mirror of
https://github.com/langflow-ai/langflow.git
synced 2026-07-24 05:39:16 +08:00
184 lines
45 KiB
HTML
184 lines
45 KiB
HTML
<!doctype html>
|
||
<html lang="en" dir="ltr" class="docs-wrapper plugin-docs plugin-id-default docs-version-current docs-doc-page docs-doc-id-Components/components-embedding-models" data-has-hydrated="false">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="generator" content="Docusaurus v3.8.1">
|
||
<title data-rh="true">Embedding Model | Langflow Documentation</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:url" content="https://docs.langflow.org/components-embedding-models"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" name="docusaurus_locale" content="en"><meta data-rh="true" name="docsearch:language" content="en"><meta data-rh="true" name="docusaurus_version" content="current"><meta data-rh="true" name="docusaurus_tag" content="docs-default-current"><meta data-rh="true" name="docsearch:version" content="current"><meta data-rh="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-rh="true" property="og:title" content="Embedding Model | Langflow Documentation"><meta data-rh="true" name="description" content="Embedding model components in Langflow generate text embeddings using a specified Large Language Model (LLM)."><meta data-rh="true" property="og:description" content="Embedding model components in Langflow generate text embeddings using a specified Large Language Model (LLM)."><link data-rh="true" rel="icon" href="/img/favicon.ico"><link data-rh="true" rel="canonical" href="https://docs.langflow.org/components-embedding-models"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/components-embedding-models" hreflang="en"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/components-embedding-models" hreflang="x-default"><link data-rh="true" rel="preconnect" href="https://UZK6BDPCVY-dsn.algolia.net" crossorigin="anonymous"><script data-rh="true" type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Embedding Model","item":"https://docs.langflow.org/components-embedding-models"}]}</script><link rel="preconnect" href="https://www.google-analytics.com">
|
||
<link rel="preconnect" href="https://www.googletagmanager.com">
|
||
<script async src="https://www.googletagmanager.com/gtag/js?id=G-SLQFLQ3KPT"></script>
|
||
<script>function gtag(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],gtag("js",new Date),gtag("config","G-SLQFLQ3KPT",{})</script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<link rel="search" type="application/opensearchdescription+xml" title="Langflow Documentation" href="/opensearch.xml">
|
||
|
||
|
||
|
||
<script>window._ibmAnalytics={settings:{name:"DataStax",tealiumProfileName:"ibm-subsidiary"},trustarc:{privacyPolicyLink:"https://ibm.com/privacy"},"digitalData.page.services.google.enabled":!0},window.digitalData={page:{pageInfo:{ibm:{siteId:"IBM_"+_ibmAnalytics.settings.name},segment:{enabled:!0,env:"prod",key:"B04fNhD06DqDPuaRfQl5lZ2iQICdxxuh",coremetrics:!1,carbonComponentEvents:!1}},category:{primaryCategory:"PC230"}},commonProperties:{productTitle:"IBM Elite Support for Langflow",productCode:"5900BUB",productCodeType:"WWPC",UT30:"30AS5",instanceId:"docs-site",subscriptionId:"public-access",productPlanName:"Public",productPlanType:"freemium",userId:"IBMid-ANONYMOUS"}}</script>
|
||
<script src="//1.www.s81c.com/common/stats/ibm-common.js" async="true"></script>
|
||
|
||
|
||
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Sora:wght@550;600&display=swap">
|
||
<script>function gtag(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],gtag("consent","default",{ad_storage:"denied",ad_user_data:"denied",ad_personalization:"denied",analytics_storage:"denied"})</script>
|
||
<script>!function(){function e(){if(void 0!==window.truste&&window.truste.cma){var e=window.truste.cma.callApi("getConsent",window.location.href)||{},n=1===e[2],t=1===e[3];gtag("consent","update",{ad_storage:n?"granted":"denied",ad_user_data:n?"granted":"denied",ad_personalization:n?"granted":"denied",analytics_storage:t?"granted":"denied"})}}window.addEventListener&&(window.addEventListener("cm_data_subject_consent_changed",e),window.addEventListener("cm_consent_preferences_set",e)),"complete"===document.readyState?e():window.addEventListener("load",e)}()</script><link rel="stylesheet" href="/assets/css/styles.5b49702d.css">
|
||
<script src="/assets/js/runtime~main.fa491f43.js" defer="defer"></script>
|
||
<script src="/assets/js/main.adedf334.js" defer="defer"></script>
|
||
</head>
|
||
<body class="navigation-with-keyboard">
|
||
