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<!-- -->
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<p>Get to know Langflow by building an OpenAI-powered chatbot application. After you've constructed a chatbot, add Retrieval Augmented Generation (RAG) to chat with your own data.</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:<!-- -->
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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>, and then select <strong>Blank Flow</strong>. A blank workspace opens where you can build your flow.</li>
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</ol>
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<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>If you don't want to create a blank flow, click <strong>New Flow</strong>, and then select <strong>Basic Prompting</strong> for a pre-built flow.
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Continue to <a href="#run-basic-prompting-flow">Run the basic prompting flow</a>.</p></div></div>
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<ol start="2">
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<li>
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<p>Select <strong>Basic Prompting</strong>.</p>
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</li>
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<li>
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<p>The <strong>Basic Prompting</strong> flow is created.</p>
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</li>
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</ol>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="build-the-basic-prompting-flow">Build the basic prompting flow<a href="#build-the-basic-prompting-flow" class="hash-link" aria-label="Direct link to Build the basic prompting flow" title="Direct link to Build the basic prompting flow"></a></h2>
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<p>The Basic Prompting flow will look like this when it's completed:</p>
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<p><img decoding="async" loading="lazy" alt="Completed basic prompting flow" src="/assets/images/starter-flow-basic-prompting-09331815d7282bd6a3feedf84838ba20.png" width="2500" height="1528" class="img_ev3q"></p>
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<p>To build the <strong>Basic Prompting</strong> flow, follow these steps:</p>
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<ol>
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<li>Click <strong>Inputs</strong>, select the <strong>Chat Input</strong> component, and then drag it to the canvas.
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The <a href="/components-io#chat-input">Chat Input</a> component accepts user input to the chat.</li>
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<li>Click <strong>Prompt</strong>, select the <strong>Prompt</strong> component, and then drag it to the canvas.
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The <a href="/components-prompts">Prompt</a> component combines the user input with a user-defined prompt.</li>
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<li>Click <strong>Outputs</strong>, select the <strong>Chat Output</strong> component, and then drag it to the canvas.
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The <a href="/components-io#chat-output">Chat Output</a> component prints the flow's output to the chat.</li>
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<li>Click <strong>Models</strong>, select the <strong>OpenAI</strong> component, and then drag it to the canvas.
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The <a href="/components-models#openai">OpenAI</a> model component sends the user input and prompt to the OpenAI API and receives a response.</li>
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</ol>
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<p>You should now have a flow that looks like this:</p>
|
||
<p><img decoding="async" loading="lazy" alt="Basic prompting flow with no connections" src="/assets/images/quickstart-basic-prompt-no-connections-f5887f67c3448c39b74f7e28b65a0b18.png" width="2528" height="1530" class="img_ev3q"></p>
|
||
<p>With no connections between them, the components won't interact with each other.
|
||
You want data to flow from <strong>Chat Input</strong> to <strong>Chat Output</strong> through the connections between the components.
|
||
Each component accepts inputs on its left side, and sends outputs on its right side.
|
||
Hover over the connection ports to see the data types that the component accepts.
|
||
For more on component inputs and outputs, see <a href="/concepts-components">Components overview</a>.</p>
|
||
<ol start="5">
|
||
<li>To connect the <strong>Chat Input</strong> component to the OpenAI model component, click and drag a line from the blue <strong>Message</strong> port to the OpenAI model component's <strong>Input</strong> port.</li>
|
||
<li>To connect the <strong>Prompt</strong> component to the OpenAI model component, click and drag a line from the blue <strong>Prompt Message</strong> port to the OpenAI model component's <strong>System Message</strong> port.</li>
|
||
<li>To connect the <strong>OpenAI</strong> model component to the <strong>Chat Output</strong>, click and drag a line from the blue <strong>Text</strong> port to the <strong>Chat Output</strong> component's <strong>Text</strong> port.</li>
|
||
</ol>
|
||
<p>Your finished basic prompting flow should look like this:</p>
|
||
<p><img decoding="async" loading="lazy" src="/assets/images/starter-flow-basic-prompting-09331815d7282bd6a3feedf84838ba20.png" width="2500" height="1528" class="img_ev3q"></p>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="run-basic-prompting-flow">Run the Basic Prompting flow<a href="#run-basic-prompting-flow" class="hash-link" aria-label="Direct link to Run the Basic Prompting flow" title="Direct link to Run the Basic Prompting flow"></a></h3>
|
||
<p>Add your OpenAI API key to the OpenAI model component, and add a prompt to the Prompt component to instruct the model how to respond.</p>
|
||
<ol>
|
||
<li>
|
||
<p>Add your credentials to the OpenAI component. The fastest way to complete these fields is with Langflow’s <a href="/configuration-global-variables">Global Variables</a>.</p>
|
||
<ol>
|
||
<li>In the OpenAI component’s OpenAI API Key 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>.
