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<!DOCTYPE html><html lang="en"> <head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><link rel="icon" href="/assets/favicon.ico"/><script defer="" data-domain="star-history.com" src="https://plausible.io/js/script.js"></script><meta name="next-head-count" content="4"/><link data-next-font="size-adjust" rel="preconnect" href="/" crossorigin="anonymous"/><link rel="preload" href="/_next/static/css/f94657194d4c857a.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/f94657194d4c857a.css" crossorigin="" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-38cee4c0e358b1a3.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-fda0a023b274c574.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-001c9e19b1894c7d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-915effad870aa62e.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/6c86d9ce-d8b7531786dd65a5.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/472-8057db644de3d496.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/590-d0a3c67c09cc0662.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-7b378153203b51eb.js" defer="" crossorigin=""></script><script src="/_next/static/xKX4ZiOi_N7h3OBOEsSZu/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/xKX4ZiOi_N7h3OBOEsSZu/_ssgManifest.js" defer="" crossorigin=""></script></head><body><div id="__next"><div class="relative w-full h-auto min-h-screen overflow-auto flex flex-col"><title>Starlet #26 thepi.pe: Intelligent Document Scraping with Vision-Language Models</title><meta name="description" content="thepi.pe is an open-source package for intelligent document scraping at scale using vision-language models (VLMs)."/><meta property="og:type" content="website"/><meta property="og:url" content="https://star-history.com/blog/thepipe"/><meta property="og:title" content="Starlet #26 thepi.pe: Intelligent Document Scraping with Vision-Language Models"/><meta property="og:description" content="thepi.pe is an open-source package for intelligent document scraping at scale using vision-language models (VLMs)."/><meta property="og:image" content="https://star-history.com/assets/blog/thepipe/banner.webp"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:url" content="https://star-history.com/blog/thepipe"/><meta name="twitter:title" content="Starlet #26 thepi.pe: Intelligent Document Scraping with Vision-Language Models"/><meta name="twitter:description" content="thepi.pe is an open-source package for intelligent document scraping at scale using vision-language models (VLMs)."/><meta name="twitter:image" content="https://star-history.com/assets/blog/thepipe/banner.webp"/><nav><div class="flex justify-center items-center gap-x-6 bg-green-600 px-6 py-1 sm:px-3.5 "><p class="text-sm leading-6 text-white"><a href="/blog/list-your-open-source-project">Want to promote your open source project? 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href="/blog/mockoon"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #3 - Mockoon</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/dlta-ai"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #2 - DLTA-AI</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/sniffnet"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #1 - Sniffnet</span></a></li></ul></div></div></div><div class="w-full flex flex-col justify-start items-center"><div class="w-full p-4 md:p-0 mt-6 md:w-5/6 lg:max-w-6xl h-full flex flex-col justify-start items-center self-center"><img class="hidden md:block w-auto max-w-full object-scale-down" src="/assets/blog/thepipe/banner.webp" alt=""/><div class="w-auto max-w-6xl mt-4 md:mt-12 prose prose-indigo prose-xl md:prose-2xl flex flex-col justify-center items-center"><h1 class="leading-16">Starlet #26 thepi.pe: Intelligent Document Scraping with Vision-Language Models</h1></div><div class="w-full mt-8 mb-2 max-w-6xl px-2 flex flex-row items-center justify-center text-sm text-gray-900 font-semibold trackingwide uppercase"><div class="flex space-x-1 text-gray-500"><span class="text-gray-900">Emmett McFarlane</span><span aria-hidden="true"> · </span><time dateTime="2024-09-25:00:00.000Z">Sep 25, 2024</time><span aria-hidden="true"> · </span><span> 2 min read </span></div></div><div class="mt-8 w-full max-w-5xl prose prose-indigo prose-xl md:prose-2xl"><p><em>This is the twenty-sixth issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our <a href="/blog/list-your-open-source-project">announcement</a>.