<svg xmlns="http://www.w3.org/2000/svg" style="display: none;"><defs>
|
||
<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
|
||
</defs></svg>
|
||
<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||(window.matchMedia("(prefers-color-scheme: dark)").matches?"dark":"light")),document.documentElement.setAttribute("data-theme-choice",t||"system")}(),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__docusaurus_skipToContent_fallback">Skip to main content</a></div><nav aria-label="Main" class="theme-layout-navbar navbar navbar--fixed-top navbarHideable_m1mJ"><div class="navbar__inner"><div class="theme-layout-navbar-left navbar__items"><button aria-label="Toggle navigation bar" aria-expanded="false" class="navbar__toggle clean-btn" type="button"><svg width="30" height="30" viewBox="0 0 30 30" aria-hidden="true"><path stroke="currentColor" stroke-linecap="round" stroke-miterlimit="10" stroke-width="2" d="M4 7h22M4 15h22M4 23h22"></path></svg></button><a class="navbar__brand" href="/"><div class="navbar__logo"><img src="/img/lf-docs-light.svg" alt="Langflow" class="themedComponent_mlkZ themedComponent--light_NVdE"><img src="/img/lf-docs-dark.svg" alt="Langflow" class="themedComponent_mlkZ themedComponent--dark_xIcU"></div></a></div><div class="theme-layout-navbar-right navbar__items navbar__items--right"><a href="https://github.com/langflow-ai/langflow" target="_blank" class="navbar__item navbar__link header-github-link" data-event="UI Interaction" data-action="clicked" data-channel="docs" data-element-id="social-github" data-namespace="header" data-platform-title="Langflow"></a><a href="https://twitter.com/langflow_ai" target="_blank" class="navbar__item navbar__link header-twitter-link" data-event="UI Interaction" data-action="clicked" data-channel="docs" data-element-id="social-twitter" data-namespace="header" data-platform-title="Langflow"></a><a href="https://discord.gg/EqksyE2EX9" target="_blank" class="navbar__item navbar__link header-discord-link" data-event="UI Interaction" data-action="clicked" data-channel="docs" data-element-id="social-discord" data-namespace="header" data-platform-title="Langflow"></a><div class="toggle_vylO colorModeToggle_DEke"><button class="clean-btn toggleButton_gllP toggleButtonDisabled_aARS" type="button" disabled="" title="system mode" aria-label="Switch between dark and light mode (currently system mode)"><svg viewBox="0 0 24 24" width="24" height="24" aria-hidden="true" class="toggleIcon_g3eP lightToggleIcon_pyhR"><path fill="currentColor" d="M12,9c1.65,0,3,1.35,3,3s-1.35,3-3,3s-3-1.35-3-3S10.35,9,12,9 M12,7c-2.76,0-5,2.24-5,5s2.24,5,5,5s5-2.24,5-5 S14.76,7,12,7L12,7z M2,13l2,0c0.55,0,1-0.45,1-1s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S1.45,13,2,13z M20,13l2,0c0.55,0,1-0.45,1-1 s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S19.45,13,20,13z M11,2v2c0,0.55,0.45,1,1,1s1-0.45,1-1V2c0-0.55-0.45-1-1-1S11,1.45,11,2z M11,20v2c0,0.55,0.45,1,1,1s1-0.45,1-1v-2c0-0.55-0.45-1-1-1C11.45,19,11,19.45,11,20z M5.99,4.58c-0.39-0.39-1.03-0.39-1.41,0 c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0s0.39-1.03,0-1.41L5.99,4.58z M18.36,16.95 c-0.39-0.39-1.03-0.39-1.41,0c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0c0.39-0.39,0.39-1.03,0-1.41 L18.36,16.95z M19.42,5.99c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06c-0.39,0.39-0.39,1.03,0,1.41 s1.03,0.39,1.41,0L19.42,5.99z M7.05,18.36c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06 c-0.39,0.39-0.39,1.03,0,1.41s1.03,0.39,1.41,0L7.05,18.36z"></path></svg><svg viewBox="0 0 24 24" width="24" height="24" aria-hidden="true" class="toggleIcon_g3eP darkToggleIcon_wfgR"><path fill="currentColor" d="M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27C17.45,17.19,14.93,19,12,19 c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z M12,3c-4.97,0-9,4.03-9,9s4.03,9,9,9s9-4.03,9-9c0-0.46-0.04-0.92-0.1-1.36 c-0.98,1.37-2.58,2.26-4.4,2.26c-2.98,0-5.4-2.42-5.4-5.4c0-1.81,0.89-3.42,2.26-4.4C12.92,3.04,12.46,3,12,3L12,3z"></path></svg><svg viewBox="0 0 24 24" width="24" height="24" aria-hidden="true" class="toggleIcon_g3eP systemToggleIcon_QzmC"><path fill="currentColor" d="m12 21c4.971 0 9-4.029 9-9s-4.029-9-9-9-9 4.029-9 9 4.029 9 9 9zm4.95-13.95c1.313 1.313 2.05 3.093 2.05 4.95s-0.738 3.637-2.05 4.95c-1.313 1.313-3.093 2.05-4.95 2.05v-14c1.857 0 3.637 0.737 4.95 2.05z"></path></svg></button></div><div class="navbarSearchContainer_Bca1"><button type="button" class="DocSearch DocSearch-Button" aria-label="Search (Command+K)"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20" aria-hidden="true"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="theme-layout-main