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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 class="" href="/data-types#table"><code>Table</code></a>, and then returns a new <code>Table</code> with the original text and an LLM response.
The output contains the following columns:</p>
<ul>
<li class=""><code>text_input</code>: The original text from the input <code>Table</code></li>
<li class=""><code>model_response</code>: The model&#x27;s response for each input</li>
<li class=""><code>batch_index</code>: The 0-indexed processing order for all rows in the <code>Table</code></li>
<li class=""><code>metadata</code> (optional): Additional information about the processing</li>
</ul>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" 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" translate="no"></a></h2>
<p>If you pass the <strong>Batch Run</strong> output to a <a class="" 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>.
This is demonstrated in the following example.</p>
<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>
<ol>
<li class="">
<p>Connect any language model component to a <strong>Batch Run</strong> component&#x27;s <strong>Language model</strong> port.</p>
</li>
<li class="">
<p>Connect <code>Table</code> output from another component to the <strong>Batch Run</strong> component&#x27;s <strong>DataFrame</strong> input.
For example, you could connect a <strong>Read File</strong> component with a CSV file.</p>
</li>
<li class="">
<p>In the <strong>Batch Run</strong> component&#x27;s <strong>Column Name</strong> field, enter the name of the column in the incoming <code>Table</code> that contains the text to process.
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>
</li>
<li class="">
<p>Connect the <strong>Batch Run</strong> component&#x27;s <strong>Batch Results</strong> output to a <strong>Parser</strong> component&#x27;s <strong>DataFrame</strong> input.</p>
</li>
<li class="">
<p>Optional: In the <strong>Batch Run</strong> <a class="" href="/concepts-components#component-menus">component menu</a>, 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.
For example, <code>Create a business card for each name.</code></p>
</li>
<li class="">
<p>In the <strong>Parser</strong> component&#x27;s <strong>Template</strong> field, enter a template for processing the <strong>Batch Run</strong> component&#x27;s new <code>Table</code> columns (<code>text_input</code>, <code>model_response</code>, and <code>batch_index</code>):</p>
<p>For example, this template uses three columns from the resulting, post-batch <code>Table</code>:</p>
<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>
</li>
<li class="">
<p>To test the processing, click the <strong>Parser</strong> component, click <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-play lf-inline-icon" aria-hidden="true" focusable="false" role="presentation"><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="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-text-search lf-inline-icon" aria-hidden="true" focusable="false" role="presentation"><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>Table</code>.</p>
<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>
</li>
</ol>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" 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" translate="no"></a></h2>
<p>Some parameters are hidden by default in the visual editor.
You can modify all component parameters through the <a class="" href="/concepts-components#component-inspection-panel">component inspection panel</a> that appears when you select a component.</p>
<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 &#x27;Language Model&#x27; 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 &#x27;column_name&#x27;. 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&#x27;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>Table</td><td>Output parameter. A DataFrame with all original columns plus the model&#x27;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="/guardrails"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Guardrails</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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