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7559 lines
822 KiB
HTML
7559 lines
822 KiB
HTML
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<link rel="canonical" href="https://docling-project.github.io/docling/examples/rag_opensearch/">
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<link rel="prev" href="../rag_milvus/">
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<link rel="next" href="../rag_weaviate/">
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<meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.7.0">
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<title>RAG with OpenSearch - Docling</title>
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<style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
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<script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
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<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-MP75NXFDH4"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-MP75NXFDH4",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-MP75NXFDH4",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
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</head>
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<body dir="ltr" data-md-color-scheme="slate" data-md-color-primary="black" data-md-color-accent="indigo">
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<input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
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<input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
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<label class="md-overlay" for="__drawer"></label>
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<div data-md-component="skip">
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<a href="#rag-with-opensearch" class="md-skip">
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Skip to content
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<header class="md-header" data-md-component="header">
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<img src="../../assets/logo.png" alt="logo">
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</a>
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<label class="md-header__button md-icon" for="__drawer">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
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</label>
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<div class="md-header__title" data-md-component="header-title">
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<div class="md-header__ellipsis">
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<div class="md-header__topic">
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<span class="md-ellipsis">
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Docling
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</span>
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</div>
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<div class="md-header__topic" data-md-component="header-topic">
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<span class="md-ellipsis">
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RAG with OpenSearch
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</span>
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</div>
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</div>
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</div>
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<form class="md-header__option" data-md-component="palette">
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<input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="black" data-md-color-accent="indigo" aria-label="Switch to light mode" type="radio" name="__palette" id="__palette_0">
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<label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12z"/></svg>
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<input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="black" data-md-color-accent="indigo" aria-label="Switch to dark mode" type="radio" name="__palette" id="__palette_1">
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<input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="black" data-md-color-accent="indigo" aria-label="Switch to system preference" type="radio" name="__palette" id="__palette_2">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m14.3 16-.7-2h-3.2l-.7 2H7.8L11 7h2l3.2 9zM20 8.69V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12zm-9.15 3.96h2.3L12 9z"/></svg>
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</form>
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<script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
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<label class="md-header__button md-icon" for="__search">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
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</label>
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<div class="md-search" data-md-component="search" role="dialog">
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<label class="md-search__overlay" for="__search"></label>
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<div class="md-search__inner" role="search">
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<form class="md-search__form" name="search">
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<input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
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<label class="md-search__icon md-icon" for="__search">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
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</label>
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<nav class="md-search__options" aria-label="Search">
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<button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
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</button>
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</nav>
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<div class="md-search__suggest" data-md-component="search-suggest"></div>
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</form>
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<div class="md-search__output">
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<div class="md-search__scrollwrap" tabindex="0" data-md-scrollfix>
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<div class="md-search-result" data-md-component="search-result">
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<div class="md-search-result__meta">
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Initializing search
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</div>
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<ol class="md-search-result__list" role="presentation"></ol>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="md-header__source">
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<a href="https://github.com/docling-project/docling" title="Go to repository" class="md-source" data-md-component="source">
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<div class="md-source__icon md-icon">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 7.1.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2025 Fonticons, Inc.--><path d="M439.6 236.1 244 40.5c-5.4-5.5-12.8-8.5-20.4-8.5s-15 3-20.4 8.4L162.5 81l51.5 51.5c27.1-9.1 52.7 16.8 43.4 43.7l49.7 49.7c34.2-11.8 61.2 31 35.5 56.7-26.5 26.5-70.2-2.9-56-37.3L240.3 199v121.9c25.3 12.5 22.3 41.8 9.1 55-6.4 6.4-15.2 10.1-24.3 10.1s-17.8-3.6-24.3-10.1c-17.6-17.6-11.1-46.9 11.2-56v-123c-20.8-8.5-24.6-30.7-18.6-45L142.6 101 8.5 235.1C3 240.6 0 247.9 0 255.5s3 15 8.5 20.4l195.6 195.7c5.4 5.4 12.7 8.4 20.4 8.4s15-3 20.4-8.4l194.7-194.7c5.4-5.4 8.4-12.8 8.4-20.4s-3-15-8.4-20.4"/></svg>
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</div>
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<div class="md-source__repository">
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docling-project/docling
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</div>
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</a>
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</nav>
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<div class="md-container" data-md-component="container">
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<div class="md-grid">
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<ul class="md-tabs__list">
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<li class="md-tabs__item">
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<a href="../.." class="md-tabs__link">
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Documentation
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</a>
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<li class="md-tabs__item">
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<a href="../../concepts/" class="md-tabs__link">
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Concepts
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</a>
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</li>
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<li class="md-tabs__item md-tabs__item--active">
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<a href="../" class="md-tabs__link">
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Examples
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</a>
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<li class="md-tabs__item">
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<a href="../../integrations/" class="md-tabs__link">
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Integrations
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</a>
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<li class="md-tabs__item">
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<a href="../../reference/document_converter/" class="md-tabs__link">
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Reference
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</a>
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</li>
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</ul>
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</div>
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</nav>
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<main class="md-main" data-md-component="main">
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<div class="md-main__inner md-grid">
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<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
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<div class="md-sidebar__scrollwrap">
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<div class="md-sidebar__inner">
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<nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
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<label class="md-nav__title" for="__drawer">
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<a href="../.." title="Docling" class="md-nav__button md-logo" aria-label="Docling" data-md-component="logo">
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<img src="../../assets/logo.png" alt="logo">
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</a>
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Docling
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</label>
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<div class="md-nav__source">
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<a href="https://github.com/docling-project/docling" title="Go to repository" class="md-source" data-md-component="source">
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<div class="md-source__icon md-icon">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 7.1.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2025 Fonticons, Inc.--><path d="M439.6 236.1 244 40.5c-5.4-5.5-12.8-8.5-20.4-8.5s-15 3-20.4 8.4L162.5 81l51.5 51.5c27.1-9.1 52.7 16.8 43.4 43.7l49.7 49.7c34.2-11.8 61.2 31 35.5 56.7-26.5 26.5-70.2-2.9-56-37.3L240.3 199v121.9c25.3 12.5 22.3 41.8 9.1 55-6.4 6.4-15.2 10.1-24.3 10.1s-17.8-3.6-24.3-10.1c-17.6-17.6-11.1-46.9 11.2-56v-123c-20.8-8.5-24.6-30.7-18.6-45L142.6 101 8.5 235.1C3 240.6 0 247.9 0 255.5s3 15 8.5 20.4l195.6 195.7c5.4 5.4 12.7 8.4 20.4 8.4s15-3 20.4-8.4l194.7-194.7c5.4-5.4 8.4-12.8 8.4-20.4s-3-15-8.4-20.4"/></svg>
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</div>
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<div class="md-source__repository">
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docling-project/docling
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</a>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_1" >
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<div class="md-nav__link md-nav__container">
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<a href="../.." class="md-nav__link ">
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<span class="md-ellipsis">
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Documentation
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</a>
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<label class="md-nav__link " for="__nav_1" id="__nav_1_label" tabindex="0">
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<span class="md-nav__icon md-icon"></span>
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</label>
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</div>
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|
||
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_1_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_1">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Documentation
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_1_2" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_1_2" id="__nav_1_2_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Getting started
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_1_2_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_1_2">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Getting started
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../getting_started/installation/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Installation
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../getting_started/quickstart/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Quickstart
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_1_3" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_1_3" id="__nav_1_3_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Usage
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_1_3_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_1_3">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Usage
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/advanced_options/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Advanced options
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/supported_formats/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Supported formats
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/enrichments/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Enrichment features
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/vision_models/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Vision models
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/gpu/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
GPU support
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/mcp/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
MCP server
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../usage/jobkit/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Jobkit
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_1_4" >
|
||
|
||
|
||
<div class="md-nav__link md-nav__container">
|
||
<a href="../../faq/" class="md-nav__link ">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
FAQ
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
</div>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_1_4_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_1_4">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
FAQ
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
|
||
|
||
|
||
<div class="md-nav__link md-nav__container">
|
||
<a href="../../concepts/" class="md-nav__link ">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Concepts
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
<label class="md-nav__link " for="__nav_2" id="__nav_2_label" tabindex="0">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
</div>
|
||
|
||
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_2">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Concepts
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/architecture/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Architecture
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/docling_document/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Docling Document
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/serialization/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Serialization
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/confidence_scores/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Confidence Scores
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/chunking/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Chunking
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../concepts/plugins/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Plugins
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
|
||
|
||
|
||
<div class="md-nav__link md-nav__container">
|
||
<a href="../" class="md-nav__link ">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Examples
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
<label class="md-nav__link " for="__nav_3" id="__nav_3_label" tabindex="">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
</div>
|
||
|
||
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
|
||
<label class="md-nav__title" for="__nav_3">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Examples
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🔀 Conversion
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_2">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🔀 Conversion
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../minimal/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Simple conversion
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../custom_convert/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Custom conversion
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../batch_convert/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Batch conversion
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../run_with_formats/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Multi-format conversion
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../minimal_vlm_pipeline/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
VLM pipeline with GraniteDocling
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../vlm_pipeline_api_model/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
VLM pipeline with remote model
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../compare_vlm_models/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
VLM comparison
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../minimal_asr_pipeline/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
ASR pipeline with Whisper
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../export_figures/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Figure export
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../export_tables/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Table export
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../export_multimodal/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Multimodal export
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../full_page_ocr/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Force full page OCR
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../tesseract_lang_detection/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Automatic OCR language detection with tesseract
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rapidocr_with_custom_models/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RapidOCR with custom OCR models
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../suryaocr_with_custom_models/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
SuryaOCR with custom OCR models
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../run_with_accelerator/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Accelerator options
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../pii_obfuscate/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Detect and obfuscate PII
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../translate/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Simple translation
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../backend_csv/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Conversion of CSV files
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../backend_xml_rag/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Conversion of custom XML
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
✂️ Serialization & chunking
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_3">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
✂️ Serialization & chunking
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../serialization/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Serialization
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../hybrid_chunking/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Hybrid chunking
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../advanced_chunking_and_serialization/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Advanced chunking & serialization
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
📤 Information extraction
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_4">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
📤 Information extraction
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../extraction/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Information extraction
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🤖 RAG with AI dev frameworks
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_5">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🤖 RAG with AI dev frameworks
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_haystack/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Haystack
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_langchain/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with LangChain
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_llamaindex/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with LlamaIndex
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../visual_grounding/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Visual grounding
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_6" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_6" id="__nav_3_6_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🖼️ Picture annotation
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_6_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_6">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🖼️ Picture annotation
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../pictures_description/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Annotate picture with local VLM
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../pictures_description_api/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Annotate picture with remote VLM
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_7" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_7" id="__nav_3_7_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
✨ Enrichment development