main-wrapper mainWrapper_z2l0"><div class="docsWrapper_hBAB"><button aria-label="Scroll back to top" class="clean-btn theme-back-to-top-button backToTopButton_sjWU" type="button"></button><div class="docRoot_UBD9"><aside class="theme-doc-sidebar-container docSidebarContainer_YfHR"><div class="sidebarViewport_aRkj"><div class="sidebar_njMd sidebarWithHideableNavbar_wUlq"><a tabindex="-1" class="sidebarLogo_isFc" href="/"><img src="/img/lf-docs-light.svg" alt="Langflow" class="themedComponent_mlkZ themedComponent--light_NVdE"><img src="/img/lf-docs-dark.svg" alt="Langflow" class="themedComponent_mlkZ themedComponent--dark_xIcU"></a><nav aria-label="Docs sidebar" class="menu thin-scrollbar menu_SIkG"><ul class="theme-doc-sidebar-menu menu__list"><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/">Get started</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/concepts-overview">Flows</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/agents">Agents</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/mcp-client">Model Context Protocol (MCP)</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/api-keys-and-authentication">Develop</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/deployment-overview">Deploy</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret menu__link--active" role="button" aria-expanded="true" href="/concepts-components">Components reference</a></div><ul class="menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/concepts-components">Components overview</a></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-2 menu__list-item"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret menu__link--active" role="button" aria-expanded="true" tabindex="0" href="/chat-input-and-output">Core components</a></div><ul class="menu__list"><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/chat-input-and-output">Input / Output</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/data-operations">Processing</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/api-request">Data Source</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/directory">Files</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/if-else">Flow Controls</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/batch-run">LLM Operations</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret menu__link--active" role="button" aria-expanded="true" tabindex="0" href="/components-models">Models and Agents</a></div><ul class="menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link" tabindex="0" href="/components-models">Language Model</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link" tabindex="0" href="/components-prompts">Prompt Template</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link" tabindex="0" href="/components-agents">Agents</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link" tabindex="0" href="/mcp-tools">MCP Tools</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link menu__link--active" aria-current="page" tabindex="0" href="/components-embedding-models">Embedding Model</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-4 menu__list-item"><a class="menu__link" tabindex="0" href="/message-history">Message History</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-3 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/calculator">Utilities</a></div></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-3 menu__list-item"><a class="menu__link" tabindex="0" href="/legacy-core-components">Legacy core components</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-2 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" tabindex="0" href="/components-bundle-components">Bundles</a></div></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/components-custom-components">Create custom Python components</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/api-reference-api-examples">API reference</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/contributing-community">Contribute</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/troubleshoot">Support</a></div></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-1 sidebar-ad">
|
||
<a href="https://www.langflow.org/desktop" target="_blank" class="menu__link">
|
||
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||
<g clip-path="url(#clip0_1645_37)">
|
||
<path d="M12 17H20C21.1046 17 22 16.1046 22 15V13M12 17H4C2.89543 17 2 16.1046 2 15V5C2 3.89543 2.89543 3 4 3H10M12 17V21M8 21H12M12 21H16M11.75 10.2917H13.2083L16.125 7.375H17.5833L20.5 4.45833H21.9583M16.125 11.75H17.5833L20.5 8.83333H21.9583M11.75 5.91667H13.2083L16.125 3H17.5833" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
