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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>
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<p>RAG is backed by a <strong>vector store</strong>, a vector database which stores embeddings of the ingested data.</p>
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<p>This enables <strong>vector search</strong>, a more powerful and context-aware search.</p>
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<p>We've chosen <a href="https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account" target="_blank" rel="noopener noreferrer">Astra DB</a> as the vector database for this starter flow, but you can follow along with any of Langflow's vector database options.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="prerequisites">Prerequisites<a href="#prerequisites" class="hash-link" aria-label="Direct link to Prerequisites" title="Direct link to Prerequisites"></a></h2>
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<ul>
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<li><a href="https://platform.openai.com/" target="_blank" rel="noopener noreferrer">An OpenAI API key</a></li>
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<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</a> with the following:<!-- -->
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<ul>
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<li>An Astra DB application token scoped to read and write to the database</li>
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<li>A collection created in <a href="https://docs.datastax.com/en/astra-db-serverless/databases/manage-collections.html#create-collection" target="_blank" rel="noopener noreferrer">Astra</a> or a new collection created in the <strong>Astra DB</strong> component</li>
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</ul>
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</li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="open-langflow-and-start-a-new-project">Open Langflow and start a new project<a href="#open-langflow-and-start-a-new-project" class="hash-link" aria-label="Direct link to Open Langflow and start a new project" title="Direct link to Open Langflow and start a new project"></a></h2>
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<ol>
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<li>From the Langflow dashboard, click <strong>New Flow</strong>.</li>
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<li>Select <strong>Vector Store RAG</strong>.</li>
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<li>The <strong>Vector Store RAG</strong> flow is created.</li>
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</ol>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="build-the-vector-rag-flow">Build the vector RAG flow<a href="#build-the-vector-rag-flow" class="hash-link" aria-label="Direct link to Build the vector RAG flow" title="Direct link to Build the vector RAG flow"></a></h2>
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<p>The vector store RAG flow is built of two separate flows for ingestion and query.</p>
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<p><img decoding="async" loading="lazy" src="/assets/images/starter-flow-vector-rag-d83743e5e049082b9c7a37aaa7b930e9.png" width="2740" height="1550" class="img_ev3q"></p>
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<p>The <strong>Load Data Flow</strong> (bottom of the screen) creates a searchable index to be queried for contextual similarity.
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This flow populates the vector store with data from a local file.
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It ingests data from a local file, splits it into chunks, indexes it in Astra DB, and computes embeddings for the chunks using the OpenAI embeddings model.</p>
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<p>The <strong>Retriever Flow</strong> (top of the screen) embeds the user's queries into vectors, which are compared to the vector store data from the <strong>Load Data Flow</strong> for contextual similarity.</p>
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<ul>
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<li><strong>Chat Input</strong> receives user input from the <strong>Playground</strong>.</li>
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<li><strong>OpenAI Embeddings</strong> converts the user query into vector form.</li>
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<li><strong>Astra DB</strong> performs similarity search using the query vector.</li>
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<li><strong>Parse Data</strong> processes the retrieved chunks.</li>
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<li><strong>Prompt</strong> combines the user query with relevant context.</li>
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<li><strong>OpenAI</strong> generates the response using the prompt.</li>
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<li><strong>Chat Output</strong> returns the response to the <strong>Playground</strong>.</li>
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</ul>
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<ol>
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<li>
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<p>Configure the <strong>OpenAI</strong> model component.</p>
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<ol>
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<li>To create a global variable for the <strong>OpenAI</strong> component, in the <strong>OpenAI API Key</strong> field, click 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-globe" aria-label="Globe"><circle cx="12" cy="12" r="10"></circle><path d="M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20"></path><path d="M2 12h20"></path></svg> <strong>Globe</strong> button, and then click <strong>Add New Variable</strong>.</li>
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<li>In the <strong>Variable Name</strong> field, enter <code>openai_api_key</code>.</li>
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<li>In the <strong>Value</strong> field, paste your OpenAI API Key (<code>sk-...</code>).</li>
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<li>Click <strong>Save Variable</strong>.</li>
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</ol>
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</li>
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<li>
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<p>Configure the <strong>Astra DB</strong> component.</p>
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<ol>
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<li>In the <strong>Astra DB Application Token</strong> field, add your <strong>Astra DB</strong> application token.