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_7_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_7">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
✨ Enrichment development
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../develop_picture_enrichment/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Figure enrichment
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../develop_formula_understanding/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Formula enrichment
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../enrich_doclingdocument/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Enrich a DoclingDocument
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_8" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_8" id="__nav_3_8_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
⚡️ GPU optimization
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_8_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_3_8">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
⚡️ GPU optimization
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../gpu_standard_pipeline/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Standard pipeline
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../gpu_vlm_pipeline/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
VLM pipeline
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../parquet_images/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Parquet benchmark
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--active md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_9" checked>
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_3_9" id="__nav_3_9_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🗂️ More examples
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_9_label" aria-expanded="true">
|
||
<label class="md-nav__title" for="__nav_3_9">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🗂️ More examples
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../dpk-ingest-chunk-tokenize/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Chunking & tokenization with Data Prep Kit
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_azuresearch/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Azure AI Search
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Docling_RAG.ipynb" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Granite [↗]
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_milvus/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Milvus
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--active">
|
||
|
||
<input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__link md-nav__link--active" for="__toc">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with OpenSearch
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<a href="./" class="md-nav__link md-nav__link--active">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with OpenSearch
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#preparation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Preparation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Preparation">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#running-the-notebook" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Running the notebook
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#virtual-environment" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Virtual environment
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#dependencies" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Dependencies
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#gpu-checking" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
GPU Checking
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#local-opensearch-instance" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Local OpenSearch instance
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#language-models" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Language models
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#setup" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Setup
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#process-data-using-docling" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Process Data Using Docling
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Process Data Using Docling">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#run-the-document-conversion-pipeline" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Run the document conversion pipeline
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#load-data-into-opensearch" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Load Data into OpenSearch
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Load Data into OpenSearch">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#define-the-transformations" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Define the Transformations
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#embed-and-insert-the-data" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Embed and Insert the Data
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#build-rag" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Build RAG
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Build RAG">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#run-a-query" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Run a query
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#custom-serializers" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
Custom serializers
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
||
<li class="md-nav__item">
|
||
<a href="#filter-context-query" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
Filter-context Query
|
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|
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</span>
|
||
</a>
|
||
|
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</li>
|
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|
||
<li class="md-nav__item">
|
||
<a href="#hybrid-search-retrieval-with-rrf" class="md-nav__link">
|
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<span class="md-ellipsis">
|
||
|
||
Hybrid Search Retrieval with RRF
|
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|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#context-expansion" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
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Context expansion
|
||
|
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</span>
|
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</a>
|
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|
||
</li>
|
||
|
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</ul>
|
||
</nav>
|
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|
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</li>
|
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|
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</ul>
|
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|
||
</nav>
|
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|
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</li>
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../rag_weaviate/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Weaviate
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../retrieval_qdrant/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Retrieval with Qdrant
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../rag_mongodb/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with MongoDB + VoyageAI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="https://github.com/workloads/pathfinder-prism" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RAG with Vault PII transform [↗]
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
|
||
|
||
|
||
<div class="md-nav__link md-nav__container">
|
||
<a href="../../integrations/" class="md-nav__link ">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Integrations
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
<label class="md-nav__link " for="__nav_4" id="__nav_4_label" tabindex="0">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
</div>
|
||
|
||
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_4">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Integrations
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4_2" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_4_2" id="__nav_4_2_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🤖 Agentic / AI dev frameworks
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_4_2_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_4_2">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🤖 Agentic / AI dev frameworks
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/bee/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Bee Agent Framework
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/crewai/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Crew AI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/haystack/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Haystack
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/hector/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Hector
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/langchain/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
LangChain
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/langflow/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Langflow
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/llamaindex/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
LlamaIndex
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/txtai/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
txtai
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
||
|
||
|
||
|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4_3" >
|
||
|
||
|
||
<label class="md-nav__link" for="__nav_4_3" id="__nav_4_3_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
⭐️ Featured
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_4_3_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_4_3">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
⭐️ Featured
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/apify/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Apify
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/data_prep_kit/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Data Prep Kit
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/instructlab/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
InstructLab
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/nvidia/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
NVIDIA
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/prodigy/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Prodigy
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/rhel_ai/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
RHEL AI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/spacy/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
spaCy
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
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|
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|
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|
||
<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4_4" >
|
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|
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|
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<label class="md-nav__link" for="__nav_4_4" id="__nav_4_4_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🗂️ More integrations
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
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|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_4_4_label" aria-expanded="false">
|
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<label class="md-nav__title" for="__nav_4_4">
|
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<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
🗂️ More integrations
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
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|
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|
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|
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|
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|
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|
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|
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<li class="md-nav__item">
|
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<a href="../../integrations/arconia/" class="md-nav__link">
|
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|
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
Arconia
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/cloudera/" class="md-nav__link">
|
||
|
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|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Cloudera
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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|
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|
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|
||
|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/docetl/" class="md-nav__link">
|
||
|
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
DocETL
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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|
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|
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|
||
|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/kotaemon/" class="md-nav__link">
|
||
|
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
Kotaemon
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
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|
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|
||
|
||
|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/opencontracts/" class="md-nav__link">
|
||
|
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|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
OpenContracts
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/openwebui/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Open WebUI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/quarkus/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Quarkus
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../integrations/vectara/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Vectara
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
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|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
<li class="md-nav__item md-nav__item--nested">
|
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|
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|
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|
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
|
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|
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|
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<label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
|
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|
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|
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|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Reference
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_5">
|
||
<span class="md-nav__icon md-icon"></span>
|
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|
||
|
||
Reference
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
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|
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|
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|
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|
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|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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|
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|
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|
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|
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|
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<li class="md-nav__item md-nav__item--nested">
|
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|
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|
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|
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5_1" >
|
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|
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|
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<label class="md-nav__link" for="__nav_5_1" id="__nav_5_1_label" tabindex="0">
|
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|
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|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Python API
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
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|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_5_1_label" aria-expanded="false">
|
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<label class="md-nav__title" for="__nav_5_1">
|
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<span class="md-nav__icon md-icon"></span>
|
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|
||
|
||
Python API
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../reference/document_converter/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Document Converter
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../reference/pipeline_options/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Pipeline options
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../reference/docling_document/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Docling Document
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--nested">
|
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|
||
|
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|
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5_2" >
|
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|
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|
||
<label class="md-nav__link" for="__nav_5_2" id="__nav_5_2_label" tabindex="0">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
CLI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<nav class="md-nav" data-md-level="2" aria-labelledby="__nav_5_2_label" aria-expanded="false">
|
||
<label class="md-nav__title" for="__nav_5_2">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
CLI
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
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|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../reference/cli/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
CLI reference
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
|
||
<div class="md-sidebar__scrollwrap">
|
||
<div class="md-sidebar__inner">
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#preparation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Preparation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Preparation">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#running-the-notebook" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Running the notebook
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#virtual-environment" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Virtual environment
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#dependencies" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Dependencies
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#gpu-checking" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
GPU Checking
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#local-opensearch-instance" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Local OpenSearch instance
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#language-models" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Language models
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#setup" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Setup
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#process-data-using-docling" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Process Data Using Docling
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Process Data Using Docling">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#run-the-document-conversion-pipeline" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Run the document conversion pipeline
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#load-data-into-opensearch" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Load Data into OpenSearch
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Load Data into OpenSearch">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#define-the-transformations" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Define the Transformations
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#embed-and-insert-the-data" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Embed and Insert the Data
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#build-rag" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Build RAG
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Build RAG">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#run-a-query" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Run a query
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#custom-serializers" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Custom serializers
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#filter-context-query" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Filter-context Query
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#hybrid-search-retrieval-with-rrf" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Hybrid Search Retrieval with RRF
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#context-expansion" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Context expansion
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<nav class="md-path" aria-label="Navigation" >
|
||
<ol class="md-path__list">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-path__item">
|
||
<a href="../" class="md-path__link">
|
||
|
||
<span class="md-ellipsis">
|
||
Examples
|
||
</span>
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-path__item">
|
||
<a href="../dpk-ingest-chunk-tokenize/" class="md-path__link">
|
||
|
||
<span class="md-ellipsis">
|
||
🗂️ More examples
|
||
</span>
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ol>
|
||
</nav>
|
||
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script>
|
||
(function (global, factory) {
|
||
typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
|
||
typeof define === 'function' && define.amd ? define(factory) :
|
||
(global = global || self, global.ClipboardCopyElement = factory());
|
||
}(this, function () { 'use strict';
|
||
|
||
function createNode(text) {
|
||
const node = document.createElement('pre');
|
||
node.style.width = '1px';
|
||
node.style.height = '1px';
|
||
node.style.position = 'fixed';
|
||
node.style.top = '5px';
|
||
node.textContent = text;
|
||
return node;
|
||
}
|
||
|
||
function copyNode(node) {
|
||
if ('clipboard' in navigator) {
|
||
// eslint-disable-next-line flowtype/no-flow-fix-me-comments
|
||
// $FlowFixMe Clipboard is not defined in Flow yet.
|
||
return navigator.clipboard.writeText(node.textContent);
|
||
}
|
||
|
||
const selection = getSelection();
|
||
|
||
if (selection == null) {
|
||
return Promise.reject(new Error());
|
||
}
|
||
|
||
selection.removeAllRanges();
|
||
const range = document.createRange();
|
||
range.selectNodeContents(node);
|
||
selection.addRange(range);
|
||
document.execCommand('copy');
|
||
selection.removeAllRanges();
|
||
return Promise.resolve();
|
||
}
|
||
function copyText(text) {
|
||
if ('clipboard' in navigator) {
|
||
// eslint-disable-next-line flowtype/no-flow-fix-me-comments
|
||
// $FlowFixMe Clipboard is not defined in Flow yet.