|
||
</g>
|
||
<defs>
|
||
<clipPath id="clip0_1645_37">
|
||
<rect width="24" height="24" fill="white"/>
|
||
</clipPath>
|
||
</defs>
|
||
</svg>
|
||
<div class="sidebar-ad-text-container">
|
||
<span class="sidebar-ad-text">Get started in minutes</span>
|
||
<span class="sidebar-ad-text sidebar-ad-text-gradient">Download Langflow Desktop</span>
|
||
</div>
|
||
</a>
|
||
</li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Components reference</span></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Core components</span></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Models and Agents</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Embedding Model</span></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Embedding Model</h1></header><p>Embedding model components in Langflow generate text embeddings using a specified Large Language Model (LLM).</p>
|
||
<p>Langflow includes an <strong>Embedding Model</strong> core component that has built-in support for some LLMs.
|
||
Alternatively, you can use any <a href="#additional-embedding-models">additional embedding model</a> in place of the <strong>Embedding Model</strong> core component.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="use-embedding-model-components-in-a-flow">Use embedding model components in a flow<a href="#use-embedding-model-components-in-a-flow" class="hash-link" aria-label="Direct link to Use embedding model components in a flow" title="Direct link to Use embedding model components in a flow"></a></h2>
|
||
<p>Use embedding model components anywhere you need to generate embeddings in a flow.</p>
|
||
<p>This example shows how to use an embedding model component in a flow to create a semantic search system.
|
||
This flow loads a text file, splits the text into chunks, generates embeddings for each chunk, and then loads the chunks and embeddings into a vector store. The input and output components allow a user to query the vector store through a chat interface.</p>
|
||
<p><img decoding="async" loading="lazy" alt="A semantic search flow that uses Embedding Model, File, Split Text, Chroma DB, Chat Input, and Chat Output components" src="/assets/images/component-embedding-models-add-chat-fec505c7d61c7bddc37eeb1d4cb9d489.png" width="4000" height="2514" class="img_ev3q"></p>
|
||
<ol>
|
||
<li>
|
||
<p>Create a flow, add a <strong>Read File</strong> component, and then select a file containing text data, such as a PDF, that you can use to test the flow.</p>
|
||
</li>
|
||
<li>
|
||
<p>Add the <strong>Embedding Model</strong> core component, and then provide a valid OpenAI API key.
|
||
You can enter the API key directly or use a <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-globe" aria-hidden="true"><circle cx="12" cy="12" r="10"></circle><path d="M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20"></path><path d="M2 12h20"></path></svg> <a href="/configuration-global-variables">global variable</a>.</p>
|
||
<div class="theme-admonition theme-admonition-tip admonition_xJq3 alert alert--success"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 12 16"><path fill-rule="evenodd" d="M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"></path></svg></span>My preferred provider or model isn't listed</div><div class="admonitionContent_BuS1"><p>If your preferred embedding model provider or model isn't supported by the <strong>Embedding Model</strong> core component, you can use any <a href="#additional-embedding-models">additional embedding models</a> in place of the core component.</p><p>Browse <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-blocks" aria-hidden="true"><rect width="7" height="7" x="14" y="3" rx="1"></rect><path d="M10 21V8a1 1 0 0 0-1-1H4a1 1 0 0 0-1 1v12a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-5a1 1 0 0 0-1-1H3"></path></svg> <a href="/components-bundle-components"><strong>Bundles</strong></a> or <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-search" aria-hidden="true"><circle cx="11" cy="11" r="8"></circle><path d="m21 21-4.3-4.3"></path></svg> <strong>Search</strong> for your preferred provider to find additional embedding models, such as the <a href="/bundles-huggingface#hugging-face-embeddings-inference"><strong>Hugging Face Embeddings Inference</strong> component</a>.</p></div></div>
|
||
</li>
|
||
<li>
|
||
<p>Add a <a href="/split-text"><strong>Split Text</strong> component</a> to your flow.
