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<title data-rh="true">Batch Run | 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/batch-run"><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="Batch Run | Langflow Documentation"><meta data-rh="true" name="description" content="The Batch Run component runs a language model over each row of one text column in a DataFrame, and then returns a new DataFrame with the original text and an LLM response."><meta data-rh="true" property="og:description" content="The Batch Run component runs a language model over each row of one text column in a DataFrame, and then returns a new DataFrame with the original text and an LLM response."><link data-rh="true" rel="icon" href="/img/favicon.ico"><link data-rh="true" rel="canonical" href="https://docs.langflow.org/batch-run"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/batch-run" hreflang="en"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/batch-run" 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":"Batch Run","item":"https://docs.langflow.org/batch-run"}]}</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">LLM Operations</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Batch Run</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>Batch Run</h1></header><style>[data-ch-theme="github-dark"] { --ch-t-colorScheme: dark;--ch-t-foreground: #c9d1d9;--ch-t-background: #0d1117;--ch-t-lighter-inlineBackground: #0d1117e6;--ch-t-editor-background: #0d1117;--ch-t-editor-foreground: #c9d1d9;--ch-t-editor-lineHighlightBackground: #6e76811a;--ch-t-editor-rangeHighlightBackground: #ffffff0b;--ch-t-editor-infoForeground: #3794FF;--ch-t-editor-selectionBackground: #264F78;--ch-t-focusBorder: #1f6feb;--ch-t-tab-activeBackground: #0d1117;--ch-t-tab-activeForeground: #c9d1d9;--ch-t-tab-inactiveBackground: #010409;--ch-t-tab-inactiveForeground: #8b949e;--ch-t-tab-border: #30363d;--ch-t-tab-activeBorder: #0d1117;--ch-t-editorGroup-border: #30363d;--ch-t-editorGroupHeader-tabsBackground: #010409;--ch-t-editorLineNumber-foreground: #6e7681;--ch-t-input-background: #0d1117;--ch-t-input-foreground: #c9d1d9;--ch-t-input-border: #30363d;--ch-t-icon-foreground: #8b949e;--ch-t-sideBar-background: #010409;--ch-t-sideBar-foreground: #c9d1d9;--ch-t-sideBar-border: #30363d;--ch-t-list-activeSelectionBackground: #6e768166;--ch-t-list-activeSelectionForeground: #c9d1d9;--ch-t-list-hoverBackground: #6e76811a;--ch-t-list-hoverForeground: #c9d1d9; }</style>
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<!-- -->
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<p>The <strong>Batch Run</strong> component runs a language model over <em>each row of one text column</em> in a <a href="/data-types#dataframe"><code>DataFrame</code></a>, and then returns a new <code>DataFrame</code> with the original text and an LLM response.
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The output contains the following columns:</p>
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<ul>
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<li><code>text_input</code>: The original text from the input <code>DataFrame</code></li>
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<li><code>model_response</code>: The model's response for each input</li>
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<li><code>batch_index</code>: The 0-indexed processing order for all rows in the <code>DataFrame</code></li>
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<li><code>metadata</code> (optional): Additional information about the processing</li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="use-the-batch-run-component-in-a-flow">Use the Batch Run component in a flow<a href="#use-the-batch-run-component-in-a-flow" class="hash-link" aria-label="Direct link to Use the Batch Run component in a flow" title="Direct link to Use the Batch Run component in a flow"></a></h2>
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<p>If you pass the <strong>Batch Run</strong> output to a <a href="/parser"><strong>Parser</strong> component</a>, you can use variables in the parsing template to reference these keys, such as <code>{text_input}</code> and <code>{model_response}</code>.
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This is demonstrated in the following example.</p>
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<p><img decoding="async" loading="lazy" alt="A batch run component connected to OpenAI and a Parser" src="/assets/images/component-batch-run-19c94fbb0646b2731b37013b84dff1f6.png" width="4000" height="2728" class="img_ev3q"></p>
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<ol>
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<li>
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<p>Connect any language model component to a <strong>Batch Run</strong> component's <strong>Language model</strong> port.</p>
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</li>
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<li>
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<p>Connect <code>DataFrame</code> output from another component to the <strong>Batch Run</strong> component's <strong>DataFrame</strong> input.