|
||
return navigator.clipboard.writeText(text);
|
||
}
|
||
|
||
const body = document.body;
|
||
|
||
if (!body) {
|
||
return Promise.reject(new Error());
|
||
}
|
||
|
||
const node = createNode(text);
|
||
body.appendChild(node);
|
||
copyNode(node);
|
||
body.removeChild(node);
|
||
return Promise.resolve();
|
||
}
|
||
|
||
function copy(button) {
|
||
const id = button.getAttribute('for');
|
||
const text = button.getAttribute('value');
|
||
|
||
function trigger() {
|
||
button.dispatchEvent(new CustomEvent('clipboard-copy', {
|
||
bubbles: true
|
||
}));
|
||
}
|
||
|
||
if (text) {
|
||
copyText(text).then(trigger);
|
||
} else if (id) {
|
||
const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
|
||
if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
|
||
const node = root.getElementById(id);
|
||
if (node) copyTarget(node).then(trigger);
|
||
}
|
||
}
|
||
|
||
function copyTarget(content) {
|
||
if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
|
||
return copyText(content.value);
|
||
} else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
|
||
return copyText(content.href);
|
||
} else {
|
||
return copyNode(content);
|
||
}
|
||
}
|
||
|
||
function clicked(event) {
|
||
const button = event.currentTarget;
|
||
|
||
if (button instanceof HTMLElement) {
|
||
copy(button);
|
||
}
|
||
}
|
||
|
||
function keydown(event) {
|
||
if (event.key === ' ' || event.key === 'Enter') {
|
||
const button = event.currentTarget;
|
||
|
||
if (button instanceof HTMLElement) {
|
||
event.preventDefault();
|
||
copy(button);
|
||
}
|
||
}
|
||
}
|
||
|
||
function focused(event) {
|
||
event.currentTarget.addEventListener('keydown', keydown);
|
||
}
|
||
|
||
function blurred(event) {
|
||
event.currentTarget.removeEventListener('keydown', keydown);
|
||
}
|
||
|
||
class ClipboardCopyElement extends HTMLElement {
|
||
constructor() {
|
||
super();
|
||
this.addEventListener('click', clicked);
|
||
this.addEventListener('focus', focused);
|
||
this.addEventListener('blur', blurred);
|
||
}
|
||
|
||
connectedCallback() {
|
||
if (!this.hasAttribute('tabindex')) {
|
||
this.setAttribute('tabindex', '0');
|
||
}
|
||
|
||
if (!this.hasAttribute('role')) {
|
||
this.setAttribute('role', 'button');
|
||
}
|
||
}
|
||
|
||
get value() {
|
||
return this.getAttribute('value') || '';
|
||
}
|
||
|
||
set value(text) {
|
||
this.setAttribute('value', text);
|
||
}
|
||
|
||
}
|
||
|
||
if (!window.customElements.get('clipboard-copy')) {
|
||
window.ClipboardCopyElement = ClipboardCopyElement;
|
||
window.customElements.define('clipboard-copy', ClipboardCopyElement);
|
||
}
|
||
|
||
return ClipboardCopyElement;
|
||
|
||
}));
|
||
</script>
|
||
<script>
|
||
document.addEventListener('clipboard-copy', function(event) {
|
||
const notice = event.target.querySelector('.notice')
|
||
notice.hidden = false
|
||
setTimeout(function() {
|
||
notice.hidden = true
|
||
}, 1000)
|
||
})
|
||
</script>
|
||
<style type="text/css">
|
||
pre { line-height: 125%; }
|
||
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||
.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
|
||
.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
|
||
.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
|
||
.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
|
||
.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
|
||
.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
|
||
.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
|
||
.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
|
||
.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
|
||
.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
|
||
.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
|
||
.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
|
||
.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
|
||
.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
|
||
.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
|
||
.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
|
||
.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
|
||
.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
|
||
.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
|
||
.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
|
||
.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
|
||
.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
|
||
.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
|
||
.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
|
||
.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
|
||
.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
|
||
.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
|
||
.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
|
||
.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
|
||
.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
|
||
.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
|
||
.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
|
||
.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
|
||
.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
|
||
.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
|
||
.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
|
||
.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
|
||
.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
|
||
.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
|
||
.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
|
||
.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
|
||
.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
|
||
.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
|
||
</style>
|
||
<style type="text/css">
|
||
@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 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<div class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
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|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h1 id="rag-with-opensearch">RAG with OpenSearch<a class="anchor-link" href="#rag-with-opensearch">¶</a></h1><table>
|
||
<thead>
|
||
<tr>
|
||
<th>Step</th>
|
||
<th>Tech</th>
|
||
<th>Execution</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Embedding</td>
|
||
<td>HuggingFace (IBM Granite Embedding 30M)</td>
|
||
<td>💻 Local</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Vector store</td>
|
||
<td>OpenSearch 3.0.0</td>
|
||
<td>💻 Local</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Gen AI</td>
|
||
<td>Ollama (IBM Granite 4.0 Tiny)</td>
|
||
<td>💻 Local</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>This is a code recipe that uses <a href="https://opensearch.org/">OpenSearch</a>, an open-source search and analytics tool,
|
||
and the <a href="https://github.com/run-llama/llama_index">LlamaIndex</a> framework to perform RAG over documents parsed by <a href="https://docling-project.github.io/docling/">Docling</a>.</p>
|
||
<p>In this notebook, we accomplish the following:</p>
|
||
<ul>
|
||
<li>📚 Parse documents using Docling's document conversion capabilities</li>
|
||
<li>🧩 Perform hierarchical chunking of the documents using Docling</li>
|
||
<li>🔢 Generate text embeddings on document chunks</li>
|
||
<li>🤖 Perform RAG using OpenSearch and the LlamaIndex framework</li>
|
||
<li>🛠️ Leverage the transformation and structure capabilities of Docling documents for RAG</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h2 id="preparation">Preparation<a class="anchor-link" href="#preparation">¶</a></h2><h3 id="running-the-notebook">Running the notebook<a class="anchor-link" href="#running-the-notebook">¶</a></h3><p>For running this notebook on your machine, you can use applications like <a href="https://jupyter.org/install">Jupyter Notebook</a> or <a href="https://code.visualstudio.com/docs/datascience/jupyter-notebooks">Visual Studio Code</a>.</p>
|
||
<p>💡 For best results, please use <strong>GPU acceleration</strong> to run this notebook.</p>
|
||
<h3 id="virtual-environment">Virtual environment<a class="anchor-link" href="#virtual-environment">¶</a></h3><p>Before installing dependencies and to avoid conflicts in your environment, it is advisable to use a <a href="https://docs.python.org/3/library/venv.html">virtual environment (venv)</a>.
|
||
For instance, <a href="https://docs.astral.sh/uv/">uv</a> is a popular tool to manage virtual environments and dependencies. You can install it with:</p>
|
||
<div class="highlight"><pre><span></span>curl<span class="w"> </span>-LsSf<span class="w"> </span>https://astral.sh/uv/install.sh<span class="w"> </span><span class="p">|</span><span class="w"> </span>sh
|
||
</pre></div>
|
||
<p>Then create the virtual environment and activate it:</p>
|
||
<div class="highlight"><pre><span></span><span class="w"> </span>uv<span class="w"> </span>venv
|
||
<span class="w"> </span><span class="nb">source</span><span class="w"> </span>.venv/bin/activate
|
||
</pre></div>
|
||
<p>Refer to <a href="https://docs.astral.sh/uv/getting-started/installation/">Installing uv</a> for more details.</p>
|
||
<h3 id="dependencies">Dependencies<a class="anchor-link" href="#dependencies">¶</a></h3><p>To start, install the required dependencies by running the following command:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
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</div>
|
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<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-1">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
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<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
|
||
|
||
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"TOKENIZERS_PARALLELISM"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"false"</span>
|
||
|
||
<span class="err">!</span> <span class="n">uv</span> <span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">q</span> <span class="o">--</span><span class="n">no</span><span class="o">-</span><span class="n">progress</span> <span class="n">notebook</span> <span class="n">ipywidgets</span> <span class="n">docling</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">readers</span><span class="o">-</span><span class="n">file</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">readers</span><span class="o">-</span><span class="n">docling</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">readers</span><span class="o">-</span><span class="n">elasticsearch</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">node</span><span class="o">-</span><span class="n">parser</span><span class="o">-</span><span class="n">docling</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">vector</span><span class="o">-</span><span class="n">stores</span><span class="o">-</span><span class="n">opensearch</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">embeddings</span><span class="o">-</span><span class="n">huggingface</span> <span class="n">llama</span><span class="o">-</span><span class="n">index</span><span class="o">-</span><span class="n">llms</span><span class="o">-</span><span class="n">ollama</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-1">import os
|
||
|
||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||
|
||
! uv pip install -q --no-progress notebook ipywidgets docling llama-index-readers-file llama-index-readers-docling llama-index-readers-elasticsearch llama-index-node-parser-docling llama-index-vector-stores-opensearch llama-index-embeddings-huggingface llama-index-llms-ollama</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>We now import all the necessary modules for this notebook:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-2">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">tempfile</span><span class="w"> </span><span class="kn">import</span> <span class="n">mkdtemp</span>
|
||
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">requests</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">docling_core.transforms.chunker</span><span class="w"> </span><span class="kn">import</span> <span class="n">HierarchicalChunker</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">docling_core.transforms.chunker.hierarchical_chunker</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
|
||
<span class="n">ChunkingDocSerializer</span><span class="p">,</span>
|
||
<span class="n">ChunkingSerializerProvider</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">docling_core.transforms.chunker.tokenizer.huggingface</span><span class="w"> </span><span class="kn">import</span> <span class="n">HuggingFaceTokenizer</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">docling_core.transforms.serializer.markdown</span><span class="w"> </span><span class="kn">import</span> <span class="n">MarkdownTableSerializer</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core</span><span class="w"> </span><span class="kn">import</span> <span class="n">SimpleDirectoryReader</span><span class="p">,</span> <span class="n">StorageContext</span><span class="p">,</span> <span class="n">VectorStoreIndex</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core.data_structs</span><span class="w"> </span><span class="kn">import</span> <span class="n">Node</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core.response_synthesizers</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_response_synthesizer</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core.schema</span><span class="w"> </span><span class="kn">import</span> <span class="n">NodeWithScore</span><span class="p">,</span> <span class="n">TransformComponent</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core.vector_stores</span><span class="w"> </span><span class="kn">import</span> <span class="n">MetadataFilter</span><span class="p">,</span> <span class="n">MetadataFilters</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.core.vector_stores.types</span><span class="w"> </span><span class="kn">import</span> <span class="n">VectorStoreQueryMode</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.embeddings.huggingface</span><span class="w"> </span><span class="kn">import</span> <span class="n">HuggingFaceEmbedding</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.llms.ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">Ollama</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.node_parser.docling</span><span class="w"> </span><span class="kn">import</span> <span class="n">DoclingNodeParser</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.readers.docling</span><span class="w"> </span><span class="kn">import</span> <span class="n">DoclingReader</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.readers.elasticsearch</span><span class="w"> </span><span class="kn">import</span> <span class="n">ElasticsearchReader</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">llama_index.vector_stores.opensearch</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
|
||
<span class="n">OpensearchVectorClient</span><span class="p">,</span>
|
||
<span class="n">OpensearchVectorStore</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">rich.console</span><span class="w"> </span><span class="kn">import</span> <span class="n">Console</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">rich.pretty</span><span class="w"> </span><span class="kn">import</span> <span class="n">pprint</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">transformers</span><span class="w"> </span><span class="kn">import</span> <span class="n">AutoTokenizer</span>
|
||
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.chunking</span><span class="w"> </span><span class="kn">import</span> <span class="n">HybridChunker</span>
|
||
|
||
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">()</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-2">import logging
|
||
from pathlib import Path
|
||
from tempfile import mkdtemp
|
||
|
||
import requests
|
||
import torch
|
||
from docling_core.transforms.chunker import HierarchicalChunker
|
||
from docling_core.transforms.chunker.hierarchical_chunker import (
|
||
ChunkingDocSerializer,
|
||
ChunkingSerializerProvider,
|
||
)
|
||
from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer
|
||
from docling_core.transforms.serializer.markdown import MarkdownTableSerializer
|
||
from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex
|
||
from llama_index.core.data_structs import Node
|
||
from llama_index.core.response_synthesizers import get_response_synthesizer
|
||
from llama_index.core.schema import NodeWithScore, TransformComponent
|
||
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
|
||
from llama_index.core.vector_stores.types import VectorStoreQueryMode
|
||
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
|
||
from llama_index.llms.ollama import Ollama
|
||
from llama_index.node_parser.docling import DoclingNodeParser
|
||
from llama_index.readers.docling import DoclingReader
|
||
from llama_index.readers.elasticsearch import ElasticsearchReader
|
||
from llama_index.vector_stores.opensearch import (
|
||
OpensearchVectorClient,
|
||
OpensearchVectorStore,
|
||
)
|
||
from rich.console import Console
|
||
from rich.pretty import pprint
|
||
from transformers import AutoTokenizer
|
||
|
||
from docling.chunking import HybridChunker
|
||
|
||
logging.getLogger().setLevel(logging.WARNING)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>/Users/ceb/git/docling/.venv/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'validate_default' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'validate_default' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.
|
||
warnings.warn(
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="gpu-checking">GPU Checking<a class="anchor-link" href="#gpu-checking">¶</a></h3>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>Part of what makes Docling so remarkable is the fact that it can run on commodity hardware. This means that this notebook can be run on a local machine with GPU acceleration. If you're using a MacBook with a silicon chip, Docling integrates seamlessly with Metal Performance Shaders (MPS). MPS provides out-of-the-box GPU acceleration for macOS, seamlessly integrating with PyTorch and TensorFlow, offering energy-efficient performance on Apple Silicon, and broad compatibility with all Metal-supported GPUs.</p>
|
||
<p>The code below checks if a GPU is available, either via CUDA or MPS.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-3">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Check if GPU or MPS is available</span>
|
||
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
|
||
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"CUDA GPU is enabled: </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_name</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="k">elif</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
|
||
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"mps"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"MPS GPU is enabled."</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span>
|
||
<span class="s2">"No GPU or MPS device found. Please check your environment and ensure GPU or MPS support is configured."</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-3"># Check if GPU or MPS is available
|
||
if torch.cuda.is_available():
|
||
device = torch.device("cuda")
|
||
print(f"CUDA GPU is enabled: {torch.cuda.get_device_name(0)}")
|
||
elif torch.backends.mps.is_available():
|
||
device = torch.device("mps")
|
||
print("MPS GPU is enabled.")
|
||
else:
|
||
raise OSError(
|
||
"No GPU or MPS device found. Please check your environment and ensure GPU or MPS support is configured."
