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<!-- -->
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<p>Helper components provide utility functions to help manage data, tasks, and other components in your flow.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="calculator">Calculator<a href="#calculator" class="hash-link" aria-label="Direct link to Calculator" title="Direct link to Calculator"></a></h2>
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<p>The Calculator component performs basic arithmetic operations on mathematical expressions. It supports addition, subtraction, multiplication, division, and exponentiation operations.</p>
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<p>For an example of using this component in a flow, see the <a href="/components-processing#python-interpreter">Python interpreter</a> component.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>expression</td><td>String</td><td>The arithmetic expression to evaluate, such as <code>4*4*(33/22)+12-20</code>.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>result</td><td>Data</td><td>The calculation result as a Data object containing the evaluated expression.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="current-date">Current date<a href="#current-date" class="hash-link" aria-label="Direct link to Current date" title="Direct link to Current date"></a></h2>
|
||
<p>The Current Date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>timezone</td><td>String</td><td>The timezone for the current date and time.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>current_date</td><td>String</td><td>The resulting current date and time in the selected timezone.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="message-history">Message history<a href="#message-history" class="hash-link" aria-label="Direct link to Message history" title="Direct link to Message history"></a></h2>
|
||
<div class="theme-admonition theme-admonition-info admonition_xJq3 alert alert--info"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 14 16"><path fill-rule="evenodd" d="M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"></path></svg></span>info</div><div class="admonitionContent_BuS1"><p>Prior to Langflow 1.5, this component was two separate components called <strong>Chat History</strong> and <strong>Message Store</strong>.</p></div></div>
|
||
<p>This component provides combined chat history and message store functionality. This component can use Langflow's SQLite database or external memory to store and retrieve chat messages.</p>
|
||
<p>Chat memory is distinct from vector store memory, because it is built specifically for storing and retrieving chat messages from databases.</p>
|
||
<p>Memory components provide access to their respective external databases <strong>as memory</strong>. This allows Large Language Models (LLMs) or <a href="/agents">agents</a> to access external memory for persistence and context retention.</p>
|
||
<p>In <strong>Retrieve</strong> mode, this component retrieves chat messages from Langflow tables or external memory.
|
||
In <strong>Store</strong> mode, this component stores chat messages in Langflow tables or external memory.</p>
|
||
<p>In this example, one <strong>Message History</strong> component stores the complete chat history in a local Langflow table, which the other <strong>Message History</strong> component retrieves as context for the LLM to answer each question.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Message store and history components" src="/assets/images/component-message-history-message-store-2b194387b1eca9291289b3c9b7b9b49b.png" width="4000" height="1328" class="img_ev3q"></p>
|
||
<p>To configure Langflow to store and retrieve messages from an external database instead of local Langflow memory, follow these steps.</p>
|
||
<ol>
|
||
<li>Add two <strong>Memory</strong> components to your flow.
|
||
This example uses <strong>Redit Chat Memory</strong>.</li>
|
||
<li>To enable external memory ports, in both <strong>Memory</strong> components, 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>, and then enable <strong>External Memory</strong>.</li>
|
||
<li>Connect the <strong>Memory</strong> ports to the <strong>Message History</strong> components.