|
||
This component splits text input into smaller chunks to be processed into embeddings.</p>
|
||
</li>
|
||
<li>
|
||
<p>Add a vector store component, such as the <strong>Chroma DB</strong> component, to your flow, and then configure the component to connect to your vector database.
|
||
This component stores the generated embeddings so they can be used for similarity search.</p>
|
||
</li>
|
||
<li>
|
||
<p>Connect the components:</p>
|
||
<ul>
|
||
<li>Connect the <strong>Read File</strong> component's <strong>Loaded Files</strong> output to the <strong>Split Text</strong> component's <strong>Data or DataFrame</strong> input.</li>
|
||
<li>Connect the <strong>Split Text</strong> component's <strong>Chunks</strong> output to the vector store component's <strong>Ingest Data</strong> input.</li>
|
||
<li>Connect the <strong>Embedding Model</strong> component's <strong>Embeddings</strong> output to the vector store component's <strong>Embedding</strong> input.</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p>To query the vector store, add <a href="/chat-input-and-output"><strong>Chat Input and Output</strong> components</a>:</p>
|
||
<ul>
|
||
<li>Connect the <strong>Chat Input</strong> component to the vector store component's <strong>Search Query</strong> input.</li>
|
||
<li>Connect the vector store component's <strong>Search Results</strong> output to the <strong>Chat Output</strong> component.</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p>Click <strong>Playground</strong>, and then enter a search query to retrieve text chunks that are most semantically similar to your query.</p>
|
||
</li>
|
||
</ol>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="embedding-model-parameters">Embedding Model parameters<a href="#embedding-model-parameters" class="hash-link" aria-label="Direct link to Embedding Model parameters" title="Direct link to Embedding Model parameters"></a></h2>
|
||
<p>The following parameters are for the <strong>Embedding Model</strong> core component.
|
||
Other embedding model components can have additional or different parameters.</p>
|
||
<!-- -->
|
||
<p>Some parameters are hidden by default in the visual editor.
|
||
You can modify all parameters through the <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-sliders-horizontal" aria-hidden="true"><line x1="21" x2="14" y1="4" y2="4"></line><line x1="10" x2="3" y1="4" y2="4"></line><line x1="21" x2="12" y1="12" y2="12"></line><line x1="8" x2="3" y1="12" y2="12"></line><line x1="21" x2="16" y1="20" y2="20"></line><line x1="12" x2="3" y1="20" y2="20"></line><line x1="14" x2="14" y1="2" y2="6"></line><line x1="8" x2="8" y1="10" y2="14"></line><line x1="16" x2="16" y1="18" y2="22"></line></svg> <strong>Controls</strong> in the <a href="/concepts-components#component-menus">component's header menu</a>.</p>
|
||
<table><thead><tr><th>Name</th><th>Display Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>provider</td><td>Model Provider</td><td>List</td><td>Input parameter. Select the embedding model provider.</td></tr><tr><td>model</td><td>Model Name</td><td>List</td><td>Input parameter. Select the embedding model to use.</td></tr><tr><td>api_key</td><td>OpenAI API Key</td><td>Secret[String]</td><td>Input parameter. The API key required for authenticating with the provider.</td></tr><tr><td>api_base</td><td>API Base URL</td><td>String</td><td>Input parameter. Base URL for the API. Leave empty for default.</td></tr><tr><td>dimensions</td><td>Dimensions</td><td>Integer</td><td>Input parameter. The number of dimensions for the output embeddings.</td></tr><tr><td>chunk_size</td><td>Chunk Size</td><td>Integer</td><td>Input parameter. The size of text chunks to process. Default: <code>1000</code>.</td></tr><tr><td>request_timeout</td><td>Request Timeout</td><td>Float</td><td>Input parameter. Timeout for API requests.</td></tr><tr><td>max_retries</td><td>Max Retries</td><td>Integer</td><td>Input parameter. Maximum number of retry attempts. Default: <code>3</code>.</td></tr><tr><td>show_progress_bar</td><td>Show Progress Bar</td><td>Boolean</td><td>Input parameter. Whether to display a progress bar during embedding generation.