</em></p> <hr> <p><img src="/assets/blog/thepipe/thepipe-logo.webp" alt="thepi.pe logo"></p> <p><a href="https://thepi.pe/"><strong>thepi.pe</strong></a> is an open-source package for intelligent document scraping at scale using vision-language models (VLMs). It works for dozens of file types. Unlike traditional scrapers, thepi.pe has no problems with poorly scanned PDFs, highly complex visuals, and irregularly formatted tables. It's designed to work with state-of-the-art models like GPT-4o and it integrates easily with LLM APIs, vector databases, and RAG frameworks.</p> <h2>How it works</h2> <p>thepi.pe uses a combination of computer vision models and carefully crafted heuristics to extract clean content from various sources. It processes this content for downstream use with language models or vision transformers.</p> <p><img src="/assets/blog/thepipe/thepipe-workflow.webp" alt="thepi.pe workflow diagram"></p> <p>The output from thepi.pe is a list of chunks containing all content within the source document. These chunks can be easily converted to a prompt format compatible with any LLM or multimodal model using <code>thepipe.core.chunks_to_messages</code>.</p> <h2>Key Features</h2> <ol> <li><strong>Multimodal Extraction</strong>: Extract clean markdown, tables, and images from documents or webpages.</li> <li><strong>Structured Data Extraction</strong>: Extract structured data as JSON or CSV from any document or webpage.</li> <li><strong>VLM-based Document Analysis</strong>: Utilizes AI for filetype detection, layout analysis, and data extraction.</li> <li><strong>Wide Format Support</strong>: Works with PDFs, word docs, powerpoints, webpages, videos, images, and more.</li> <li><strong>LLM Integration</strong>: Seamlessly interfaces with any language model (GPT4, Claude, LLaMa) and vector databases like LlamaIndex.</li> </ol> <h2>Getting Started</h2> <p>Install thepi.pe:</p> <pre><code class="language-bash">pip install thepipe-api </code></pre> <p>Here's a quick example of how to use thepi.pe with the hosted API:</p> <pre><code class="language-python">from thepipe.scraper import scrape_file from thepipe.core import chunks_to_messages from openai import OpenAI # Scrape markdown, tables, visuals chunks = scrape_file(filepath="paper.pdf", ai_extraction=True) # Call LLM with clean, comprehensive data client = OpenAI() response = client.chat.completions.create( model="gpt-4o", messages=chunks_to_messages(chunks), ) </code></pre> <h2>Supported File Types</h2> <p>thepi.pe supports a wide range of file types, including:</p> <ul> <li>Webpages (URLs)</li> <li>PDFs</li> <li>Word Documents</li> <li>PowerPoint Presentations</li> <li>Videos and Audio files</li> <li>Jupyter Notebooks</li> <li>Spreadsheets</li> <li>Images</li> </ul> <p>And it's easily extensible to support any custom file type of your choice.</p> <h2>Learn More and Contribute</h2> <p>If you're excited about intelligent document processing and want to try thepi.pe for yourself, visit our GitHub repository!</p> <ul> <li>GitHub: <a href="https://github.com/emcf/thepipe">https://github.com/emcf/thepipe</a></li> <li>Website: <a href="https://thepi.pe/">https://thepi.pe/</a></li> </ul> <p>Join us in shaping the future of intelligent document processing! 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intelligent document scraping at scale using vision-language models (VLMs).","readingTime":2},"parsedBlogHTML":"\u003cp\u003e\u003cem\u003eThis is the twenty-sixth issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our \u003ca href=\"/blog/list-your-open-source-project\"\u003eannouncement\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/thepipe/thepipe-logo.webp\" alt=\"thepi.pe logo\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://thepi.pe/\"\u003e\u003cstrong\u003ethepi.pe\u003c/strong\u003e\u003c/a\u003e is an open-source package for intelligent document scraping at scale using vision-language models (VLMs). It works for dozens of file types. Unlike traditional scrapers, thepi.pe has no problems with poorly scanned PDFs, highly complex visuals, and irregularly formatted tables. It\u0026#39;s designed to work with state-of-the-art models like GPT-4o and it integrates easily with LLM APIs, vector databases, and RAG frameworks.