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The component connects to your database and populates the menus with existing databases and collections.</li>
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<li>Select your <strong>Database</strong>.
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If you don't have a collection, select <strong>New database</strong>.
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Complete the <strong>Name</strong>, <strong>Cloud provider</strong>, and <strong>Region</strong> fields, and then click <strong>Create</strong>. <strong>Database creation takes a few minutes</strong>.</li>
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<li>Select your <strong>Collection</strong>. Collections are created in your <a href="https://astra.datastax.com" target="_blank" rel="noopener noreferrer">Astra DB deployment</a> for storing vector data.</li>
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</ol>
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<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>If you select a collection embedded with Nvidia through Astra's vectorize service, the <strong>Embedding Model</strong> port is removed, because you have already generated embeddings for this collection with the Nvidia <code>NV-Embed-QA</code> model. The component fetches the data from the collection, and uses the same embeddings for queries.</p></div></div>
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</li>
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<li>
|
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<p>If you don't have a collection, create a new one within the component.</p>
|
||
<ol>
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||
<li>
|
||
<p>Select <strong>New collection</strong>.</p>
|
||
</li>
|
||
<li>
|
||
<p>Complete the <strong>Name</strong>, <strong>Embedding generation method</strong>, <strong>Embedding model</strong>, and <strong>Dimensions</strong> fields, and then click <strong>Create</strong>.</p>
|
||
<p>Your choice for the <strong>Embedding generation method</strong> and <strong>Embedding model</strong> depends on whether you want to use embeddings generated by a provider through Astra's vectorize service, or generated by a component in Langflow.</p>
|
||
<ul>
|
||
<li>To use embeddings generated by a provider through Astra's vectorize service, select the model from the <strong>Embedding generation method</strong> dropdown menu, and then select the model from the <strong>Embedding model</strong> dropdown menu.</li>
|
||
<li>To use embeddings generated by a component in Langflow, select <strong>Bring your own</strong> for both the <strong>Embedding generation method</strong> and <strong>Embedding model</strong> fields. In this starter project, the option for the embeddings method and model is the <strong>OpenAI Embeddings</strong> component connected to the <strong>Astra DB</strong> component.</li>
|
||
<li>The <strong>Dimensions</strong> value must match the dimensions of your collection. This field is <strong>not required</strong> if you use embeddings generated through Astra's vectorize service. You can find this value in the <strong>Collection</strong> in your <a href="https://astra.datastax.com" target="_blank" rel="noopener noreferrer">Astra DB deployment</a>.</li>
|
||
</ul>
|
||
<p>For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html" target="_blank" rel="noopener noreferrer">DataStax Astra DB Serverless documentation</a>.</p>
|
||
</li>
|
||
</ol>
|
||
</li>
|
||
</ol>
|
||
<p>If you used Langflow's <strong>Global Variables</strong> feature, the RAG application flow components are already configured with the necessary credentials.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="run-the-vector-store-rag-flow">Run the Vector Store RAG flow<a href="#run-the-vector-store-rag-flow" class="hash-link" aria-label="Direct link to Run the Vector Store RAG flow" title="Direct link to Run the Vector Store RAG flow"></a></h2>
|
||
<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 "What topics do you know about?")</li>
|
||
<li>The bot will respond with a summary of the data you'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-basic-prompting"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Basic prompting</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/starter-projects-simple-agent"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Simple 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 href="#prerequisites" class="table-of-contents__link toc-highlight">Prerequisites</a></li><li><a href="#open-langflow-and-start-a-new-project" class="table-of-contents__link toc-highlight">Open Langflow and start a new project</a></li><li><a href="#build-the-vector-rag-flow" class="table-of-contents__link toc-highlight">Build the vector RAG flow</a></li><li><a href="#run-the-vector-store-rag-flow" class="table-of-contents__link toc-highlight">Run the Vector Store RAG flow</a></li></ul></div></div></div></div></main></div></div></div><div style="position:fixed;right:20px;bottom:20px;z-index:100;display:flex;align-items:center;gap:10px;cursor:pointer"><div style="background-color:#f6f6f6;border-radius:50%;width:48px;height:48px;display:flex;align-items:center;justify-content:center;box-shadow:0 2px 4px rgba(0,0,0,0.1)"><img src="/img/langflow-icon-black-transparent.svg" style="width:40px" alt="Search"></div></div></div>
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