Update README.md

In the comprehensive example,

AttributeError: type object 'CacheMode' has no attribute 'ENABLE'. Did you mean: 'ENABLED'?

MANIFEST.in

@@ -1 +1,2 @@
 include requirements.txt
+recursive-include crawl4ai/js_snippet *.js

README.md

@@ -1,4 +1,4 @@
-# 🚀🤖 Crawl4AI: Crawl Smarter, Faster, Freely. For AI.
+# 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.
 
 <a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 

crawl4ai/__version__.py

@@ -1,2 +1,2 @@
 # crawl4ai/_version.py
-__version__ = "0.4.2"
+__version__ = "0.4.22"

crawl4ai/async_configs.py

@@ -7,6 +7,7 @@ from .config import (
 from .user_agent_generator import UserAgentGenerator
 from .extraction_strategy import ExtractionStrategy
 from .chunking_strategy import ChunkingStrategy
+from .markdown_generation_strategy import MarkdownGenerationStrategy
 
 class BrowserConfig:
     """
@@ -269,6 +270,7 @@ class CrawlerRunConfig:
         word_count_threshold: int =  MIN_WORD_THRESHOLD ,
         extraction_strategy : ExtractionStrategy=None,  # Will default to NoExtractionStrategy if None
         chunking_strategy : ChunkingStrategy= None,    # Will default to RegexChunking if None
+        markdown_generator : MarkdownGenerationStrategy = None,
         content_filter=None,
         cache_mode=None,
         session_id: str = None,
@@ -309,6 +311,7 @@ class CrawlerRunConfig:
         self.word_count_threshold = word_count_threshold
         self.extraction_strategy = extraction_strategy
         self.chunking_strategy = chunking_strategy
+        self.markdown_generator = markdown_generator
         self.content_filter = content_filter
         self.cache_mode = cache_mode
         self.session_id = session_id
@@ -364,6 +367,7 @@ class CrawlerRunConfig:
             word_count_threshold=kwargs.get("word_count_threshold", 200),
             extraction_strategy=kwargs.get("extraction_strategy"),
             chunking_strategy=kwargs.get("chunking_strategy"),
+            markdown_generator=kwargs.get("markdown_generator"),
             content_filter=kwargs.get("content_filter"),
             cache_mode=kwargs.get("cache_mode"),
             session_id=kwargs.get("session_id"),

crawl4ai/async_webcrawler.py

@@ -7,7 +7,8 @@ from pathlib import Path
 from typing import Optional, List, Union
 import json
 import asyncio
-from contextlib import nullcontext, asynccontextmanager
+# from contextlib import nullcontext, asynccontextmanager
+from contextlib import asynccontextmanager
 from .models import CrawlResult, MarkdownGenerationResult
 from .async_database import async_db_manager
 from .chunking_strategy import *
@@ -15,6 +16,7 @@ from .content_filter_strategy import *
 from .extraction_strategy import *
 from .async_crawler_strategy import AsyncCrawlerStrategy, AsyncPlaywrightCrawlerStrategy, AsyncCrawlResponse
 from .cache_context import CacheMode, CacheContext, _legacy_to_cache_mode
+from .markdown_generation_strategy import DefaultMarkdownGenerator, MarkdownGenerationStrategy
 from .content_scraping_strategy import WebScrapingStrategy
 from .async_logger import AsyncLogger
 from .async_configs import BrowserConfig, CrawlerRunConfig
@@ -133,16 +135,11 @@ class AsyncWebCrawler:
     async def __aexit__(self, exc_type, exc_val, exc_tb):
         await self.crawler_strategy.__aexit__(exc_type, exc_val, exc_tb)
     
-    @asynccontextmanager
-    async def nullcontext(self):
-        yield
-    
     async def awarmup(self):
         """Initialize the crawler with warm-up sequence."""
         self.logger.info(f"Crawl4AI {crawl4ai_version}", tag="INIT")
         self.ready = True
 
-
     @asynccontextmanager
     async def nullcontext(self):
         """异步空上下文管理器"""
@@ -323,7 +320,8 @@ class AsyncWebCrawler:
                         config=config,  # Pass the config object instead of individual parameters
                         screenshot=screenshot_data,
                         pdf_data=pdf_data,
-                        verbose=config.verbose
+                        verbose=config.verbose,
+                        **kwargs
                     )
 
                     # Set response data
@@ -424,7 +422,8 @@ class AsyncWebCrawler:
                     css_selector=config.css_selector,
                     only_text=config.only_text,
                     image_description_min_word_threshold=config.image_description_min_word_threshold,
-                    content_filter=config.content_filter
+                    content_filter=config.content_filter,
+                    **kwargs
                 )
 
                 if result is None:
@@ -435,16 +434,29 @@ class AsyncWebCrawler:
             except Exception as e:
                 raise ValueError(f"Process HTML, Failed to extract content from the website: {url}, error: {str(e)}")
 
+       
+
             # Extract results
-            markdown_v2 = result.get("markdown_v2", None)
             cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
-            markdown = sanitize_input_encode(result.get("markdown", ""))
             fit_markdown = sanitize_input_encode(result.get("fit_markdown", ""))
             fit_html = sanitize_input_encode(result.get("fit_html", ""))
             media = result.get("media", [])
             links = result.get("links", [])
             metadata = result.get("metadata", {})
 