</td></tr><tr><td>model_kwargs</td><td>Model Kwargs</td><td>Dictionary</td><td>Input parameter. Additional keyword arguments to pass to the model.</td></tr><tr><td>embeddings</td><td>Embeddings</td><td>Embeddings</td><td>Output parameter. An instance for generating embeddings using the selected provider.</td></tr></tbody></table>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="additional-embedding-models">Additional embedding models<a href="#additional-embedding-models" class="hash-link" aria-label="Direct link to Additional embedding models" title="Direct link to Additional embedding models"></a></h2>
|
||
<p>If your provider or model isn't supported by the <strong>Embedding Model</strong> core component, you can replace this component with any other component that generates embeddings.</p>
|
||
<p>To find additional embedding model components, browse <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-blocks" aria-hidden="true"><rect width="7" height="7" x="14" y="3" rx="1"></rect><path d="M10 21V8a1 1 0 0 0-1-1H4a1 1 0 0 0-1 1v12a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-5a1 1 0 0 0-1-1H3"></path></svg> <a href="/components-bundle-components"><strong>Bundles</strong></a> or <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-search" aria-hidden="true"><circle cx="11" cy="11" r="8"></circle><path d="m21 21-4.3-4.3"></path></svg> <strong>Search</strong> for your preferred provider.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="pair-models-with-vector-stores">Pair models with vector stores<a href="#pair-models-with-vector-stores" class="hash-link" aria-label="Direct link to Pair models with vector stores" title="Direct link to Pair models with vector stores"></a></h2>
|
||
<!-- -->
|
||
<p>By design, vector data is essential for LLM applications, such as chatbots and agents.</p>
|
||
<p>While you can use an LLM alone for generic chat interactions and common tasks, you can take your application to the next level with context sensitivity (such as RAG) and custom datasets (such as internal business data).
|
||
This often requires integrating vector databases and vector searches that provide the additional context and define meaningful queries.</p>
|
||
<p>Langflow includes vector store components that can read and write vector data, including embedding storage, similarity search, Graph RAG traversals, and dedicated search instances like OpenSearch.
|
||
Because of their interdependent functionality, it is common to use vector store, language model, and embedding model components in the same flow or in a series of dependent flows.</p>
|
||
<p>To find available vector store components, browse <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-blocks" aria-hidden="true"><rect width="7" height="7" x="14" y="3" rx="1"></rect><path d="M10 21V8a1 1 0 0 0-1-1H4a1 1 0 0 0-1 1v12a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-5a1 1 0 0 0-1-1H3"></path></svg> <a href="/components-bundle-components"><strong>Bundles</strong></a> or <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-search" aria-hidden="true"><circle cx="11" cy="11" r="8"></circle><path d="m21 21-4.3-4.3"></path></svg> <strong>Search</strong> for your preferred vector database provider.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Example: Vector search flow</summary><div><div class="collapsibleContent_i85q"><div class="theme-admonition theme-admonition-tip admonition_xJq3 alert alert--success"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 12 16"><path fill-rule="evenodd" d="M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"></path></svg></span>tip</div><div class="admonitionContent_BuS1"><p>For a tutorial that uses vector data in a flow, see <a href="/chat-with-rag">Create a vector RAG chatbot</a>.</p></div></div>
|
||
<p>The following example demonstrates how to use vector store components in flows alongside related components like embedding model and language model components.
|
||
These steps walk through important configuration details, functionality, and best practices for using these components effectively.