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>MPS GPU is enabled.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="local-opensearch-instance">Local OpenSearch instance<a class="anchor-link" href="#local-opensearch-instance">¶</a></h3><p>To run the notebook locally, we can pull an OpenSearch image and run a single node for local development.
|
||
You can use a container tool like <a href="https://podman.io/">Podman</a> or <a href="https://www.docker.com/">Docker</a>.
|
||
In the interest of simplicity, we disable the SSL option for this example.</p>
|
||
<p>💡 The version of the OpenSearch instance needs to be compatible with the version of the <a href="https://github.com/opensearch-project/opensearch-py">OpenSearch Python Client</a> library,
|
||
since this library is used by the LlamaIndex framework, which we leverage in this notebook.</p>
|
||
<p>On your computer terminal run:</p>
|
||
<div class="highlight"><pre><span></span>podman<span class="w"> </span>run<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-it<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--pull<span class="w"> </span>always<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-p<span class="w"> </span><span class="m">9200</span>:9200<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-p<span class="w"> </span><span class="m">9600</span>:9600<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-e<span class="w"> </span><span class="s2">"discovery.type=single-node"</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-e<span class="w"> </span><span class="nv">DISABLE_INSTALL_DEMO_CONFIG</span><span class="o">=</span><span class="nb">true</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-e<span class="w"> </span><span class="nv">DISABLE_SECURITY_PLUGIN</span><span class="o">=</span><span class="nb">true</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--name<span class="w"> </span>opensearch-node<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>-d<span class="w"> </span>opensearchproject/opensearch:3.0.0
|
||
</pre></div>
|
||
<p>Once the instance is running, verify that you can connect to OpenSearch:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"http://localhost:9200"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-4">response = requests.get("http://localhost:9200")
|
||
print(response.text)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>{
|
||
"name" : "b20d8368e745",
|
||
"cluster_name" : "docker-cluster",
|
||
"cluster_uuid" : "0gEZCJQwRHabS_E-n_3i9g",
|
||
"version" : {
|
||
"distribution" : "opensearch",
|
||
"number" : "3.0.0",
|
||
"build_type" : "tar",
|
||
"build_hash" : "dc4efa821904cc2d7ea7ef61c0f577d3fc0d8be9",
|
||
"build_date" : "2025-05-03T06:23:50.311109522Z",
|
||
"build_snapshot" : false,
|
||
"lucene_version" : "10.1.0",
|
||
"minimum_wire_compatibility_version" : "2.19.0",
|
||
"minimum_index_compatibility_version" : "2.0.0"
|
||
},
|
||
"tagline" : "The OpenSearch Project: https://opensearch.org/"
|
||
}
|
||
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="language-models">Language models<a class="anchor-link" href="#language-models">¶</a></h3><p>We will use <a href="https://huggingface.co/">HuggingFace</a> and <a href="https://ollama.com/">Ollama</a> to run language models on your local computer, rather than relying on cloud services.</p>
|
||
<p>In this example, the following models are considered:</p>
|
||
<ul>
|
||
<li><a href="https://huggingface.co/ibm-granite/granite-embedding-30m-english">IBM Granite Embedding 30M English</a> with HuggingFace for text embeddings</li>
|
||
<li><a href="https://ollama.com/library/granite4:tiny-h">IBM Granite 4.0 Tiny</a> with Ollama for model inference</li>
|
||
</ul>
|
||
<p>Once Ollama is installed on your computer, you can pull the model above from your terminal:</p>
|
||
<div class="highlight"><pre><span></span>ollama<span class="w"> </span>pull<span class="w"> </span>granite4:tiny-h
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="setup">Setup<a class="anchor-link" href="#setup">¶</a></h3><p>We setup the main variables for OpenSearch and the embedding and generation models.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-5">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># http endpoint for your cluster</span>
|
||
<span class="n">OPENSEARCH_ENDPOINT</span> <span class="o">=</span> <span class="s2">"http://localhost:9200"</span>
|
||
<span class="c1"># index to store the Docling document vectors</span>
|
||
<span class="n">OPENSEARCH_INDEX</span> <span class="o">=</span> <span class="s2">"docling-index"</span>
|
||
<span class="c1"># the embedding model</span>
|
||
<span class="n">EMBED_MODEL</span> <span class="o">=</span> <span class="n">HuggingFaceEmbedding</span><span class="p">(</span>
|
||
<span class="n">model_name</span><span class="o">=</span><span class="s2">"ibm-granite/granite-embedding-30m-english"</span>
|
||
<span class="p">)</span>
|
||
<span class="c1"># maximum chunk size in tokens</span>
|
||
<span class="n">EMBED_MAX_TOKENS</span> <span class="o">=</span> <span class="mi">200</span>
|
||
<span class="c1"># the generation model</span>
|
||
<span class="n">GEN_MODEL</span> <span class="o">=</span> <span class="n">Ollama</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="o">=</span><span class="s2">"granite4:tiny-h"</span><span class="p">,</span>
|
||
<span class="n">request_timeout</span><span class="o">=</span><span class="mf">120.0</span><span class="p">,</span>
|
||
<span class="c1"># Manually set the context window to limit memory usage</span>
|
||
<span class="n">context_window</span><span class="o">=</span><span class="mi">8000</span><span class="p">,</span>
|
||
<span class="c1"># Set temperature to 0 for reproducibility of the results</span>
|
||
<span class="n">temperature</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="c1"># a sample document</span>
|
||
<span class="n">SOURCE</span> <span class="o">=</span> <span class="s2">"https://arxiv.org/pdf/2408.09869"</span>
|
||
|
||
<span class="n">embed_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">EMBED_MODEL</span><span class="o">.</span><span class="n">get_text_embedding</span><span class="p">(</span><span class="s2">"hi"</span><span class="p">))</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The embedding dimension is </span><span class="si">{</span><span class="n">embed_dim</span><span class="si">}</span><span class="s2">."</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-5"># http endpoint for your cluster
|
||
OPENSEARCH_ENDPOINT = "http://localhost:9200"
|
||
# index to store the Docling document vectors
|
||
OPENSEARCH_INDEX = "docling-index"
|
||
# the embedding model
|
||
EMBED_MODEL = HuggingFaceEmbedding(
|
||
model_name="ibm-granite/granite-embedding-30m-english"
|
||
)
|
||
# maximum chunk size in tokens
|
||
EMBED_MAX_TOKENS = 200
|
||
# the generation model
|
||
GEN_MODEL = Ollama(
|
||
model="granite4:tiny-h",
|
||
request_timeout=120.0,
|
||
# Manually set the context window to limit memory usage
|
||
context_window=8000,
|
||
# Set temperature to 0 for reproducibility of the results
|
||
temperature=0.0,
|
||
)
|
||
# a sample document
|
||
SOURCE = "https://arxiv.org/pdf/2408.09869"
|
||
|
||
embed_dim = len(EMBED_MODEL.get_text_embedding("hi"))
|
||
print(f"The embedding dimension is {embed_dim}.")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>The embedding dimension is 384.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h2 id="process-data-using-docling">Process Data Using Docling<a class="anchor-link" href="#process-data-using-docling">¶</a></h2><p>Docling can parse various document formats into a unified representation (<a href="https://docling-project.github.io/docling/concepts/docling_document/">DoclingDocument</a>), which can then be exported to different output formats. For a full list of supported input and output formats, please refer to <a href="https://docling-project.github.io/docling/usage/supported_formats/">Supported formats</a> section of Docling's documentation.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>In this recipe, we will use a single PDF file, the <a href="https://arxiv.org/pdf/2408.09869">Docling Technical Report</a>. We will process it using the <a href="https://docling-project.github.io/docling/concepts/chunking/#hybrid-chunker">Hybrid Chunker</a> provided by Docling to generate structured, hierarchical chunks suitable for downstream RAG tasks.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="run-the-document-conversion-pipeline">Run the document conversion pipeline<a class="anchor-link" href="#run-the-document-conversion-pipeline">¶</a></h3><p>We will convert the original PDF file into a <code>DoclingDocument</code> format using a <code>DoclingReader</code> object. We specify the JSON export type to retain the document hierarchical structure as an input for the next step (chunking the document).</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-6">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">tmp_dir_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">mkdtemp</span><span class="p">())</span>
|
||
<span class="n">req</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">SOURCE</span><span class="p">)</span>
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">tmp_dir_path</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">Path</span><span class="p">(</span><span class="n">SOURCE</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">.pdf"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">out_file</span><span class="p">:</span>
|
||
<span class="n">out_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">req</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
|
||
|
||
<span class="n">reader</span> <span class="o">=</span> <span class="n">DoclingReader</span><span class="p">(</span><span class="n">export_type</span><span class="o">=</span><span class="n">DoclingReader</span><span class="o">.</span><span class="n">ExportType</span><span class="o">.</span><span class="n">JSON</span><span class="p">)</span>
|
||
<span class="n">dir_reader</span> <span class="o">=</span> <span class="n">SimpleDirectoryReader</span><span class="p">(</span>
|
||
<span class="n">input_dir</span><span class="o">=</span><span class="n">tmp_dir_path</span><span class="p">,</span>
|
||
<span class="n">file_extractor</span><span class="o">=</span><span class="p">{</span><span class="s2">".pdf"</span><span class="p">:</span> <span class="n">reader</span><span class="p">},</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="c1"># load the PDF files</span>
|
||
<span class="n">documents</span> <span class="o">=</span> <span class="n">dir_reader</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-6">tmp_dir_path = Path(mkdtemp())
|
||
req = requests.get(SOURCE)
|
||
with open(tmp_dir_path / f"{Path(SOURCE).name}.pdf", "wb") as out_file:
|
||
out_file.write(req.content)
|
||
|
||
reader = DoclingReader(export_type=DoclingReader.ExportType.JSON)
|
||
dir_reader = SimpleDirectoryReader(
|
||
input_dir=tmp_dir_path,
|
||
file_extractor={".pdf": reader},
|
||
)
|
||
|
||
# load the PDF files
|
||
documents = dir_reader.load_data()</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="load-data-into-opensearch">Load Data into OpenSearch<a class="anchor-link" href="#load-data-into-opensearch">¶</a></h3><h4 id="define-the-transformations">Define the Transformations<a class="anchor-link" href="#define-the-transformations">¶</a></h4><p>Before the actual ingestion of data, we need to define the data transformations to apply on the <code>DoclingDocument</code>:</p>
|
||
<ul>
|
||
<li><code>DoclingNodeParser</code> executes the document-based chunking with the hybrid chunker, which leverages the tokenizer of the embedding model to ensure that the resulting chunks fit within the model input text limit.</li>
|
||
<li><code>MetadataTransform</code> is a custom transformation to ensure that generated chunk metadata is best formatted for indexing with OpenSearch</li>
|
||
</ul>
|
||
<p>💡 For demonstration purposes, we configure the hybrid chunker to produce chunks capped at 200 tokens. The optimal limit will vary according to the specific requirements of the AI application in question.
