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Use reader-lm locally with ollama
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| # Faster but not private way to achive the above | |
| # is to define a bash function and source it at startup | |
| function html_to_md () { | |
| if [[ $# -eq 2 ]]; then | |
| curl "https://r.jina.ai/$1" > "$2".md | |
| echo "Content saved to \"$2\".md" | |
| else | |
| curl "https://r.jina.ai/$@" | |
| fi | |
| } |
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| import argparse | |
| import ollama | |
| import re | |
| import requests | |
| from math import log, pow | |
| ### Start code from https://jina.ai/news/reader-lm-small-language-models-for-cleaning-and-converting-html-to-markdown/?nocache=1 | |
| def get_html_content(url): | |
| api_url = f'https://r.jina.ai/{url}' | |
| headers = {'X-Return-Format': 'html'} | |
| try: | |
| response = requests.get(api_url, headers=headers, timeout=10) | |
| response.raise_for_status() | |
| return response.text | |
| except requests.exceptions.RequestException as e: | |
| return f"error: {str(e)}" | |
| # (REMOVE <SCRIPT> to </script> and variations) | |
| SCRIPT_PATTERN = r'<[ ]*script.*?\/[ ]*script[ ]*>' # mach any char zero or more times | |
| # text = re.sub(pattern, '', text, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| # (REMOVE HTML <STYLE> to </style> and variations) | |
| STYLE_PATTERN = r'<[ ]*style.*?\/[ ]*style[ ]*>' # mach any char zero or more times | |
| # text = re.sub(pattern, '', text, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| # (REMOVE HTML <META> to </meta> and variations) | |
| META_PATTERN = r'<[ ]*meta.*?>' # mach any char zero or more times | |
| # text = re.sub(pattern, '', text, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| # (REMOVE HTML COMMENTS <!-- to --> and variations) | |
| COMMENT_PATTERN = r'<[ ]*!--.*?--[ ]*>' # mach any char zero or more times | |
| # text = re.sub(pattern, '', text, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| # (REMOVE HTML LINK <LINK> to </link> and variations) | |
| LINK_PATTERN = r'<[ ]*link.*?>' # mach any char zero or more times | |
| # (REPLACE base64 images) | |
| BASE64_IMG_PATTERN = r'<img[^>]+src="data:image/[^;]+;base64,[^"]+"[^>]*>' | |
| # (REPLACE <svg> to </svg> and variations) | |
| SVG_PATTERN = r'(<svg[^>]*>)(.*?)(<\/svg>)' | |
| def replace_svg(html: str, new_content: str = "this is a placeholder") -> str: | |
| return re.sub( | |
| SVG_PATTERN, | |
| lambda match: f"{match.group(1)}{new_content}{match.group(3)}", | |
| html, | |
| flags=re.DOTALL, | |
| ) | |
| def replace_base64_images(html: str, new_image_src: str = "#") -> str: | |
| return re.sub(BASE64_IMG_PATTERN, f'<img src="{new_image_src}"/>', html) | |
| def has_base64_images(text: str) -> bool: | |
| base64_content_pattern = r'data:image/[^;]+;base64,[^"]+' | |
| return bool(re.search(base64_content_pattern, text, flags=re.DOTALL)) | |
| def has_svg_components(text: str) -> bool: | |
| return bool(re.search(SVG_PATTERN, text, flags=re.DOTALL)) | |
| def clean_html(html: str, clean_svg: bool = False, clean_base64: bool = False): | |
| html = re.sub(SCRIPT_PATTERN, '', html, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| html = re.sub(STYLE_PATTERN, '', html, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| html = re.sub(META_PATTERN, '', html, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| html = re.sub(COMMENT_PATTERN, '', html, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| html = re.sub(LINK_PATTERN, '', html, flags=(re.IGNORECASE | re.MULTILINE | re.DOTALL)) | |
| if clean_svg: | |
| html = replace_svg(html) | |
| if clean_base64: | |
| html = replace_base64_images(html) | |
| return html | |
| ### End code from there | |
| def main(): | |
| parser = argparse.ArgumentParser(description="HTML to markdown using reader-lm") | |
| parser.add_argument("url", type=str, help="The URL to process. Will be downloaded from r.jina.ai to ensure dynamic content gets parsed.") | |
| parser.add_argument("-o", "--output", type=str, help="The output filename. If not provided, defaults to the standard output", default=None) | |
| args = parser.parse_args() | |
| html = get_html_content(args.url) | |
| print("Downloaded content") | |
| clean_svg = has_svg_components(html) | |
| clean_base64 = has_base64_images(html) | |
| html = clean_html(html, clean_svg, clean_base64) | |
| print("Cleaned HTML, ready to process") | |
| # print(html) | |
| # Badly approximate context length (makes it much faster on my system) | |
| # Not using a power of 2 makes it hallucinate massively in my tests \/(0.0)\/ | |
| ctx = min(262144, | |
| pow(2, int(log(len(html.split()))/log(2)) + 1) | |
| ) | |
| ctx = max(1024, ctx) | |
| print(f"Approximate context length: {ctx}") | |
| response = ollama.generate( | |
| model="reader-lm", | |
| prompt=html, | |
| stream=True, | |
| options={"temperature":0, "num_ctx": ctx, "num_predict": -1, "top_p": 1.0, "top_k":-1, "max_tokens": 4096} | |
| ) | |
| if args.output: | |
| with open(output_file, "w") as file: | |
| for chunk in response: | |
| file.write(chunk['response']) | |
| else: | |
| for chunk in response: | |
| print(chunk['response'], end="") | |
| if __name__ == "__main__": | |
| main() |
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