|
||
This is only one example; it isn't a prescriptive guide to all possible use cases or configurations.</p>
|
||
<ol>
|
||
<li>
|
||
<p>Create a flow with the <strong>Vector Store RAG</strong> template.</p>
|
||
<p>This template has two subflows.
|
||
The <strong>Load Data</strong> subflow loads embeddings and content into a vector database, and the <strong>Retriever</strong> subflow runs a vector search to retrieve relevant context based on a user's query.</p>
|
||
</li>
|
||
<li>
|
||
<p>Configure the database connection for both <a href="/bundles-datastax#astra-db"><strong>Astra DB</strong> components</a>, or replace them with another pair of vector store components of your choice.
|
||
Make sure the components connect to the same vector store, and that the component in the <strong>Retriever</strong> subflow is able to run a similarity search.</p>
|
||
<p>The parameters you set in each vector store component depend on the component's role in your flow.
|
||
In this example, the <strong>Load Data</strong> subflow <em>writes</em> to the vector store, whereas the <strong>Retriever</strong> subflow <em>reads</em> from the vector store.
|
||
Therefore, search-related parameters are only relevant to the <strong>Vector Search</strong> component in the <strong>Retriever</strong> subflow.</p>
|
||
<p>For information about specific parameters, see the documentation for your chosen vector store component.</p>
|
||
</li>
|
||
<li>
|
||
<p>To configure the embedding model, do one of the following:</p>
|
||
<ul>
|
||
<li>
|
||
<p><strong>Use an OpenAI model</strong>: In both <strong>OpenAI Embeddings</strong> components, enter your OpenAI API key.
|
||
You can use the default model or select a different OpenAI embedding model.</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Use another provider</strong>: Replace the <strong>OpenAI Embeddings</strong> components with another pair of <a href="/components-embedding-models">embedding model components</a> of your choice, and then configure the parameters and credentials accordingly.</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Use Astra DB vectorize</strong>: If you are using an Astra DB vector store that has a vectorize integration, you can remove both <strong>OpenAI Embeddings</strong> components.
|
||
If you do this, the vectorize integration automatically generates embeddings from the <strong>Ingest Data</strong> (in the <strong>Load Data</strong> subflow) and <strong>Search Query</strong> (in the <strong>Retriever</strong> subflow).</p>
|
||
</li>
|
||
</ul>
|
||
<div class="theme-admonition theme-admonition-tip admonition_xJq3 alert alert--success"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 12 16"><path fill-rule="evenodd" d="M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"></path></svg></span>tip</div><div class="admonitionContent_BuS1"><p>If your vector store already contains embeddings, make sure your embedding model components use the same model as your previous embeddings.
|
||
Mixing embedding models in the same vector store can produce inaccurate search results.</p></div></div>
|
||
</li>
|
||
<li>
|
||
<p>Recommended: In the <a href="/split-text"><strong>Split Text</strong> component</a>, optimize the chunking settings for your embedding model.
|
||
For example, if your embedding model has a token limit of 512, then the <strong>Chunk Size</strong> parameter must not exceed that limit.</p>
|
||
<p>Additionally, because the <strong>Retriever</strong> subflow passes the chat input directly to the vector store component for vector search, make sure that your chat input string doesn't exceed your embedding model's limits.
|
||
For this example, you can enter a query that is within the limits; however, in a production environment, you might need to implement additional checks or preprocessing steps to ensure compliance.
|
||
For example, use additional components to prepare the chat input before running the vector search, or enforce chat input limits in your application code.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Language Model</strong> component, enter your OpenAI API key, or select a different provider and model to use for the chat portion of the flow.</p>
|
||
</li>
|
||
<li>
|
||
<p>Run the <strong>Load Data</strong> subflow to populate your vector store.
|
||
In the <strong>Read File</strong> component, select one or more files, and then click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-play" aria-hidden="true"><polygon points="6 3 20 12 6 21 6 3"></polygon></svg> <strong>Run component</strong> on the vector store component in the <strong>Load Data</strong> subflow.</p>
|
||
<p>The <strong>Load Data</strong> subflow loads files from your local machine, chunks them, generates embeddings for the chunks, and then stores the chunks and their embeddings in the vector database.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Embedding data into a vector store" src="/assets/images/vector-store-document-ingestion-6157311fb4d16e7f944d55254f0cc0e2.png" width="4000" height="2512" class="img_ev3q"></p>
|
||
<p>The <strong>Load Data</strong> subflow is separate from the <strong>Retriever</strong> subflow because you probably won't run it every time you use the chat.