|
||
If this value is omitted, the chunker automatically derives the maximum size from the tokenizer. This safeguard guarantees that each chunk remains within the bounds supported by the underlying embedding model.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
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||
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||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
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|
||
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|
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|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
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|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># create the hybrid chunker</span>
|
||
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">HuggingFaceTokenizer</span><span class="p">(</span>
|
||
<span class="n">tokenizer</span><span class="o">=</span><span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">EMBED_MODEL</span><span class="o">.</span><span class="n">model_name</span><span class="p">),</span>
|
||
<span class="n">max_tokens</span><span class="o">=</span><span class="n">EMBED_MAX_TOKENS</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="n">chunker</span> <span class="o">=</span> <span class="n">HybridChunker</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">)</span>
|
||
|
||
<span class="c1"># create a Docling node parser</span>
|
||
<span class="n">node_parser</span> <span class="o">=</span> <span class="n">DoclingNodeParser</span><span class="p">(</span><span class="n">chunker</span><span class="o">=</span><span class="n">chunker</span><span class="p">)</span>
|
||
|
||
|
||
<span class="c1"># create a custom transformation to avoid out-of-range integers</span>
|
||
<span class="k">class</span><span class="w"> </span><span class="nc">MetadataTransform</span><span class="p">(</span><span class="n">TransformComponent</span><span class="p">):</span>
|
||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
|
||
<span class="n">binary_hash</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"origin"</span><span class="p">,</span> <span class="p">{})</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"binary_hash"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">binary_hash</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||
<span class="n">node</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"origin"</span><span class="p">][</span><span class="s2">"binary_hash"</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">binary_hash</span><span class="p">)</span>
|
||
<span class="k">return</span> <span class="n">nodes</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-7"># create the hybrid chunker
|
||
tokenizer = HuggingFaceTokenizer(
|
||
tokenizer=AutoTokenizer.from_pretrained(EMBED_MODEL.model_name),
|
||
max_tokens=EMBED_MAX_TOKENS,
|
||
)
|
||
chunker = HybridChunker(tokenizer=tokenizer)
|
||
|
||
# create a Docling node parser
|
||
node_parser = DoclingNodeParser(chunker=chunker)
|
||
|
||
|
||
# create a custom transformation to avoid out-of-range integers
|
||
class MetadataTransform(TransformComponent):
|
||
def __call__(self, nodes, **kwargs):
|
||
for node in nodes:
|
||
binary_hash = node.metadata.get("origin", {}).get("binary_hash", None)
|
||
if binary_hash is not None:
|
||
node.metadata["origin"]["binary_hash"] = str(binary_hash)
|
||
return nodes</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="embed-and-insert-the-data">Embed and Insert the Data<a class="anchor-link" href="#embed-and-insert-the-data">¶</a></h3><p>In this step, we create an <code>OpenSearchVectorClient</code>, which encapsulates the logic for a single OpenSearch index with vector search enabled.</p>
|
||
<p>We then initialize the index using our sample data (a single PDF file), the Docling node parser, and the OpenSearch client that we just created.</p>
|
||
<p>💡 You may get a warning message like:</p>
|
||
<blockquote>
|
||
<p>Token indices sequence length is longer than the specified maximum sequence length for this model</p>
|
||
</blockquote>
|
||
<p>This is a <em>false alarm</em> and you may get more background explanation in <a href="https://docling-project.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model">Docling's FAQ</a> page.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
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|
||
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|
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|
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|
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|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
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|
||
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|
||
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|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># OpensearchVectorClient stores text in this field by default</span>
|
||
<span class="n">text_field</span> <span class="o">=</span> <span class="s2">"content"</span>
|
||
<span class="c1"># OpensearchVectorClient stores embeddings in this field by default</span>
|
||
<span class="n">embed_field</span> <span class="o">=</span> <span class="s2">"embedding"</span>
|
||
|
||
<span class="n">client</span> <span class="o">=</span> <span class="n">OpensearchVectorClient</span><span class="p">(</span>
|
||
<span class="n">endpoint</span><span class="o">=</span><span class="n">OPENSEARCH_ENDPOINT</span><span class="p">,</span>
|
||
<span class="n">index</span><span class="o">=</span><span class="n">OPENSEARCH_INDEX</span><span class="p">,</span>
|
||
<span class="n">dim</span><span class="o">=</span><span class="n">embed_dim</span><span class="p">,</span>
|
||
<span class="n">engine</span><span class="o">=</span><span class="s2">"faiss"</span><span class="p">,</span>
|
||
<span class="n">embedding_field</span><span class="o">=</span><span class="n">embed_field</span><span class="p">,</span>
|
||
<span class="n">text_field</span><span class="o">=</span><span class="n">text_field</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="n">vector_store</span> <span class="o">=</span> <span class="n">OpensearchVectorStore</span><span class="p">(</span><span class="n">client</span><span class="p">)</span>
|
||
<span class="n">storage_context</span> <span class="o">=</span> <span class="n">StorageContext</span><span class="o">.</span><span class="n">from_defaults</span><span class="p">(</span><span class="n">vector_store</span><span class="o">=</span><span class="n">vector_store</span><span class="p">)</span>
|
||
|
||
<span class="n">index</span> <span class="o">=</span> <span class="n">VectorStoreIndex</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span>
|
||
<span class="n">documents</span><span class="o">=</span><span class="n">documents</span><span class="p">,</span>
|
||
<span class="n">transformations</span><span class="o">=</span><span class="p">[</span><span class="n">node_parser</span><span class="p">,</span> <span class="n">MetadataTransform</span><span class="p">()],</span>
|
||
<span class="n">storage_context</span><span class="o">=</span><span class="n">storage_context</span><span class="p">,</span>
|
||
<span class="n">embed_model</span><span class="o">=</span><span class="n">EMBED_MODEL</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-8"># OpensearchVectorClient stores text in this field by default
|
||
text_field = "content"
|
||
# OpensearchVectorClient stores embeddings in this field by default
|
||
embed_field = "embedding"
|
||
|
||
client = OpensearchVectorClient(
|
||
endpoint=OPENSEARCH_ENDPOINT,
|
||
index=OPENSEARCH_INDEX,
|
||
dim=embed_dim,
|
||
engine="faiss",
|
||
embedding_field=embed_field,
|
||
text_field=text_field,
|
||
)
|
||
|
||
vector_store = OpensearchVectorStore(client)
|
||
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
||
|
||
index = VectorStoreIndex.from_documents(
|
||
documents=documents,
|
||
transformations=[node_parser, MetadataTransform()],
|
||
storage_context=storage_context,
|
||
embed_model=EMBED_MODEL,
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>2025-10-24 15:05:49,841 - WARNING - GET http://localhost:9200/docling-index [status:404 request:0.006s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h2 id="build-rag">Build RAG<a class="anchor-link" href="#build-rag">¶</a></h2><p>In this section, we will see how to assemble a RAG system, execute a query, and get a generated response.</p>
|
||
<p>We will also describe how to leverage Docling capabilities to improve RAG results.</p>
|
||
<h3 id="run-a-query">Run a query<a class="anchor-link" href="#run-a-query">¶</a></h3><p>With LlamaIndex's query engine, we can simply run a RAG system as follows:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
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|
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|
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|
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|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">console</span> <span class="o">=</span> <span class="n">Console</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">88</span><span class="p">)</span>
|
||
|
||
<span class="n">QUERY</span> <span class="o">=</span> <span class="s2">"Which are the main AI models in Docling?"</span>
|
||
<span class="n">query_engine</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_query_engine</span><span class="p">(</span><span class="n">llm</span><span class="o">=</span><span class="n">GEN_MODEL</span><span class="p">)</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="n">query_engine</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"👤: </span><span class="si">{</span><span class="n">QUERY</span><span class="si">}</span><span class="se">\n</span><span class="s2">🤖: </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-9">console = Console(width=88)
|
||
|
||
QUERY = "Which are the main AI models in Docling?"
|
||
query_engine = index.as_query_engine(llm=GEN_MODEL)
|
||
res = query_engine.query(QUERY)
|
||
|
||
console.print(f"👤: {QUERY}\n🤖: {res.response.strip()}")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">👤: Which are the main AI models in Docling?
|
||
🤖: The two main AI models used in Docling are:
|
||
|
||
<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span>. A layout analysis model, an accurate object-detector for page elements
|
||
<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span>. TableFormer, a state-of-the-art table structure recognition model
|
||
|
||
These models were initially released as part of the open-source Docling package to help
|
||
with document understanding tasks.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="custom-serializers">Custom serializers<a class="anchor-link" href="#custom-serializers">¶</a></h3><p>Docling can extract the table content and process it for chunking, like other text elements.</p>
|
||
<p>In the following example, the response is generated from a retrieved chunk containing a table.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-10">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">QUERY</span> <span class="o">=</span> <span class="s2">"What is the time to solution with the native backend on Intel?"</span>
|
||
<span class="n">query_engine</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_query_engine</span><span class="p">(</span><span class="n">llm</span><span class="o">=</span><span class="n">GEN_MODEL</span><span class="p">)</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="n">query_engine</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"👤: </span><span class="si">{</span><span class="n">QUERY</span><span class="si">}</span><span class="se">\n</span><span class="s2">🤖: </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-10">QUERY = "What is the time to solution with the native backend on Intel?"
|
||
query_engine = index.as_query_engine(llm=GEN_MODEL)
|
||
res = query_engine.query(QUERY)
|
||
console.print(f"👤: {QUERY}\n🤖: {res.response.strip()}")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">👤: What is the time to solution with the native backend on Intel?
|
||
🤖: The time to solution <span style="font-weight: bold">(</span>TTS<span style="font-weight: bold">)</span> for the native backend on Intel is:
|
||
- For Apple M3 Max <span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">16</span> cores<span style="font-weight: bold">)</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">375</span> seconds
|
||
- For <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">Intel</span><span style="font-weight: bold">(</span>R<span style="font-weight: bold">)</span> Xeon E5-<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2690</span>, native backend: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">244</span> seconds
|
||
|
||
So the TTS with the native backend on Intel ranges from approximately <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">244</span> to <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">375</span> seconds
|
||
depending on the specific configuration.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>The result above was generated with the table serialized in a triplet format.
|
||
Language models may perform better on complex tables if the structure is represented in a format that is widely adopted,
|
||
like <a href="https://en.wikipedia.org/wiki/Markdown">markdown</a>.</p>
|
||
<p>For this purpose, we can leverage a custom serializer that transforms tables in markdown format:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-11">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MDTableSerializerProvider</span><span class="p">(</span><span class="n">ChunkingSerializerProvider</span><span class="p">):</span>
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">get_serializer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc</span><span class="p">):</span>
|
||
<span class="k">return</span> <span class="n">ChunkingDocSerializer</span><span class="p">(</span>
|
||
<span class="n">doc</span><span class="o">=</span><span class="n">doc</span><span class="p">,</span>
|
||
<span class="c1"># configuring a different table serializer</span>
|
||
<span class="n">table_serializer</span><span class="o">=</span><span class="n">MarkdownTableSerializer</span><span class="p">(),</span>
|
||
<span class="p">)</span>
|
||
|
||
|
||
<span class="c1"># clear the database from the previous chunks</span>
|
||
<span class="n">client</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
|
||
<span class="n">vector_store</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
|
||
|
||
<span class="n">chunker</span> <span class="o">=</span> <span class="n">HybridChunker</span><span class="p">(</span>
|
||
<span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
|
||
<span class="n">max_tokens</span><span class="o">=</span><span class="n">EMBED_MAX_TOKENS</span><span class="p">,</span>
|
||
<span class="n">serializer_provider</span><span class="o">=</span><span class="n">MDTableSerializerProvider</span><span class="p">(),</span>
|
||
<span class="p">)</span>
|
||
<span class="n">node_parser</span> <span class="o">=</span> <span class="n">DoclingNodeParser</span><span class="p">(</span><span class="n">chunker</span><span class="o">=</span><span class="n">chunker</span><span class="p">)</span>
|
||
<span class="n">index</span> <span class="o">=</span> <span class="n">VectorStoreIndex</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span>
|
||
<span class="n">documents</span><span class="o">=</span><span class="n">documents</span><span class="p">,</span>
|
||
<span class="n">transformations</span><span class="o">=</span><span class="p">[</span><span class="n">node_parser</span><span class="p">,</span> <span class="n">MetadataTransform</span><span class="p">()],</span>
|
||
<span class="n">storage_context</span><span class="o">=</span><span class="n">storage_context</span><span class="p">,</span>
|
||
<span class="n">embed_model</span><span class="o">=</span><span class="n">EMBED_MODEL</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-11">class MDTableSerializerProvider(ChunkingSerializerProvider):
|
||
def get_serializer(self, doc):
|
||
return ChunkingDocSerializer(
|
||
doc=doc,
|
||
# configuring a different table serializer
|
||
table_serializer=MarkdownTableSerializer(),
|
||
)
|
||
|
||
|
||
# clear the database from the previous chunks
|
||
client.clear()
|
||
vector_store.clear()
|
||
|
||
chunker = HybridChunker(
|
||
tokenizer=tokenizer,
|
||
max_tokens=EMBED_MAX_TOKENS,
|
||
serializer_provider=MDTableSerializerProvider(),
|
||
)
|
||
node_parser = DoclingNodeParser(chunker=chunker)
|
||
index = VectorStoreIndex.from_documents(
|
||
documents=documents,
|
||
transformations=[node_parser, MetadataTransform()],
|
||
storage_context=storage_context,
|
||
embed_model=EMBED_MODEL,
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>Token indices sequence length is longer than the specified maximum sequence length for this model (538 > 512). Running this sequence through the model will result in indexing errors
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-12">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">query_engine</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_query_engine</span><span class="p">(</span><span class="n">llm</span><span class="o">=</span><span class="n">GEN_MODEL</span><span class="p">)</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="n">query_engine</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"👤: </span><span class="si">{</span><span class="n">QUERY</span><span class="si">}</span><span class="se">\n</span><span class="s2">🤖: </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-12">query_engine = index.as_query_engine(llm=GEN_MODEL)
|
||
res = query_engine.query(QUERY)
|
||
console.print(f"👤: {QUERY}\n🤖: {res.response.strip()}")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">👤: What is the time to solution with the native backend on Intel?
