feat(extraction): add RegexExtractionStrategy for pattern-based extraction
Add new RegexExtractionStrategy for fast, zero-LLM extraction of common data types: - Built-in patterns for emails, URLs, phones, dates, and more - Support for custom regex patterns - LLM-assisted pattern generation utility - Optimized HTML preprocessing with fit_html field - Enhanced network response body capture Breaking changes: None
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@@ -503,6 +503,8 @@ class AsyncWebCrawler:
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tables = media.pop("tables", [])
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links = result.links.model_dump()
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metadata = result.metadata
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fit_html = preprocess_html_for_schema(html_content=html, text_threshold= 500, max_size= 300_000)
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################################
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# Generate Markdown #
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@@ -519,7 +521,7 @@ class AsyncWebCrawler:
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html_source_selector = {
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"raw_html": lambda: html, # The original raw HTML
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"cleaned_html": lambda: cleaned_html, # The HTML after scraping strategy
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"fit_html": lambda: preprocess_html_for_schema(html_content=html), # Preprocessed raw HTML
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"fit_html": lambda: fit_html, # The HTML after preprocessing for schema
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}
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markdown_input_html = cleaned_html # Default to cleaned_html
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@@ -593,6 +595,7 @@ class AsyncWebCrawler:
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content = {
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"markdown": markdown_result.raw_markdown,
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"html": html,
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"fit_html": fit_html,
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"cleaned_html": cleaned_html,
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"fit_markdown": markdown_result.fit_markdown,
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}.get(content_format, markdown_result.raw_markdown)
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@@ -600,7 +603,7 @@ class AsyncWebCrawler:
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# Use IdentityChunking for HTML input, otherwise use provided chunking strategy
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chunking = (
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IdentityChunking()
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if content_format in ["html", "cleaned_html"]
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if content_format in ["html", "cleaned_html", "fit_html"]
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else config.chunking_strategy
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)
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sections = chunking.chunk(content)
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@@ -624,6 +627,7 @@ class AsyncWebCrawler:
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return CrawlResult(
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url=url,
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html=html,
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fit_html=fit_html,
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cleaned_html=cleaned_html,
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markdown=markdown_result,
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media=media,
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