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For example, you could connect a <strong>Read File</strong> component with a CSV file.</p>
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</li>
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<li>
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<p>In the <strong>Batch Run</strong> component's <strong>Column Name</strong> field, enter the name of the column in the incoming <code>DataFrame</code> that contains the text to process.
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For example, if you want to extract text from a <code>name</code> column in a CSV file, enter <code>name</code> in the <strong>Column Name</strong> field.</p>
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</li>
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<li>
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<p>Connect the <strong>Batch Run</strong> component's <strong>Batch Results</strong> output to a <strong>Parser</strong> component's <strong>DataFrame</strong> input.</p>
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</li>
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<li>
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<p>Optional: In the <strong>Batch Run</strong> <a href="/concepts-components#component-menus">component's header menu</a>, 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-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>, enable the <strong>System Message</strong> parameter, click <strong>Close</strong>, and then enter an instruction for how you want the LLM to process each cell extracted from the file.
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For example, <code>Create a business card for each name.</code></p>
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</li>
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<li>
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<p>In the <strong>Parser</strong> component's <strong>Template</strong> field, enter a template for processing the <strong>Batch Run</strong> component's new <code>DataFrame</code> columns (<code>text_input</code>, <code>model_response</code>, and <code>batch_index</code>):</p>
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<p>For example, this template uses three columns from the resulting, post-batch <code>DataFrame</code>:</p>
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<div class="ch-codeblock not-prose" data-ch-theme="github-dark"><div class="ch-code-wrapper ch-code" data-ch-measured="false"><code class="ch-code-scroll-parent"><br><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>record_number: {batch_index}, name: {text_input}, summary: {model_response}</span></div></div><br></code></div></div>
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</li>
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<li>
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||
<p>To test the processing, click the <strong>Parser</strong> component, 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>, 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-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> to view the final <code>DataFrame</code>.</p>
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<p>You can also connect a <strong>Chat Output</strong> component to the <strong>Parser</strong> component if you want to see the output in the <strong>Playground</strong>.</p>
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||
</li>
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||
</ol>
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||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="batch-run-parameters">Batch Run parameters<a href="#batch-run-parameters" class="hash-link" aria-label="Direct link to Batch Run parameters" title="Direct link to Batch Run parameters"></a></h2>
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<p>Some parameters are hidden by default in the visual editor.
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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>
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||
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>model</td><td>HandleInput</td><td>Input parameter. Connect the 'Language Model' output from a language model component. Required.</td></tr><tr><td>system_message</td><td>MultilineInput</td><td>Input parameter. A multi-line system instruction for all rows in the DataFrame.</td></tr><tr><td>df</td><td>DataFrameInput</td><td>Input parameter. The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required.</td></tr><tr><td>column_name</td><td>MessageTextInput</td><td>Input parameter. The name of the DataFrame column to treat as text messages. If empty, all columns are formatted in TOML.</td></tr><tr><td>output_column_name</td><td>MessageTextInput</td><td>Input parameter. Name of the column where the model's response is stored. Default=<code>model_response</code>.</td></tr><tr><td>enable_metadata</td><td>BoolInput</td><td>Input parameter. If <code>True</code>, add metadata to the output DataFrame.</td></tr><tr><td>batch_results</td><td>DataFrame</td><td>Output parameter. A DataFrame with all original columns plus the model's response column.</td></tr></tbody></table></div></article><nav class="docusaurus-mt-lg pagination-nav" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/run-flow"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Run Flow</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/llm-selector"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">LLM Selector</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-the-batch-run-component-in-a-flow" class="table-of-contents__link toc-highlight">Use the Batch Run component in a flow</a></li><li><a href="#batch-run-parameters" class="table-of-contents__link toc-highlight">Batch Run parameters</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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