|
||
🤖: The table shows that for the native backend on Intel systems, the time-to-solution
|
||
<span style="font-weight: bold">(</span>TTS<span style="font-weight: bold">)</span> ranges from <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">239</span> seconds to <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">375</span> seconds. Specifically:
|
||
- With <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span> threads, the TTS is <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">239</span> seconds.
|
||
- With <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">16</span> threads, the TTS is <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">244</span> seconds.
|
||
|
||
So the time to solution with the native backend on Intel varies between approximately
|
||
<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">239</span> and <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">375</span> seconds depending on the thread budget used.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
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||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>Observe that the generated response is now more accurate. Refer to the <a href="https://docling-project.github.io/docling/examples/advanced_chunking_and_serialization/">Advanced chunking & serialization</a> example for more details on serialization strategies.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="filter-context-query">Filter-context Query<a class="anchor-link" href="#filter-context-query">¶</a></h3><p>By default, the <code>DoclingNodeParser</code> will keep the hierarchical information of items when creating the chunks.
|
||
That information will be stored as metadata in the OpenSearch index. Leveraging the document structure is a powerful
|
||
feature of Docling for improving RAG systems, both for retrieval and for answer generation.</p>
|
||
<p>For example, we can use chunk metadata with layout information to run queries in a filter context, for high retrieval accuracy.</p>
|
||
<p>Using the previous setup, we can see that the most similar chunk corresponds to a paragraph without enough grounding for the question:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
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||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-13">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">display_nodes</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
|
||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
|
||
<span class="n">doc_res</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"k"</span><span class="p">:</span> <span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"score"</span><span class="p">:</span> <span class="n">item</span><span class="o">.</span><span class="n">score</span><span class="p">,</span> <span class="s2">"text"</span><span class="p">:</span> <span class="n">item</span><span class="o">.</span><span class="n">text</span><span class="p">,</span> <span class="s2">"items"</span><span class="p">:</span> <span class="p">[]}</span>
|
||
<span class="n">doc_items</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"doc_items"</span><span class="p">]</span>
|
||
<span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">doc_items</span><span class="p">:</span>
|
||
<span class="n">doc_res</span><span class="p">[</span><span class="s2">"items"</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">"ref"</span><span class="p">:</span> <span class="n">doc</span><span class="p">[</span><span class="s2">"self_ref"</span><span class="p">],</span> <span class="s2">"label"</span><span class="p">:</span> <span class="n">doc</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]})</span>
|
||
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">doc_res</span><span class="p">)</span>
|
||
<span class="n">pprint</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">max_string</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-13">def display_nodes(nodes):
|
||
res = []
|
||
for idx, item in enumerate(nodes):
|
||
doc_res = {"k": idx + 1, "score": item.score, "text": item.text, "items": []}
|
||
doc_items = item.metadata["doc_items"]
|
||
for doc in doc_items:
|
||
doc_res["items"].append({"ref": doc["self_ref"], "label": doc["label"]})
|
||
res.append(doc_res)
|
||
pprint(res, max_string=200)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-14">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">retriever</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span><span class="n">similarity_top_k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||
|
||
<span class="n">QUERY</span> <span class="o">=</span> <span class="s2">"How does pypdfium perform?"</span>
|
||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">retriever</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">display_nodes</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-14">retriever = index.as_retriever(similarity_top_k=1)
|
||
|
||
QUERY = "How does pypdfium perform?"
|
||
nodes = retriever.retrieve(QUERY)
|
||
|
||
print(QUERY)
|
||
display_nodes(nodes)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>How does pypdfium perform?
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">[</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ </span><span style="font-weight: bold">{</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'k'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'score'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0.694972</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'text'</span>: <span style="color: #008000; text-decoration-color: #008000">'- [13] B. Pfitzmann, C. Auer, M. Dolfi, A. S. Nassar, and P. Staar. Doclaynet: a large humanannotated dataset for document-layout segmentation. pages 3743-3751, 2022.\n- [14] pypdf Maintainers. pypdf: '</span>+<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">314</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'items'</span>: <span style="font-weight: bold">[</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ │ </span><span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/texts/93'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'list_item'</span><span style="font-weight: bold">}</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ │ </span><span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/texts/94'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'list_item'</span><span style="font-weight: bold">}</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ │ </span><span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/texts/95'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'list_item'</span><span style="font-weight: bold">}</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ │ </span><span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/texts/96'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'list_item'</span><span style="font-weight: bold">}</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="font-weight: bold">]</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ </span><span style="font-weight: bold">}</span>
|
||
<span style="font-weight: bold">]</span>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>We may want to restrict the retrieval to only those chunks containing tabular data, expecting to retrieve more quantitative information for our type of question:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-15">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">filters</span> <span class="o">=</span> <span class="n">MetadataFilters</span><span class="p">(</span>
|
||
<span class="n">filters</span><span class="o">=</span><span class="p">[</span><span class="n">MetadataFilter</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="s2">"doc_items.label"</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="s2">"table"</span><span class="p">)]</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="n">table_retriever</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="n">similarity_top_k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">table_retriever</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">display_nodes</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-15">filters = MetadataFilters(
|
||
filters=[MetadataFilter(key="doc_items.label", value="table")]
|
||
)
|
||
|
||
table_retriever = index.as_retriever(filters=filters, similarity_top_k=1)
|
||
nodes = table_retriever.retrieve(QUERY)
|
||
|
||
print(QUERY)
|
||
display_nodes(nodes)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>How does pypdfium perform?
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">[</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ </span><span style="font-weight: bold">{</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'k'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'score'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0.6238112</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'text'</span>: <span style="color: #008000; text-decoration-color: #008000">'Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution (TT'</span>+<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">515</span>,
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ │ </span><span style="color: #008000; text-decoration-color: #008000">'items'</span>: <span style="font-weight: bold">[{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/tables/0'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'table'</span><span style="font-weight: bold">}</span>, <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'ref'</span>: <span style="color: #008000; text-decoration-color: #008000">'#/tables/0'</span>, <span style="color: #008000; text-decoration-color: #008000">'label'</span>: <span style="color: #008000; text-decoration-color: #008000">'table'</span><span style="font-weight: bold">}]</span>
|
||
<span style="color: #7fbf7f; text-decoration-color: #7fbf7f">│ </span><span style="font-weight: bold">}</span>
|
||
<span style="font-weight: bold">]</span>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="hybrid-search-retrieval-with-rrf">Hybrid Search Retrieval with RRF<a class="anchor-link" href="#hybrid-search-retrieval-with-rrf">¶</a></h3><p>Hybrid search combines keyword and semantic search to improve search relevance. To avoid relying on traditional score normalization techniques, the reciprocal rank fusion (RRF) feature on hybrid search can significantly improve the relevance of the retrieved chunks in our RAG system.</p>
|
||
<p>First, create a search pipeline and specify RRF as technique:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-16">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">url</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">OPENSEARCH_ENDPOINT</span><span class="si">}</span><span class="s2">/_search/pipeline/rrf-pipeline"</span>
|
||
<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"Content-Type"</span><span class="p">:</span> <span class="s2">"application/json"</span><span class="p">}</span>
|
||
<span class="n">body</span> <span class="o">=</span> <span class="p">{</span>
|
||
<span class="s2">"description"</span><span class="p">:</span> <span class="s2">"Post processor for hybrid RRF search"</span><span class="p">,</span>
|
||
<span class="s2">"phase_results_processors"</span><span class="p">:</span> <span class="p">[</span>
|
||
<span class="p">{</span><span class="s2">"score-ranker-processor"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"combination"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"technique"</span><span class="p">:</span> <span class="s2">"rrf"</span><span class="p">}}}</span>
|
||
<span class="p">],</span>
|
||
<span class="p">}</span>
|
||
|
||
<span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">put</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">body</span><span class="p">,</span> <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-16">url = f"{OPENSEARCH_ENDPOINT}/_search/pipeline/rrf-pipeline"
|
||
headers = {"Content-Type": "application/json"}
|
||
body = {
|
||
"description": "Post processor for hybrid RRF search",
|
||
"phase_results_processors": [
|
||
{"score-ranker-processor": {"combination": {"technique": "rrf"}}}
|
||
],
|
||
}
|
||
|
||
response = requests.put(url, json=body, headers=headers)
|
||
print(response.text)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>{"acknowledged":true}
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>We can then repeat the previous steps to get a <code>VectorStoreIndex</code> object, leveraging the search pipeline that we just created:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-17">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client_rrf</span> <span class="o">=</span> <span class="n">OpensearchVectorClient</span><span class="p">(</span>
|
||
<span class="n">endpoint</span><span class="o">=</span><span class="n">OPENSEARCH_ENDPOINT</span><span class="p">,</span>
|
||
<span class="n">index</span><span class="o">=</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">OPENSEARCH_INDEX</span><span class="si">}</span><span class="s2">-rrf"</span><span class="p">,</span>
|
||
<span class="n">dim</span><span class="o">=</span><span class="n">embed_dim</span><span class="p">,</span>
|
||
<span class="n">engine</span><span class="o">=</span><span class="s2">"faiss"</span><span class="p">,</span>
|
||
<span class="n">embedding_field</span><span class="o">=</span><span class="n">embed_field</span><span class="p">,</span>
|
||
<span class="n">text_field</span><span class="o">=</span><span class="n">text_field</span><span class="p">,</span>
|
||
<span class="n">search_pipeline</span><span class="o">=</span><span class="s2">"rrf-pipeline"</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="n">vector_store_rrf</span> <span class="o">=</span> <span class="n">OpensearchVectorStore</span><span class="p">(</span><span class="n">client_rrf</span><span class="p">)</span>
|
||
<span class="n">storage_context_rrf</span> <span class="o">=</span> <span class="n">StorageContext</span><span class="o">.</span><span class="n">from_defaults</span><span class="p">(</span><span class="n">vector_store</span><span class="o">=</span><span class="n">vector_store_rrf</span><span class="p">)</span>
|
||
<span class="n">index_hybrid</span> <span class="o">=</span> <span class="n">VectorStoreIndex</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span>
|
||
<span class="n">documents</span><span class="o">=</span><span class="n">documents</span><span class="p">,</span>
|
||
<span class="n">transformations</span><span class="o">=</span><span class="p">[</span><span class="n">node_parser</span><span class="p">,</span> <span class="n">MetadataTransform</span><span class="p">()],</span>
|
||
<span class="n">storage_context</span><span class="o">=</span><span class="n">storage_context_rrf</span><span class="p">,</span>
|
||
<span class="n">embed_model</span><span class="o">=</span><span class="n">EMBED_MODEL</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-17">client_rrf = OpensearchVectorClient(
|
||
endpoint=OPENSEARCH_ENDPOINT,
|
||
index=f"{OPENSEARCH_INDEX}-rrf",
|
||
dim=embed_dim,
|
||
engine="faiss",
|
||
embedding_field=embed_field,
|
||
text_field=text_field,
|
||
search_pipeline="rrf-pipeline",
|
||
)
|
||
|
||
vector_store_rrf = OpensearchVectorStore(client_rrf)
|
||
storage_context_rrf = StorageContext.from_defaults(vector_store=vector_store_rrf)
|
||
index_hybrid = VectorStoreIndex.from_documents(
|
||
documents=documents,
|
||
transformations=[node_parser, MetadataTransform()],
|
||
storage_context=storage_context_rrf,
|
||
embed_model=EMBED_MODEL,
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>2025-10-24 15:06:05,175 - WARNING - GET http://localhost:9200/docling-index-rrf [status:404 request:0.001s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>The first retriever, which entirely relies on semantic (vector) search, fails to catch the supporting chunk for the given question in the top 1 position.