|
||
The flow looks like this:
|
||
<img decoding="async" loading="lazy" alt="Message store and history components with external memory" src="/assets/images/component-message-history-external-memory-7e32402d0b6e819f5539adfbfbd3620f.png" width="4000" height="1768" class="img_ev3q"></li>
|
||
<li>In the <strong>Redis Chat Memory</strong> components, add your connection information. These values are found in your Redis deployment. For more information, see the <a href="https://redis.io/docs/latest/" target="_blank" rel="noopener noreferrer">Redis documentation</a>.</li>
|
||
</ol>
|
||
<p>For more information on configuring memory in Langflow, see <a href="/memory">Memory</a>.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>memory</td><td>Memory</td><td>Retrieve messages from an external memory. If empty, the Langflow tables are used.</td></tr><tr><td>sender</td><td>String</td><td>Filter by sender type.</td></tr><tr><td>sender_name</td><td>String</td><td>Filter by sender name.</td></tr><tr><td>n_messages</td><td>Integer</td><td>The number of messages to retrieve.</td></tr><tr><td>session_id</td><td>String</td><td>The session ID of the chat. If empty, the current session ID parameter is used.</td></tr><tr><td>order</td><td>String</td><td>The order of the messages.</td></tr><tr><td>template</td><td>String</td><td>The template to use for formatting the data. It can contain the keys <code>{text}</code>, <code>{sender}</code> or any other key in the message data.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>messages</td><td>Data</td><td>The retrieved messages as Data objects.</td></tr><tr><td>messages_text</td><td>Message</td><td>The retrieved messages formatted as text.</td></tr><tr><td>dataframe</td><td>DataFrame</td><td>A DataFrame containing the message data.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="legacy-components">Legacy components<a href="#legacy-components" class="hash-link" aria-label="Direct link to Legacy components" title="Direct link to Legacy components"></a></h2>
|
||
<p>Legacy components are available for use but are no longer supported.</p>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="create-list">Create List<a href="#create-list" class="hash-link" aria-label="Direct link to Create List" title="Direct link to Create List"></a></h3>
|
||
<p>This component dynamically creates a record with a specified number of fields.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>n_fields</td><td>Integer</td><td>The number of fields to be added to the record.</td></tr><tr><td>text_key</td><td>String</td><td>The key used as text.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>list</td><td>List</td><td>The dynamically created list with the specified number of fields.</td></tr></tbody></table></div></div></details>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="id-generator">ID Generator<a href="#id-generator" class="hash-link" aria-label="Direct link to ID Generator" title="Direct link to ID Generator"></a></h3>
|
||
<p>This component generates a unique ID.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>unique_id</td><td>String</td><td>The generated unique ID.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>id</td><td>String</td><td>The generated unique ID.</td></tr></tbody></table></div></div></details>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="output-parser">Output Parser<a href="#output-parser" class="hash-link" aria-label="Direct link to Output Parser" title="Direct link to Output Parser"></a></h3>
|
||
<p>This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's <code>CommaSeparatedListOutputParser</code>.</p>
|
||
<div class="theme-admonition theme-admonition-note admonition_xJq3 alert alert--secondary"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 14 16"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 0 1-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 0 1-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg></span>note</div><div class="admonitionContent_BuS1"><p>This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.</p></div></div>
|
||
<p>Both the <strong>Output Parser</strong> and <strong>Structured Output</strong> components format LLM responses, but they have different use cases.
|
||
The <strong>Output Parser</strong> is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example <code>["item1", "item2", "item3"]</code>.
|
||
The <strong>Structured Output</strong> is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.</p>
|
||
<p>To use this component:</p>
|
||
<ol>
|
||
<li>Create a Prompt component and connect the Output Parser's <code>format_instructions</code> output to it. This ensures the LLM knows how to format its response.</li>
|
||
<li>Write your actual prompt text in the Prompt component, including the <code>{format_instructions}</code> variable.
|
||
For example, in your Prompt component, the template might look like:</li>
|
||
</ol>
|
||
<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>{format_instructions}</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Please list three fruits.</span></div></div><br></code></div></div>
|
||
<ol start="3">
|
||
<li>
|
||
<p>Connect the <code>output_parser</code> output to your LLM model.</p>
|
||
</li>
|
||
<li>
|
||
<p>The output parser converts this into a Python list: <code>["apple", "banana", "orange"]</code>.</p>
|
||
</li>
|
||
</ol>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>parser_type</td><td>String</td><td>The parser type. Currently supports "CSV".</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>format_instructions</td><td>String</td><td>Pass to a prompt template to include formatting instructions for LLM responses.</td></tr><tr><td>output_parser</td><td>Parser</td><td>The constructed output parser that can be used to parse LLM responses.</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-embedding-models"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Embedding models</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/components-io"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Inputs and outputs</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="#calculator" class="table-of-contents__link toc-highlight">Calculator</a></li><li><a href="#current-date" class="table-of-contents__link toc-highlight">Current date</a></li><li><a href="#message-history" class="table-of-contents__link toc-highlight">Message history</a></li><li><a href="#legacy-components" class="table-of-contents__link toc-highlight">Legacy components</a><ul><li><a href="#create-list" class="table-of-contents__link toc-highlight">Create List</a></li><li><a href="#id-generator" class="table-of-contents__link toc-highlight">ID Generator</a></li><li><a href="#output-parser" class="table-of-contents__link toc-highlight">Output Parser</a></li></ul></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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