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<title data-rh="true">Vector Store RAG | 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/starter-projects-vector-store-rag"><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="Vector Store RAG | Langflow Documentation"><meta data-rh="true" name="description" content="Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it."><meta data-rh="true" property="og:description" content="Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it."><link data-rh="true" rel="icon" href="/img/favicon.ico"><link data-rh="true" rel="canonical" href="https://docs.langflow.org/starter-projects-vector-store-rag"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/starter-projects-vector-store-rag" hreflang="en"><link data-rh="true" rel="alternate" href="https://docs.langflow.org/starter-projects-vector-store-rag" hreflang="x-default"><link rel="preconnect" href="https://www.google-analytics.com">
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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">Starter Projects</span><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Vector Store RAG</span><meta itemprop="position" content="2"></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>Vector Store RAG</h1></header><p>Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it.</p>
<p>RAG is backed by a <strong>vector store</strong>, a vector database which stores embeddings of the ingested data.</p>
<p>This enables <strong>vector search</strong>, a more powerful and context-aware search.</p>
<p>We&#x27;ve chosen <a href="https://astra.datastax.com/signup?utm_source=langflow-pre-release&amp;utm_medium=referral&amp;utm_campaign=langflow-announcement&amp;utm_content=create-a-free-astra-db-account" target="_blank" rel="noopener noreferrer">Astra DB</a> as the vector database for this starter project, but you can follow along with any of Langflow&#x27;s vector database options.</p>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="6aa2c6dff6894eccadc39d4903d79e66">Prerequisites<a class="hash-link" aria-label="Direct link to Prerequisites" title="Direct link to Prerequisites" href="/starter-projects-vector-store-rag#6aa2c6dff6894eccadc39d4903d79e66"></a></h2>
<hr>
<ul>
<li><a href="https://docs.langflow.org/getting-started-installation" target="_blank" rel="noopener noreferrer">Langflow installed and running</a></li>
<li><a href="https://platform.openai.com/" target="_blank" rel="noopener noreferrer">OpenAI API key</a></li>
<li><a href="https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html" target="_blank" rel="noopener noreferrer">An Astra DB vector database created</a> with:<!-- -->
<ul>
<li>Application Token</li>
<li>API Endpoint</li>
</ul>
</li>
</ul>
<p><img decoding="async" loading="lazy" src="/assets/images/648489928-577b6d447c9ac8bea324a1a77eee3537.png" width="1216" height="684" class="img_ev3q"></p>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="1c37d15332f94cfe8f6e11dc6cc8a4ea">Vector Store RAG<a class="hash-link" aria-label="Direct link to Vector Store RAG" title="Direct link to Vector Store RAG" href="/starter-projects-vector-store-rag#1c37d15332f94cfe8f6e11dc6cc8a4ea"></a></h2>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="e3ed64193e5e448f81279e1d54ba43cf">Create the vector store RAG project<a class="hash-link" aria-label="Direct link to Create the vector store RAG project" title="Direct link to Create the vector store RAG project" href="/starter-projects-vector-store-rag#e3ed64193e5e448f81279e1d54ba43cf"></a></h3>
<ol>
<li>From the Langflow dashboard, click <strong>New Project</strong>.</li>
<li>Select <strong>Vector Store RAG</strong>.</li>
<li>The <strong>Vector Store RAG</strong> project is created.</li>
</ol>
<p><img decoding="async" loading="lazy" src="/assets/images/1946624394-655b32fb8cd5857a67d5d66d6e5e23de.png" width="2624" height="1476" class="img_ev3q"></p>
<p>The vector store RAG flow is built of two separate flows. Ingestion and query.</p>
<p>The <strong>ingestion</strong> part (bottom of the screen) populates the vector store with data from a local file. It ingests data from a file (<strong>File</strong>), splits it into chunks (<strong>Split Text</strong>), indexes it in Astra DB (<strong>Astra DB</strong>), and computes embeddings for the chunks using an embedding model (<strong>OpenAI Embeddings</strong>).</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>tip</div><div class="admonitionContent_BuS1"><p>Embeddings are numerical vectors that represent data meaningfully. They enable efficient similarity searches in vector stores by placing similar items close together in the vector space, enhancing search and recommendation tasks.</p></div></div>
<p>This part creates a searchable index to be queried for contextual similarity.</p>
<p>The <strong>query</strong> part (top of the screen) allows users to retrieve embedded vector store data. Components:</p>
<ul>