|
||
Note that we highlight few expected keywords for illustration purposes.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-18">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">QUERY</span> <span class="o">=</span> <span class="s2">"Does Docling project provide a Dockerfile?"</span>
|
||
<span class="n">retriever</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span><span class="n">similarity_top_k</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">retriever</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">exp</span> <span class="o">=</span> <span class="s2">"Docling also provides a Dockerfile"</span>
|
||
<span class="n">start</span> <span class="o">=</span> <span class="s2">"[bold yellow]"</span>
|
||
<span class="n">end</span> <span class="o">=</span> <span class="s2">"[/]"</span>
|
||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span>
|
||
<span class="sa">f</span><span class="s2">"*** k=</span><span class="si">{</span><span class="n">idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> ***</span><span class="se">\n</span><span class="si">{</span><span class="n">item</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">exp</span><span class="p">,</span><span class="w"> </span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">start</span><span class="si">}{</span><span class="n">exp</span><span class="si">}{</span><span class="n">end</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-18">QUERY = "Does Docling project provide a Dockerfile?"
|
||
retriever = index.as_retriever(similarity_top_k=3)
|
||
nodes = retriever.retrieve(QUERY)
|
||
exp = "Docling also provides a Dockerfile"
|
||
start = "[bold yellow]"
|
||
end = "[/]"
|
||
for idx, item in enumerate(nodes):
|
||
console.print(
|
||
f"*** k={idx + 1} ***\n{item.text.strip().replace(exp, f'{start}{exp}{end}')}"
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span> ***
|
||
Docling is designed to allow easy extension of the model library and pipelines. In the
|
||
future, we plan to extend Docling with several more models, such as a figure-classifier
|
||
model, an equationrecognition model, a code-recognition model and more. This will help
|
||
improve the quality of conversion for specific types of content, as well as augment
|
||
extracted document metadata with additional information. Further investment into testing
|
||
and optimizing GPU acceleration as well as improving the Docling-native PDF backend are
|
||
on our roadmap, too.
|
||
We encourage everyone to propose or implement additional features and models, and will
|
||
gladly take your inputs and contributions under review . The codebase of Docling is open
|
||
for use and contribution, under the MIT license agreement and in alignment with our
|
||
contributing guidelines included in the Docling repository. If you use Docling in your
|
||
projects, please consider citing this technical report.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span> ***
|
||
In the final pipeline stage, Docling assembles all prediction results produced on each
|
||
page into a well-defined datatype that encapsulates a converted document, as defined in
|
||
the auxiliary package docling-core . The generated document object is passed through a
|
||
post-processing model which leverages several algorithms to augment features, such as
|
||
detection of the document language, correcting the reading order, matching figures with
|
||
captions and labelling metadata such as title, authors and references. The final output
|
||
can then be serialized to JSON or transformed into a Markdown representation at the
|
||
users request.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">3</span> ***
|
||
```
|
||
source = <span style="color: #008000; text-decoration-color: #008000">"https://arxiv.org/pdf/2206.01062"</span> # PDF path or URL converter =
|
||
<span style="color: #800080; text-decoration-color: #800080; font-weight: bold">DocumentConverter</span><span style="font-weight: bold">()</span> result = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">converter.convert_single</span><span style="font-weight: bold">(</span>source<span style="font-weight: bold">)</span>
|
||
<span style="color: #800080; text-decoration-color: #800080; font-weight: bold">print</span><span style="font-weight: bold">(</span><span style="color: #800080; text-decoration-color: #800080; font-weight: bold">result.render_as_markdown</span><span style="font-weight: bold">())</span> # output: <span style="color: #008000; text-decoration-color: #008000">"## DocLayNet: A Large Human -Annotated </span>
|
||
<span style="color: #008000; text-decoration-color: #008000">Dataset for Document -Layout Analysis [...]"</span>
|
||
```
|
||
Optionally, you can configure custom pipeline features and runtime options, such as
|
||
turning on or off features <span style="font-weight: bold">(</span>e.g. OCR, table structure recognition<span style="font-weight: bold">)</span>, enforcing limits on
|
||
the input document size, and defining the budget of CPU threads. Advanced usage examples
|
||
and options are documented in the README file. <span style="color: #808000; text-decoration-color: #808000; font-weight: bold">Docling also provides a Dockerfile</span> to
|
||
demonstrate how to install and run it inside a container.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>However, the retriever with the hybrid search pipeline effectively recognizes the key paragraph in the first position:</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-19">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">retriever_rrf</span> <span class="o">=</span> <span class="n">index_hybrid</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span>
|
||
<span class="n">vector_store_query_mode</span><span class="o">=</span><span class="n">VectorStoreQueryMode</span><span class="o">.</span><span class="n">HYBRID</span><span class="p">,</span> <span class="n">similarity_top_k</span><span class="o">=</span><span class="mi">3</span>
|
||
<span class="p">)</span>
|
||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">retriever_rrf</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span>
|
||
<span class="sa">f</span><span class="s2">"*** k=</span><span class="si">{</span><span class="n">idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> ***</span><span class="se">\n</span><span class="si">{</span><span class="n">item</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">exp</span><span class="p">,</span><span class="w"> </span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">start</span><span class="si">}{</span><span class="n">exp</span><span class="si">}{</span><span class="n">end</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-19">retriever_rrf = index_hybrid.as_retriever(
|
||
vector_store_query_mode=VectorStoreQueryMode.HYBRID, similarity_top_k=3
|
||
)
|
||
nodes = retriever_rrf.retrieve(QUERY)
|
||
for idx, item in enumerate(nodes):
|
||
console.print(
|
||
f"*** k={idx + 1} ***\n{item.text.strip().replace(exp, f'{start}{exp}{end}')}"
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span> ***
|
||
```
|
||
source = <span style="color: #008000; text-decoration-color: #008000">"https://arxiv.org/pdf/2206.01062"</span> # PDF path or URL converter =
|
||
<span style="color: #800080; text-decoration-color: #800080; font-weight: bold">DocumentConverter</span><span style="font-weight: bold">()</span> result = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">converter.convert_single</span><span style="font-weight: bold">(</span>source<span style="font-weight: bold">)</span>
|
||
<span style="color: #800080; text-decoration-color: #800080; font-weight: bold">print</span><span style="font-weight: bold">(</span><span style="color: #800080; text-decoration-color: #800080; font-weight: bold">result.render_as_markdown</span><span style="font-weight: bold">())</span> # output: <span style="color: #008000; text-decoration-color: #008000">"## DocLayNet: A Large Human -Annotated </span>
|
||
<span style="color: #008000; text-decoration-color: #008000">Dataset for Document -Layout Analysis [...]"</span>
|
||
```
|
||
Optionally, you can configure custom pipeline features and runtime options, such as
|
||
turning on or off features <span style="font-weight: bold">(</span>e.g. OCR, table structure recognition<span style="font-weight: bold">)</span>, enforcing limits on
|
||
the input document size, and defining the budget of CPU threads. Advanced usage examples
|
||
and options are documented in the README file. <span style="color: #808000; text-decoration-color: #808000; font-weight: bold">Docling also provides a Dockerfile</span> to
|
||
demonstrate how to install and run it inside a container.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span> ***
|
||
Docling is designed to allow easy extension of the model library and pipelines. In the
|
||
future, we plan to extend Docling with several more models, such as a figure-classifier
|
||
model, an equationrecognition model, a code-recognition model and more. This will help
|
||
improve the quality of conversion for specific types of content, as well as augment
|
||
extracted document metadata with additional information. Further investment into testing
|
||
and optimizing GPU acceleration as well as improving the Docling-native PDF backend are
|
||
on our roadmap, too.
|
||
We encourage everyone to propose or implement additional features and models, and will
|
||
gladly take your inputs and contributions under review . The codebase of Docling is open
|
||
for use and contribution, under the MIT license agreement and in alignment with our
|
||
contributing guidelines included in the Docling repository. If you use Docling in your
|
||
projects, please consider citing this technical report.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">3</span> ***
|
||
We therefore decided to provide multiple backend choices, and additionally open-source a
|
||
custombuilt PDF parser, which is based on the low-level qpdf <span style="font-weight: bold">[</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span><span style="font-weight: bold">]</span> library. It is made
|
||
available in a separate package named docling-parse and powers the default PDF backend
|
||
in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may
|
||
be a safe backup choice in certain cases, e.g. if issues are seen with particular font
|
||
encodings.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="context-expansion">Context expansion<a class="anchor-link" href="#context-expansion">¶</a></h3><p>Using small chunks can offer several benefits: it increases retrieval precision and it keeps the answer generation tightly focused, which improves accuracy, reduces hallucination, and speeds up inferece.
|
||
However, your RAG system may overlook contextual information necessary for producing a fully grounded response.</p>
|
||
<p>Docling's preservation of document structure enables you to employ various strategies for enriching the context available during answer generation within the RAG pipeline.
|
||
For example, after identifying the most relevant chunk, you might include adjacent chunks from the same section as additional groudning material before generating the final answer.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>In the following example, the generated response is wrong, since the top retrieved chunks do not contain all the information that is required to answer the question.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-20">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">QUERY</span> <span class="o">=</span> <span class="s2">"According to the tests with arXiv and IBM Redbooks, which backend should I use if I have limited resources and complex tables?"</span>
|
||
<span class="n">query_rrf</span> <span class="o">=</span> <span class="n">index_hybrid</span><span class="o">.</span><span class="n">as_query_engine</span><span class="p">(</span>
|
||
<span class="n">vector_store_query_mode</span><span class="o">=</span><span class="n">VectorStoreQueryMode</span><span class="o">.</span><span class="n">HYBRID</span><span class="p">,</span>
|
||
<span class="n">llm</span><span class="o">=</span><span class="n">GEN_MODEL</span><span class="p">,</span>
|
||
<span class="n">similarity_top_k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="n">query_rrf</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"👤: </span><span class="si">{</span><span class="n">QUERY</span><span class="si">}</span><span class="se">\n</span><span class="s2">🤖: </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-20">QUERY = "According to the tests with arXiv and IBM Redbooks, which backend should I use if I have limited resources and complex tables?"
|
||
query_rrf = index_hybrid.as_query_engine(
|
||
vector_store_query_mode=VectorStoreQueryMode.HYBRID,
|
||
llm=GEN_MODEL,
|
||
similarity_top_k=3,
|
||
)
|
||
res = query_rrf.query(QUERY)
|
||
console.print(f"👤: {QUERY}\n🤖: {res.response.strip()}")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">👤: According to the tests with arXiv and IBM Redbooks, which backend should I use if I
|
||
have limited resources and complex tables?