|
||
You can run the <strong>Load Data</strong> subflow as needed to preload or update the data in your vector store.
|
||
Then, your chat interactions only use the components that are necessary for chat.</p>
|
||
<p>If your vector store already contains data that you want to use for vector search, then you don't need to run the <strong>Load Data</strong> subflow.</p>
|
||
</li>
|
||
<li>
|
||
<p>Open the <strong>Playground</strong> and start chatting to run the <strong>Retriever</strong> subflow.</p>
|
||
<p>The <strong>Retriever</strong> subflow generates an embedding from chat input, runs a vector search to retrieve similar content from your vector store, parses the search results into supplemental context for the LLM, and then uses the LLM to generate a natural language response to your query.
|
||
The LLM uses the vector search results along with its internal training data and tools, such as basic web search and datetime information, to produce the response.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Retrieval from a vector store" src="/assets/images/vector-store-retrieval-af7257d77ff0259ab1a0980641d464ce.png" width="4000" height="1324" class="img_ev3q"></p>
|
||
<p>To avoid passing the entire block of raw search results to the LLM, the <strong>Parser</strong> component extracts <code>text</code> strings from the search results <code>Data</code> object, and then passes them to the <strong>Prompt Template</strong> component in <code>Message</code> format.
|
||
From there, the strings and other template content are compiled into natural language instructions for the LLM.</p>
|
||
<p>You can use other components for this transformation, such as the <strong>Data Operations</strong> component, depending on how you want to use the search results.</p>
|
||
<p>To view the raw search results, click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-text-search" aria-hidden="true"><path d="M21 6H3"></path><path d="M10 12H3"></path><path d="M10 18H3"></path><circle cx="17" cy="15" r="3"></circle><path d="m21 19-1.9-1.9"></path></svg> <strong>Inspect output</strong> on the vector store component after running the <strong>Retriever</strong> subflow.</p>
|
||
</li>
|
||
</ol></div></div></details></div></article><nav class="docusaurus-mt-lg pagination-nav" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/mcp-tools"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">MCP Tools</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/message-history"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Message History</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#use-embedding-model-components-in-a-flow" class="table-of-contents__link toc-highlight">Use embedding model components in a flow</a></li><li><a href="#embedding-model-parameters" class="table-of-contents__link toc-highlight">Embedding Model parameters</a></li><li><a href="#additional-embedding-models" class="table-of-contents__link toc-highlight">Additional embedding models</a></li><li><a href="#pair-models-with-vector-stores" class="table-of-contents__link toc-highlight">Pair models with vector stores</a></li></ul></div></div></div></div></main></div></div></div><footer class="theme-layout-footer footer"><div class="container container-fluid"><div class="row footer__links"><div class="theme-layout-footer-column col footer__col"><div class="footer__title"></div><ul class="footer__items clean-list"><li class="footer__item"><div class="footer-links">
|
||
<span>© 2025 Langflow</span>
|
||
<span id="preferenceCenterContainer"> · <a href="#" onclick='return"undefined"!=typeof window&&window.truste&&window.truste.eu&&window.truste.eu.clickListener&&window.truste.eu.clickListener(),!1' style="cursor: pointer;">Manage Privacy Choices</a></span>
|
||
</div></li></ul></div></div></div></footer><div style="position:fixed;right:21px;bottom:21px;z-index:100;display:flex;align-items:center;gap:10px;cursor:pointer"><div style="background-color:#f6f6f6;border-radius:50%;width:48px;height:48px;display:flex;align-items:center;justify-content:center;box-shadow:0 2px 4px rgba(0,0,0,0.1)"><img src="/img/langflow-icon-black-transparent.svg" style="width:40px" alt="Search"></div></div></div>
|
||
</body>
|
||
</html> |