_and_functions.md
-      - Detailed API Documentation: api/detailed_api_documentation.md
   - Miscellaneous:
       - Change Log: changelog.md
       - Contact: contact.md

requirements.txt

@@ -1,22 +1,66 @@
-numpy>=1.25.0
-aiohttp>=3.9.5
-aiosqlite>=0.20.0
-beautifulsoup4>=4.12.3
-fastapi>=0.111.0
-html2text>=2024.2.26
-httpx>=0.27.0
-litellm>=1.40.17
-nltk>=3.8.1
-pydantic>=2.7.4
-python-dotenv>=1.0.1
-requests>=2.32.3
-rich>=13.7.1
-scikit-learn>=1.5.0
-selenium>=4.23.1
-uvicorn>=0.30.1
-transformers>=4.41.2
-torch>=2.3.1
-tokenizers>=0.19.1
-pillow>=10.3.0
-slowapi>=0.1.9
-playwright>=1.46.0
+aiohappyeyeballs==2.4.0
+aiohttp==3.10.5
+aiosignal==1.3.1
+aiosqlite==0.20.0
+annotated-types==0.7.0
+anyio==4.6.0
+async-timeout==4.0.3
+attrs==24.2.0
+beautifulsoup4==4.12.3
+certifi==2024.8.30
+charset-normalizer==3.3.2
+click==8.1.7
+distro==1.9.0
+exceptiongroup==1.2.2
+filelock==3.16.1
+frozenlist==1.4.1
+fsspec==2024.9.0
+greenlet==3.0.3
+h11==0.14.0
+html2text==2024.2.26
+httpcore==1.0.5
+httpx==0.27.2
+huggingface-hub==0.25.1
+idna==3.10
+importlib_metadata==8.5.0
+Jinja2==3.1.4
+jiter==0.5.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+litellm==1.48.0
+lxml==5.3.0
+MarkupSafe==2.1.5
+multidict==6.1.0
+nest-asyncio==1.6.0
+numpy==2.1.1
+openai==1.47.1
+outcome==1.3.0.post0
+packaging==24.1
+pillow==10.4.0
+playwright==1.47.0
+psutil==6.0.0
+pydantic==2.9.2
+pydantic_core==2.23.4
+pyee==12.0.0
+PySocks==1.7.1
+python-dotenv==1.0.1
+PyYAML==6.0.2
+referencing==0.35.1
+regex==2024.9.11
+requests==2.32.3
+rpds-py==0.20.0
+selenium==4.25.0
+sniffio==1.3.1
+sortedcontainers==2.4.0
+soupsieve==2.6
+tiktoken==0.7.0
+tokenizers==0.20.0
+tqdm==4.66.5
+trio==0.26.2
+trio-websocket==0.11.1
+typing_extensions==4.12.2
+urllib3==2.2.3
+websocket-client==1.8.0
+wsproto==1.2.0
+yarl==1.12.1
+zipp==3.20.2

setup.py

@@ -2,6 +2,7 @@ from setuptools import setup, find_packages
 import os
 from pathlib import Path
 import shutil
+import subprocess
 
 # Create the .crawl4ai folder in the user's home directory if it doesn't exist
 # If the folder already exists, remove the cache folder
@@ -18,14 +19,33 @@ cache_folder.mkdir(exist_ok=True)
 with open("requirements.txt") as f:
     requirements = f.read().splitlines()
     
+# Read version from __init__.py
+with open("crawl4ai/__init__.py") as f:
+    for line in f:
+        if line.startswith("__version__"):
+            version = line.split("=")[1].strip().strip('"')
+            break
+
 # Define the requirements for different environments
-default_requirements = [req for req in requirements if not req.startswith(("torch", "transformers", "onnxruntime", "nltk", "spacy", "tokenizers", "scikit-learn"))]
+default_requirements = [req for req in requirements if not req.startswith(("torch", "transformers", "onnxruntime", "nltk", "spacy", "tokenizers", "scikit-learn", "selenium"))]
 torch_requirements = [req for req in requirements if req.startswith(("torch", "nltk", "spacy", "scikit-learn", "numpy"))]
 transformer_requirements = [req for req in requirements if req.startswith(("transformers", "tokenizers", "onnxruntime"))]
+sync_requirements = ["selenium"]
+cosine_similarity_requirements = ["torch", "transformers", "nltk", "spacy"]
+
+def post_install():
+    print("Running post-installation setup...")
+    try:
+        subprocess.check_call(["playwright", "install"])
+        print("Playwright installation completed successfully.")
+    except subprocess.CalledProcessError:
+        print("Error during Playwright installation. Please run 'playwright install' manually.")
+    except FileNotFoundError:
+        print("Playwright not found. Please ensure it's installed and run 'playwright install' manually.")
 
 setup(
     name="Crawl4AI",
-    version="0.2.77",
+    version=version,
     description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & scraper",
     long_description=open("README.md", encoding="utf-8").read(),
     long_description_content_type="text/markdown",
@@ -38,7 +58,9 @@ setup(
     extras_require={
         "torch": torch_requirements,
         "transformer": transformer_requirements,
-        "all": requirements,
+        "sync": sync_requirements,
+        "cosine": cosine_similarity_requirements,
+        "all": requirements + sync_requirements + cosine_similarity_requirements,
     },
     entry_points={
         'console_scripts': [
@@ -56,4 +78,7 @@ setup(
         "Programming Language :: Python :: 3.10",
     ],
     python_requires=">=3.7",
+    cmdclass={
+        'install': post_install,
+    },
 )