<li><strong>Chat Input</strong> defines where to send the user input (coming from the Playground).</li>
<li><strong>OpenAI Embeddings</strong> is the model used to generate embeddings from the user input.</li>
<li><strong>Astra DB</strong> retrieves the most relevant chunks from the Astra DB database (here, used for search, not ingestion).</li>
<li><strong>Parse Data</strong> converts chunks coming from the <strong>Astra DB</strong> component into plain text to feed a prompt.</li>
<li><strong>Prompt</strong> takes in the user input and the retrieved chunks as text and builds a prompt for the model.</li>
<li><strong>OpenAI</strong> takes in the prompt to generate a response.</li>
<li><strong>Chat Output</strong> component displays the response in the Playground.</li>
</ul>
<ol>
<li>To create an environment variable for the <strong>OpenAI</strong> component, in the <strong>OpenAI API Key</strong> field, click the <strong>Globe</strong> button, and then click <strong>Add New Variable</strong>.<!-- -->
<ol>
<li>In the <strong>Variable Name</strong> field, enter <code>openai_api_key</code>.</li>
<li>In the <strong>Value</strong> field, paste your OpenAI API Key (<code>sk-...</code>).</li>
<li>Click <strong>Save Variable</strong>.</li>
</ol>
</li>
<li>To create environment variables for the <strong>Astra DB</strong> and <strong>Astra DB Search</strong> components:<!-- -->
<ol>
<li>In the <strong>Token</strong> field, click the <strong>Globe</strong> button, and then click <strong>Add New Variable</strong>.</li>
<li>In the <strong>Variable Name</strong> field, enter <code>astra_token</code>.</li>
<li>In the <strong>Value</strong> field, paste your Astra application token (<code>AstraCS:WSnyFUhRxsrg…</code>).</li>
<li>Click <strong>Save Variable</strong>.</li>
<li>Repeat the above steps for the <strong>API Endpoint</strong> field, pasting your Astra API Endpoint instead (<code>https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com</code>).</li>
<li>Add the global variable to both the <strong>Astra DB</strong> and <strong>Astra DB Search</strong> components.</li>
</ol>
</li>
</ol>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="815a6536d2d548d987f0f4e375a58b15">Run the Vector Store RAG<a class="hash-link" aria-label="Direct link to Run the Vector Store RAG" title="Direct link to Run the Vector Store RAG" href="/starter-projects-vector-store-rag#815a6536d2d548d987f0f4e375a58b15"></a></h3>
<ol>
<li>Click the <strong>Playground</strong> button. Here you can chat with the AI that uses context from the database you created.</li>
<li>Type a message and press Enter. (Try something like &quot;What topics do you know about?&quot;)</li>
<li>The bot will respond with a summary of the data you&#x27;ve embedded.</li>
</ol></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/starter-projects-memory-chatbot"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Memory Chatbot</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/Starter-Projects/starter-projects-dynamic-agent"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Dynamic Agent</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 class="table-of-contents__link toc-highlight" href="/starter-projects-vector-store-rag#6aa2c6dff6894eccadc39d4903d79e66">Prerequisites</a></li><li><a class="table-of-contents__link toc-highlight" href="/starter-projects-vector-store-rag#1c37d15332f94cfe8f6e11dc6cc8a4ea">Vector Store RAG</a><ul><li><a class="table-of-contents__link toc-highlight" href="/starter-projects-vector-store-rag#e3ed64193e5e448f81279e1d54ba43cf">Create the vector store RAG project</a></li><li><a class="table-of-contents__link toc-highlight" href="/starter-projects-vector-store-rag#815a6536d2d548d987f0f4e375a58b15">Run the Vector Store RAG</a></li></ul></li></ul></div></div></div></div></main></div></div></div><div id="ms-floating-button" class="ms-fixed ms-bottom-4 ms-right-8 ms-z-[100] ms-flex ms-flex-col ms-items-end hover:ms-cursor-pointer"><div class="ms-relative ms-mb-4"><div style="background-color:#f6f6f6" class="ms-absolute -ms-bottom-1 ms-right-6 -ms-z-10 ms-h-4 ms-w-4 ms-rotate-45"></div><div style="background-color:#f6f6f6" class="ms-z-10 ms-flex ms-items-center ms-justify-center ms-rounded-lg ms-px-3 ms-py-2"><p style="color:#000000;margin:0" class="ms-text-sm">Hi, how can I help you?</p></div></div><div style="background-color:#f6f6f6" class="ms-flex ms-h-16 ms-w-16 ms-flex-col ms-items-center ms-justify-center ms-self-end ms-rounded-full ms-p-2 ms-transition"><div class="ms-rotate-0 ms-flex ms-h-full ms-w-full ms-transform ms-cursor-pointer ms-items-center ms-justify-center ms-rounded-full ms-transition-all ms-delay-75 ms-duration-100 ms-ease-in-out"><div style="color:#000000;font-size:22px;width:48px;height:48px;margin:0px;padding:0px;display:flex;align-items:center;justify-content:center;text-align:center"><img src="/img/chain.png" style="width:40px"></div></div></div><div></div></div></div>
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