- Test all methods

- Update index.hml
- Update Readme
- Resolve some bugs

docs/chunking_strategies.json

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+{
+    "RegexChunking": "### RegexChunking\n\n`RegexChunking` is a text chunking strategy that splits a given text into smaller parts using regular expressions.\nThis is useful for preparing large texts for processing by language models, ensuring they are divided into manageable segments.\n\n#### Constructor Parameters:\n- `patterns` (list, optional): A list of regular expression patterns used to split the text. Default is to split by double newlines (`['\\n\\n']`).\n\n#### Example usage:\n```python\nchunker = RegexChunking(patterns=[r'\\n\\n', r'\\. '])\nchunks = chunker.chunk(\"This is a sample text. It will be split into chunks.\")\n```",
+    
+    "NlpSentenceChunking": "### NlpSentenceChunking\n\n`NlpSentenceChunking` uses a natural language processing model to chunk a given text into sentences. This approach leverages SpaCy to accurately split text based on sentence boundaries.\n\n#### Constructor Parameters:\n- `model` (str, optional): The SpaCy model to use for sentence detection. Default is `'en_core_web_sm'`.\n\n#### Example usage:\n```python\nchunker = NlpSentenceChunking(model='en_core_web_sm')\nchunks = chunker.chunk(\"This is a sample text. It will be split into sentences.\")\n```",
+    
+    "TopicSegmentationChunking": "### TopicSegmentationChunking\n\n`TopicSegmentationChunking` uses the TextTiling algorithm to segment a given text into topic-based chunks. This method identifies thematic boundaries in the text.\n\n#### Constructor Parameters:\n- `num_keywords` (int, optional): The number of keywords to extract for each topic segment. Default is `3`.\n\n#### Example usage:\n```python\nchunker = TopicSegmentationChunking(num_keywords=3)\nchunks = chunker.chunk(\"This is a sample text. It will be split into topic-based segments.\")\n```",
+    
+    "FixedLengthWordChunking": "### FixedLengthWordChunking\n\n`FixedLengthWordChunking` splits a given text into chunks of fixed length, based on the number of words.\n\n#### Constructor Parameters:\n- `chunk_size` (int, optional): The number of words in each chunk. Default is `100`.\n\n#### Example usage:\n```python\nchunker = FixedLengthWordChunking(chunk_size=100)\nchunks = chunker.chunk(\"This is a sample text. It will be split into fixed-length word chunks.\")\n```",
+    
+    "SlidingWindowChunking": "### SlidingWindowChunking\n\n`SlidingWindowChunking` uses a sliding window approach to chunk a given text. Each chunk has a fixed length, and the window slides by a specified step size.\n\n#### Constructor Parameters:\n- `window_size` (int, optional): The number of words in each chunk. Default is `100`.\n- `step` (int, optional): The number of words to slide the window. Default is `50`.\n\n#### Example usage:\n```python\nchunker = SlidingWindowChunking(window_size=100, step=50)\nchunks = chunker.chunk(\"This is a sample text. It will be split using a sliding window approach.\")\n```"
+  }
+  

docs/extraction_strategies.json

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+{
+    "NoExtractionStrategy": "### NoExtractionStrategy\n\n`NoExtractionStrategy` is a basic extraction strategy that returns the entire HTML content without any modification. It is useful for cases where no specific extraction is required. Only clean html, and amrkdown.\n\n#### Constructor Parameters:\nNone.\n\n#### Example usage:\n```python\nextractor = NoExtractionStrategy()\nextracted_content = extractor.extract(url, html)\n```",
+    
+    "LLMExtractionStrategy": "### LLMExtractionStrategy\n\n`LLMExtractionStrategy` uses a Language Model (LLM) to extract meaningful blocks or chunks from the given HTML content. This strategy leverages an external provider for language model completions.\n\n#### Constructor Parameters:\n- `provider` (str, optional): The provider to use for the language model completions. Default is `DEFAULT_PROVIDER` (following provider/model eg. openai/gpt-4o).\n- `api_token` (str, optional): The API token for the provider. If not provided, it will try to load from the environment variable `OPENAI_API_KEY`.\n\n#### Example usage:\n```python\nextractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token')\nextracted_content = extractor.extract(url, html)\n```",
+    
+    "CosineStrategy": "### CosineStrategy\n\n`CosineStrategy` uses hierarchical clustering based on cosine similarity to extract clusters of text from the given HTML content. This strategy is suitable for identifying related content sections.\n\n#### Constructor Parameters:\n- `word_count_threshold` (int, optional): Minimum number of words per cluster. Default is `20`.\n- `max_dist` (float, optional): The maximum cophenetic distance on the dendrogram to form clusters. Default is `0.2`.\n- `linkage_method` (str, optional): The linkage method for hierarchical clustering. Default is `'ward'`.\n- `top_k` (int, optional): Number of top categories to extract. Default is `3`.\n- `model_name` (str, optional): The model name for embedding generation. Default is `'BAAI/bge-small-en-v1.5'`.\n\n#### Example usage:\n```python\nextractor = CosineStrategy(word_count_threshold=20, max_dist=0.2, linkage_method='ward', top_k=3, model_name='BAAI/bge-small-en-v1.5')\nextracted_content = extractor.extract(url, html)\n```",
+    
+    "TopicExtractionStrategy": "### TopicExtractionStrategy\n\n`TopicExtractionStrategy` uses the TextTiling algorithm to segment the HTML content into topics and extracts keywords for each segment. This strategy is useful for identifying and summarizing thematic content.\n\n#### Constructor Parameters:\n- `num_keywords` (int, optional): Number of keywords to represent each topic segment. Default is `3`.\n\n#### Example usage:\n```python\nextractor = TopicExtractionStrategy(num_keywords=3)\nextracted_content = extractor.extract(url, html)\n```"
+  }
+  

docs/quickstart.py

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+import os
+from crawl4ai.web_crawler import WebCrawler
+from crawl4ai.chunking_strategy import *
+from crawl4ai.extraction_strategy import *
+
+
+def main():
+    crawler = WebCrawler()
+    crawler.warmup()
+    
+    # Single page crawl
+    result = crawler.run(
+        url="https://www.nbcnews.com/business",
+        word_count_threshold=5,  # Minimum word count for a HTML tag to be considered as a worthy block
+        chunking_strategy=RegexChunking(patterns=["\n\n"]),  # Default is RegexChunking
+        extraction_strategy=CosineStrategy(
+            word_count_threshold=20, max_dist=0.2, linkage_method="ward", top_k=3
+        ),  # Default is CosineStrategy
+        # extraction_strategy= LLMExtractionStrategy(provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY')),
+        bypass_cache=True,
+        extract_blocks=True,  # Whether to extract semantical blocks of text from the HTML
+        css_selector="",  # Eg: "div.article-body" or all H2 tags liek "h2"
+        verbose=True,
+        include_raw_html=True,  # Whether to include the raw HTML content in the response
+    )
+    
+
+    print("[LOG] 📦 Crawl result:")
+    print(result.model_dump())
+
+
+if __name__ == "__main__":
+    main()