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<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">
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</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</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><strong>Embedding Model</strong> components in Langflow generate text embeddings using a specified Large Language Model (LLM).</p>
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<p>Langflow includes an <strong>Embedding Model</strong> core component that has built-in support for some LLMs.
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Alternatively, you can use <a href="#additional-embedding-model-components">additional embedding models</a> in place of the core <strong>Embedding Model</strong> component.</p>
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<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>
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<p>Use <strong>Embedding Model</strong> components anywhere you need to generate embeddings in a flow.</p>
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<p>This example shows how to use an <strong>Embedding Model</strong> component in a flow to create a semantic search system.
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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 <strong>Input and Output</strong> components allow a user to query the vector store through a chat interface.</p>
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<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>
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<ol>
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<li>
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<p>Create a flow, add a <strong>File</strong> component, and then select a file containing text data, such as a PDF, that you can use to test the flow.</p>
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</li>
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<li>
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<p>Add an <strong>Embedding Model</strong> component, and then provide a valid OpenAI API key.
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You can enter component API keys directly or use Langflow global variables to reference your API keys.</p>
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<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 preferred embedding model provider or model isn't supported by the <strong>Embedding Model</strong> core component, you can use <a href="#additional-embedding-model-components">additional embedding models</a> in place of the core component.</p><p>Search the <strong>Components</strong> menu 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>
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</li>
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<li>
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<p>Add a <a href="/components-processing#split-text"><strong>Split Text</strong> component</a> to your flow.
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This component splits text input into smaller chunks to be processed into embeddings.</p>
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</li>
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<li>
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<p>Add a <a href="/components-vector-stores"><strong>Vector Store</strong> component</a>, such as the <strong>Chroma DB</strong> component, to your flow, and then configure the component to connect to your vector store database.
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This component stores the generated embeddings so they can be used for similarity search.</p>
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</li>
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<li>
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<p>Connect the components:</p>
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<ul>
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<li>Connect the <strong>File</strong> component's <strong>Loaded Files</strong> output to the <strong>Split Text</strong> component's <strong>Data or DataFrame</strong> input.</li>
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<li>Connect the <strong>Split Text</strong> component's <strong>Chunks</strong> output to the <strong>Vector Store</strong> component's <strong>Ingest Data</strong> input.</li>
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<li>Connect the <strong>Embedding Model</strong> component's <strong>Embeddings</strong> output to the <strong>Vector Store</strong> component's <strong>Embedding</strong> input.</li>
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</ul>
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</li>
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<li>
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<p>To query the vector store, add <a href="/components-io#chat-io"><strong>Chat Input and Output</strong> components</a>:</p>
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<ul>
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<li>Connect the <strong>Chat Input</strong> component to the <strong>Vector Store</strong> component's <strong>Search Query</strong> input.</li>
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<li>Connect the <strong>Vector Store</strong> component's <strong>Search Results</strong> output to the <strong>Chat Output</strong> component.</li>
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</ul>
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</li>
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<li>
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<p>Click <strong>Playground</strong>, and then enter a search query to retrieve text chunks that are most semantically similar to your query.</p>
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</li>
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</ol>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="embedding-model">Embedding Model<a href="#embedding-model" class="hash-link" aria-label="Direct link to Embedding Model" title="Direct link to Embedding Model"></a></h2>
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<p>Some <strong>Embedding Model</strong> component input parameters are hidden by default in the visual editor.
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You can toggle 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>
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<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>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="additional-embedding-model-components">Additional embedding models<a href="#additional-embedding-model-components" class="hash-link" aria-label="Direct link to Additional embedding models" title="Direct link to Additional embedding models"></a></h2>
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<p>If your provider or model isn't supported by the <strong>Embedding Model</strong> core component, additional provider-specific <strong>Embedding Model</strong> components are available in the <a href="/components-bundle-components"><strong>Bundles</strong></a> section of the <strong>Components</strong> menu.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="legacy-embedding-components">Legacy embedding components<a href="#legacy-embedding-components" class="hash-link" aria-label="Direct link to Legacy embedding components" title="Direct link to Legacy embedding components"></a></h2>
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<p>The following components are legacy components.
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You can still use them in your flows, but they are no longer maintained and they can be removed in future releases.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Embedding Similarity</summary><div><div class="collapsibleContent_i85q"><p>The <strong>Embedding Similarity</strong> component is replaced by built-in similarity search functionality in <a href="/components-vector-stores"><strong>Vector Store</strong> components</a>.</p><p>This component calculates similarity scores for two embedding vectors.</p><p>It accepts the following parameters:</p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>embedding_vectors</td><td>Embedding Vectors</td><td>Input parameter. A list containing exactly two data objects with embedding vectors to compare.</td></tr><tr><td>similarity_metric</td><td>Similarity Metric</td><td>Input parameter. Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance".</td></tr><tr><td>similarity_data</td><td>Similarity Data</td><td>Output parameter. A data object containing the computed similarity score and additional information.</td></tr></tbody></table></div></div></details>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Text Embedder</summary><div><div class="collapsibleContent_i85q"><p>The <strong>Text Embedder</strong> component is replaced by the <strong>Embedding Model</strong> component.</p><p>This component generates embeddings for a given message using a specified embedding model.</p><p>It accepts the following parameters:</p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>embedding_model</td><td>Embedding Model</td><td>Input parameter. The embedding model to use for generating embeddings.</td></tr><tr><td>message</td><td>Message</td><td>Input parameter. The message for which to generate embeddings.</td></tr><tr><td>embeddings</td><td>Embedding Data</td><td>Output parameter. A data object containing the original text and its embedding vector.</td></tr></tbody></table></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="/components-models"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Language Model</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/components-data"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Data</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" class="table-of-contents__link toc-highlight">Embedding Model</a></li><li><a href="#additional-embedding-model-components" class="table-of-contents__link toc-highlight">Additional embedding models</a></li><li><a href="#legacy-embedding-components" class="table-of-contents__link toc-highlight">Legacy embedding components</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">
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