|
||
🤖: According to the tests in this section using both the MacBook Pro M3 Max and
|
||
bare-metal server running Ubuntu <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">20.04</span> LTS on an Intel Xeon E5-<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2690</span> CPU with a fixed
|
||
thread budget of <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span>, Docling achieved faster processing speeds when using the
|
||
custom-built PDF backend based on the low-level qpdf library <span style="font-weight: bold">(</span>docling-parse<span style="font-weight: bold">)</span> compared to
|
||
the alternative PDF backend relying on pypdfium.
|
||
|
||
Furthermore, the context mentions that Docling provides a separate package named
|
||
docling-ibm-models which includes pre-trained weights and inference code for
|
||
TableFormer, a state-of-the-art table structure recognition model. This suggests that if
|
||
you have complex tables in your documents, using this specialized table recognition
|
||
model could be beneficial.
|
||
|
||
Therefore, based on the tests with arXiv papers and IBM Redbooks, if you have limited
|
||
resources <span style="font-weight: bold">(</span>likely referring to computational power<span style="font-weight: bold">)</span> and need to process documents
|
||
containing complex tables, it would be recommended to use the docling-parse PDF backend
|
||
along with the TableFormer AI model from docling-ibm-models. This combination should
|
||
provide a good balance of performance and table recognition capabilities for your
|
||
specific needs.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-21">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">nodes</span> <span class="o">=</span> <span class="n">retriever_rrf</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span><span class="n">QUERY</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span>
|
||
<span class="sa">f</span><span class="s2">"*** k=</span><span class="si">{</span><span class="n">idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> ***</span><span class="se">\n</span><span class="si">{</span><span class="n">item</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">exp</span><span class="p">,</span><span class="w"> </span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">start</span><span class="si">}{</span><span class="n">exp</span><span class="si">}{</span><span class="n">end</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-21">nodes = retriever_rrf.retrieve(QUERY)
|
||
for idx, item in enumerate(nodes):
|
||
console.print(
|
||
f"*** k={idx + 1} ***\n{item.text.strip().replace(exp, f'{start}{exp}{end}')}"
|
||
)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span> ***
|
||
In this section, we establish some reference numbers for the processing speed of Docling
|
||
and the resource budget it requires. All tests in this section are run with default
|
||
options on our standard test set distributed with Docling, which consists of three
|
||
papers from arXiv and two IBM Redbooks, with a total of <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">225</span> pages. Measurements were
|
||
taken using both available PDF backends on two different hardware systems: one MacBook
|
||
Pro M3 Max, and one bare-metal server running Ubuntu <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">20.04</span> LTS on an Intel Xeon E5-<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2690</span>
|
||
CPU. For reproducibility, we fixed the thread budget <span style="font-weight: bold">(</span>through setting OMP NUM THREADS
|
||
environment variable <span style="font-weight: bold">)</span> once to <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span> <span style="font-weight: bold">(</span>Docling default<span style="font-weight: bold">)</span> and once to <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">16</span> <span style="font-weight: bold">(</span>equal to full core
|
||
count on the test hardware<span style="font-weight: bold">)</span>. All results are shown in Table <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span>.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span> ***
|
||
We therefore decided to provide multiple backend choices, and additionally open-source a
|
||
custombuilt PDF parser, which is based on the low-level qpdf <span style="font-weight: bold">[</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span><span style="font-weight: bold">]</span> library. It is made
|
||
available in a separate package named docling-parse and powers the default PDF backend
|
||
in Docling. As an alternative, we provide a PDF backend relying on pypdfium , which may
|
||
be a safe backup choice in certain cases, e.g. if issues are seen with particular font
|
||
encodings.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">*** <span style="color: #808000; text-decoration-color: #808000">k</span>=<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">3</span> ***
|
||
As part of Docling, we initially release two highly capable AI models to the open-source
|
||
community, which have been developed and published recently by our team. The first model
|
||
is a layout analysis model, an accurate object-detector for page elements <span style="font-weight: bold">[</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">13</span><span style="font-weight: bold">]</span>. The
|
||
second model is TableFormer <span style="font-weight: bold">[</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">12</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">9</span><span style="font-weight: bold">]</span>, a state-of-the-art table structure recognition
|
||
model. We provide the pre-trained weights <span style="font-weight: bold">(</span>hosted on huggingface<span style="font-weight: bold">)</span> and a separate package
|
||
for the inference code as docling-ibm-models . Both models are also powering the
|
||
open-access deepsearch-experience, our cloud-native service for knowledge exploration
|
||
tasks.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>Even though the top retrieved chunks are relevant for the question, the key information lays in the paragraph after the first chunk:</p>
|
||
<blockquote>
|
||
<p>If you need to run Docling in very low-resource environments, please consider configuring the pypdfium backend. While it is faster and more memory efficient than the default docling-parse backend, it will come at the expense of worse quality results, especially in table structure recovery.</p>
|
||
</blockquote>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<p>We next examine the fragments that immediately precede and follow the top‑retrieved chunk, so long as those neighbors remain within the same section, to preserve the semantic integrity of the context.
|
||
The generated answer is now accurate because it has been grounded in the necessary contextual information.</p>
|
||
<p>💡 In a production setting, it may be preferable to persist the parsed documents (i.e., <code>DoclingDocument</code> objects) as JSON in an object store or database and then fetch them when you need to traverse the document for context‑expansion scenarios. In this simplified example, however, we will query the OpenSearch index directly to obtain the required chunks.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-22">
|
||
<div>
|
||
<span class="notice" hidden="">Copied!</span>
|
||
<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
|
||
<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">top_headings</span> <span class="o">=</span> <span class="n">nodes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"headings"</span><span class="p">]</span>
|
||
<span class="n">top_text</span> <span class="o">=</span> <span class="n">nodes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span>
|
||
|
||
<span class="n">rdr</span> <span class="o">=</span> <span class="n">ElasticsearchReader</span><span class="p">(</span><span class="n">endpoint</span><span class="o">=</span><span class="n">OPENSEARCH_ENDPOINT</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">OPENSEARCH_INDEX</span><span class="p">)</span>
|
||
<span class="n">docs</span> <span class="o">=</span> <span class="n">rdr</span><span class="o">.</span><span class="n">load_data</span><span class="p">(</span>
|
||
<span class="n">field</span><span class="o">=</span><span class="n">text_field</span><span class="p">,</span>
|
||
<span class="n">query</span><span class="o">=</span><span class="p">{</span>
|
||
<span class="s2">"query"</span><span class="p">:</span> <span class="p">{</span>
|
||
<span class="s2">"terms_set"</span><span class="p">:</span> <span class="p">{</span>
|
||
<span class="s2">"metadata.headings.keyword"</span><span class="p">:</span> <span class="p">{</span>
|
||
<span class="s2">"terms"</span><span class="p">:</span> <span class="n">top_headings</span><span class="p">,</span>
|
||
<span class="s2">"minimum_should_match_script"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"source"</span><span class="p">:</span> <span class="s2">"params.num_terms"</span><span class="p">},</span>
|
||
<span class="p">}</span>
|
||
<span class="p">}</span>
|
||
<span class="p">}</span>
|
||
<span class="p">},</span>
|
||
<span class="p">)</span>
|
||
<span class="n">ext_nodes</span> <span class="o">=</span> <span class="p">[]</span>
|
||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">docs</span><span class="p">):</span>
|
||
<span class="k">if</span> <span class="n">item</span><span class="o">.</span><span class="n">text</span> <span class="o">==</span> <span class="n">top_text</span><span class="p">:</span>
|
||
<span class="n">ext_nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">NodeWithScore</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">Node</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="n">item</span><span class="o">.</span><span class="n">text</span><span class="p">),</span> <span class="n">score</span><span class="o">=</span><span class="mf">1.0</span><span class="p">))</span>
|
||
<span class="k">if</span> <span class="n">idx</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||
<span class="n">ext_nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||
<span class="n">NodeWithScore</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">Node</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="n">docs</span><span class="p">[</span><span class="n">idx</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">),</span> <span class="n">score</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
|
||
<span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">idx</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
|
||
<span class="n">ext_nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||
<span class="n">NodeWithScore</span><span class="p">(</span><span class="n">node</span><span class="o">=</span><span class="n">Node</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="n">docs</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">),</span> <span class="n">score</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
|
||
<span class="p">)</span>
|
||
<span class="k">break</span>
|
||
|
||
<span class="n">synthesizer</span> <span class="o">=</span> <span class="n">get_response_synthesizer</span><span class="p">(</span><span class="n">llm</span><span class="o">=</span><span class="n">GEN_MODEL</span><span class="p">)</span>
|
||
<span class="n">res</span> <span class="o">=</span> <span class="n">synthesizer</span><span class="o">.</span><span class="n">synthesize</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="n">QUERY</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">ext_nodes</span><span class="p">)</span>
|
||
<span class="n">console</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"👤: </span><span class="si">{</span><span class="n">QUERY</span><span class="si">}</span><span class="se">\n</span><span class="s2">🤖: </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div class="clipboard-copy-txt" id="cell-22">top_headings = nodes[0].metadata["headings"]
|
||
top_text = nodes[0].text
|
||
|
||
rdr = ElasticsearchReader(endpoint=OPENSEARCH_ENDPOINT, index=OPENSEARCH_INDEX)
|
||
docs = rdr.load_data(
|
||
field=text_field,
|
||
query={
|
||
"query": {
|
||
"terms_set": {
|
||
"metadata.headings.keyword": {
|
||
"terms": top_headings,
|
||
"minimum_should_match_script": {"source": "params.num_terms"},
|
||
}
|
||
}
|
||
}
|
||
},
|
||
)
|
||
ext_nodes = []
|
||
for idx, item in enumerate(docs):
|
||
if item.text == top_text:
|
||
ext_nodes.append(NodeWithScore(node=Node(text=item.text), score=1.0))
|
||
if idx > 0:
|
||
ext_nodes.append(
|
||
NodeWithScore(node=Node(text=docs[idx - 1].text), score=1.0)
|
||
)
|
||
if idx < len(docs) - 1:
|
||
ext_nodes.append(
|
||
NodeWithScore(node=Node(text=docs[idx + 1].text), score=1.0)
|
||
)
|
||
break
|
||
|
||
synthesizer = get_response_synthesizer(llm=GEN_MODEL)
|
||
res = synthesizer.synthesize(query=QUERY, nodes=ext_nodes)
|
||
console.print(f"👤: {QUERY}\n🤖: {res.response.strip()}")</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
|
||
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">👤: According to the tests with arXiv and IBM Redbooks, which backend should I use if I
|
||
have limited resources and complex tables?
|
||
🤖: According to the tests described in the provided context, if you need to run Docling
|
||
in a very low-resource environment and are dealing with complex tables that require
|
||
high-quality table structure recovery, you should consider configuring the pypdfium
|
||
backend. The context mentions that while it is faster and more memory efficient than the
|
||
default docling-parse backend, it may come at the expense of worse quality results,
|
||
especially in table structure recovery. Therefore, for limited resources and complex
|
||
tables where quality is crucial, pypdfium would be a suitable choice despite its
|
||
potential drawbacks compared to the default backend.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div> <!-- jp-Notebook -->
|
||
</div> <!-- jupyter-wrapper -->
|
||
|
||
<style>
|
||
['pre { line-height: 125%; }\ntd.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\nspan.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\ntd.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\nspan.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }\n.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }\n.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */\n.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */\n.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */\n.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */\n.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */\n.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */\n.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */\n.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */\n.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */\n.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */\n.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */\n.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */\n.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */\n.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */\n.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */\n.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */\n.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */\n.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */\n.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */\n.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */\n.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */\n.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */\n.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */\n.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */\n.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */\n.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */\n.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */\n.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */\n.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */\n.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */\n.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */\n.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */\n.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */\n.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */\n.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */\n.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */\n.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */\n.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */\n.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */\n.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */\n.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */']
|
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</style>
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|
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