+            # Markdown Generation
+            markdown_generator: Optional[MarkdownGenerationStrategy] = config.markdown_generator or DefaultMarkdownGenerator()
+            if not config.content_filter and not markdown_generator.content_filter:
+                markdown_generator.content_filter = PruningContentFilter()
+            
+            markdown_result: MarkdownGenerationResult = markdown_generator.generate_markdown(
+                cleaned_html=cleaned_html,
+                base_url=url,
+                # html2text_options=kwargs.get('html2text', {})
+            )
+            markdown_v2 = markdown_result
+            markdown = sanitize_input_encode(markdown_result.raw_markdown)
+
             # Log processing completion
             self.logger.info(
                 message="Processed {url:.50}... | Time: {timing}ms",

crawl4ai/content_scraping_strategy.py

@@ -602,16 +602,16 @@ class WebScrapingStrategy(ContentScrapingStrategy):
 
         cleaned_html = str_body.replace('\n\n', '\n').replace('  ', ' ')
 
-        markdown_content = self._generate_markdown_content(
+        # markdown_content = self._generate_markdown_content(
-            cleaned_html=cleaned_html,
+        #     cleaned_html=cleaned_html,
-            html=html,
+        #     html=html,
-            url=url,
+        #     url=url,
-            success=success,
+        #     success=success,
-            **kwargs
+        #     **kwargs
-        )
+        # )
         
         return {
-            **markdown_content,
+            # **markdown_content,
             'cleaned_html': cleaned_html,
             'success': success,
             'media': media,

docs/examples/llm_extraction_openai_pricing.py

@@ -1,23 +1,21 @@
-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 *
+import asyncio
+from pydantic import BaseModel, Field
 
 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(
+from crawl4ai import AsyncWebCrawler
+
+async def main():
+    # Use AsyncWebCrawler
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
             url=url,
             word_count_threshold=1,
             extraction_strategy= LLMExtractionStrategy(
@@ -25,17 +23,18 @@ result = crawler.run(
                 provider= "groq/llama-3.1-70b-versatile", api_token = os.getenv('GROQ_API_KEY'),
                 schema=OpenAIModelFee.model_json_schema(),
                 extraction_type="schema",
-        instruction="From the crawled content, extract all mentioned model names along with their "\
+                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. "\
+                            "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: '\
+                            '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("Success:", result.success)
+        model_fees = json.loads(result.extracted_content)
+        print(len(model_fees))
 
-print(len(model_fees))
+        with open(".data/data.json", "w", encoding="utf-8") as f:
-
-with open(".data/data.json", "w", encoding="utf-8") as f:
             f.write(result.extracted_content)
+
+asyncio.run(main())

docs/examples/quickstart_async.config.py

@@ -142,6 +142,7 @@ async def extract_structured_data_using_llm(provider: str, api_token: str = None
     crawler_config = CrawlerRunConfig(
         cache_mode=CacheMode.BYPASS,
         word_count_threshold=1,
+        page_timeout = 80000,
         extraction_strategy=LLMExtractionStrategy(
             provider=provider,
             api_token=api_token,
@@ -497,21 +498,21 @@ async def main():
     
     # Advanced examples
     # await extract_structured_data_using_css_extractor()
-    # await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+    await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
     # await crawl_dynamic_content_pages_method_1()
     # await crawl_dynamic_content_pages_method_2()
     
     # Browser comparisons
-    await crawl_custom_browser_type()
+    # await crawl_custom_browser_type()
     
     # Performance testing
     # await speed_comparison()
 
     # Screenshot example
-    await capture_and_save_screenshot(
+    # await capture_and_save_screenshot(
-        "https://www.example.com",
+    #     "https://www.example.com",
-        os.path.join(__location__, "tmp/example_screenshot.jpg")
+    #     os.path.join(__location__, "tmp/example_screenshot.jpg")
-    )
+    # )
 
 if __name__ == "__main__":
     asyncio.run(main())

docs/examples/quickstart_async.py

@@ -239,8 +239,10 @@ async def crawl_dynamic_content_pages_method_1():
         all_commits = []
 
         js_next_page = """
+        (() => {
             const button = document.querySelector('a[data-testid="pagination-next-button"]');
             if (button) button.click();
+        })();
         """
 
         for page in range(3):  # Crawl 3 pages
@@ -604,14 +606,14 @@ async def fit_markdown_remove_overlay():
 
 
 async def main():
-    await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
+    # await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
     
-    await simple_crawl()
+    # await simple_crawl()
-    await simple_example_with_running_js_code()
+    # await simple_example_with_running_js_code()
-    await simple_example_with_css_selector()
+    # await simple_example_with_css_selector()
-    # await use_proxy()
+    # # await use_proxy()
-    await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
+    # await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
-    await extract_structured_data_using_css_extractor()
+    # await extract_structured_data_using_css_extractor()
 
     # LLM extraction examples
     # await extract_structured_data_using_llm()

docs/md_v2/basic/simple-crawling.md

@@ -99,7 +99,7 @@ async def main():
             remove_overlay_elements=True,
             
             # Cache control
-            cache_mode=CacheMode.ENABLE  # Use cache if available
+            cache_mode=CacheMode.ENABLED  # Use cache if available
         )
         
         if result.success:

setup.py

@@ -57,6 +57,9 @@ setup(
     author_email="unclecode@kidocode.com",
     license="MIT",
     packages=find_packages(),
+    package_data={
+        'crawl4ai': ['js_snippet/*.js']  # This matches the exact path structure
+    },
     install_requires=default_requirements
     + ["playwright", "aiofiles"],  # Added aiofiles
     extras_require={