\u003c/p\u003e\n\u003ch2\u003eHow it works\u003c/h2\u003e\n\u003cp\u003ethepi.pe uses a combination of computer vision models and carefully crafted heuristics to extract clean content from various sources. It processes this content for downstream use with language models or vision transformers.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/thepipe/thepipe-workflow.webp\" alt=\"thepi.pe workflow diagram\"\u003e\u003c/p\u003e\n\u003cp\u003eThe output from thepi.pe is a list of chunks containing all content within the source document. These chunks can be easily converted to a prompt format compatible with any LLM or multimodal model using \u003ccode\u003ethepipe.core.chunks_to_messages\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003eKey Features\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eMultimodal Extraction\u003c/strong\u003e: Extract clean markdown, tables, and images from documents or webpages.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eStructured Data Extraction\u003c/strong\u003e: Extract structured data as JSON or CSV from any document or webpage.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVLM-based Document Analysis\u003c/strong\u003e: Utilizes AI for filetype detection, layout analysis, and data extraction.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWide Format Support\u003c/strong\u003e: Works with PDFs, word docs, powerpoints, webpages, videos, images, and more.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLLM Integration\u003c/strong\u003e: Seamlessly interfaces with any language model (GPT4, Claude, LLaMa) and vector databases like LlamaIndex.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eInstall thepi.pe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode class=\"language-bash\"\u003epip install thepipe-api\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere\u0026#39;s a quick example of how to use thepi.pe with the hosted API:\u003c/p\u003e\n\u003cpre\u003e\u003ccode class=\"language-python\"\u003efrom thepipe.scraper import scrape_file\nfrom thepipe.core import chunks_to_messages\nfrom openai import OpenAI\n\n# Scrape markdown, tables, visuals\nchunks = scrape_file(filepath=\u0026quot;paper.pdf\u0026quot;, ai_extraction=True)\n\n# Call LLM with clean, comprehensive data\nclient = OpenAI()\nresponse = client.chat.completions.create(\n model=\u0026quot;gpt-4o\u0026quot;,\n messages=chunks_to_messages(chunks),\n)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003eSupported File Types\u003c/h2\u003e\n\u003cp\u003ethepi.pe supports a wide range of file types, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWebpages (URLs)\u003c/li\u003e\n\u003cli\u003ePDFs\u003c/li\u003e\n\u003cli\u003eWord Documents\u003c/li\u003e\n\u003cli\u003ePowerPoint Presentations\u003c/li\u003e\n\u003cli\u003eVideos and Audio files\u003c/li\u003e\n\u003cli\u003eJupyter Notebooks\u003c/li\u003e\n\u003cli\u003eSpreadsheets\u003c/li\u003e\n\u003cli\u003eImages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnd it\u0026#39;s easily extensible to support any custom file type of your choice.\u003c/p\u003e\n\u003ch2\u003eLearn More and Contribute\u003c/h2\u003e\n\u003cp\u003eIf you\u0026#39;re excited about intelligent document processing and want to try thepi.pe for yourself, visit our GitHub repository!\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGitHub: \u003ca href=\"https://github.com/emcf/thepipe\"\u003ehttps://github.com/emcf/thepipe\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eWebsite: \u003ca href=\"https://thepi.pe/\"\u003ehttps://thepi.pe/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eJoin us in shaping the future of intelligent document processing!\nThank you to the organizers of Star History\u0026#39;s Starlet program for this opportunity to present thepi.pe to the best minds in the industry.\u003c/p\u003e\n"},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"thepipe"},"buildId":"xKX4ZiOi_N7h3OBOEsSZu","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>