|
||
Alternatively, click your username in the top right corner, and then click <strong>Settings</strong>, <strong>Global Variables</strong>, and then <strong>Add New</strong>.</li>
|
||
<li>Name your variable. Paste your OpenAI API key (sk-…) in the Value field.</li>
|
||
<li>In the <strong>Apply To Fields</strong> field, select the OpenAI API Key field to apply this variable to all OpenAI Embeddings components.</li>
|
||
</ol>
|
||
</li>
|
||
<li>
|
||
<p>To add a prompt to the <strong>Prompt</strong> component, click the <strong>Template</strong> field, and then enter your prompt.
|
||
The prompt guides the bot's responses to input.
|
||
If you're unsure, use <code>Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.</code></p>
|
||
</li>
|
||
<li>
|
||
<p>Click <strong>Playground</strong> to start a chat session.</p>
|
||
</li>
|
||
<li>
|
||
<p>Enter a query, and then make sure the bot responds according to the prompt you set in the <strong>Prompt</strong> component.</p>
|
||
</li>
|
||
</ol>
|
||
<p>You have successfully created a chatbot application using OpenAI in the Langflow Workspace.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="add-vector-rag-to-your-application">Add vector RAG to your application<a href="#add-vector-rag-to-your-application" class="hash-link" aria-label="Direct link to Add vector RAG to your application" title="Direct link to Add vector RAG to your application"></a></h2>
|
||
<p>You created a chatbot application with Langflow, but let's try an experiment.</p>
|
||
<ol>
|
||
<li>Ask the bot: <code>Who won the Oscar in 2024 for best movie?</code></li>
|
||
<li>The bot's response is similar to this:</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>I'm sorry, but I don't have information on events or awards that occurred after</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>October 2023, including the Oscars in 2024.</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>You may want to check the latest news or the official Oscars website</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>for the most current information.</span></div></div><br></code></div></div>
|
||
<p>Well, that's unfortunate, but you can load more up-to-date data with <strong>Retrieval Augmented Generation</strong>, or <strong>RAG</strong>.</p>
|
||
<p>Vector RAG allows you to load your own data and chat with it, unlocking a wider range of possibilities for your chatbot application.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="add-vector-rag-with-the-astra-db-component">Add vector RAG with the Astra DB component<a href="#add-vector-rag-with-the-astra-db-component" class="hash-link" aria-label="Direct link to Add vector RAG with the Astra DB component" title="Direct link to Add vector RAG with the Astra DB component"></a></h2>
|
||
<p>Build on the basic prompting flow and add vector RAG to your chatbot application with the <strong>Astra DB Vector Store</strong> component.</p>
|
||
<p>Add document ingestion to your basic prompting flow, with the <strong>Astra DB</strong> component as the vector store.</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>If you don't want to create a blank flow, click <strong>New Flow</strong>, and then select <strong>Vector RAG</strong> for a pre-built flow.</p></div></div>
|
||
<p>Adding vector RAG to the basic prompting flow will look like this when completed:</p>
|
||
<p><img decoding="async" loading="lazy" alt="Add document ingestion to the basic prompting flow" src="/assets/images/quickstart-add-document-ingestion-5f9756b0a4b7e232b1507e12f335b15e.png" width="2848" height="1324" class="img_ev3q"></p>
|
||
<p>To build the flow, follow these steps:</p>
|
||
<ol>
|
||
<li>Disconnect the <strong>Chat Input</strong> component from the <strong>OpenAI</strong> component by double-clicking on the connecting line.</li>
|
||
<li>Click <strong>Vector Stores</strong>, select the <strong>Astra DB</strong> component, and then drag it to the canvas.
|
||
The <a href="/components-vector-stores#astra-db-vector-store">Astra DB vector store</a> component connects to your <strong>Astra DB</strong> database.</li>
|
||
<li>Click <strong>Data</strong>, select the <strong>File</strong> component, and then drag it to the canvas.
|
||
The <a href="/components-data#file">File</a> component loads files from your local machine.</li>
|
||
<li>Click <strong>Processing</strong>, select the <strong>Split Text</strong> component, and then drag it to the canvas.
|
||
The <a href="/components-processing#split-text">Split Text</a> component splits the loaded text into smaller chunks.</li>
|
||
<li>Click <strong>Processing</strong>, select the <strong>Parse Data</strong> component, and then drag it to the canvas.
|
||
The <a href="/components-processing#data-to-message">Data to Message</a> component converts the data from the <strong>Astra DB</strong> component into plain text.</li>
|
||
<li>Click <strong>Embeddings</strong>, select the <strong>OpenAI Embeddings</strong> component, and then drag it to the canvas.
|
||
The <a href="/components-embedding-models#openai-embeddings">OpenAI Embeddings</a> component generates embeddings for the user's input, which are compared to the vector data in the database.</li>
|
||
<li>Connect the new components into the existing flow, so your flow looks like this:</li>
|
||
</ol>
|
||
<p><img decoding="async" loading="lazy" alt="Add document ingestion to the basic prompting flow" src="/assets/images/quickstart-add-document-ingestion-5f9756b0a4b7e232b1507e12f335b15e.png" width="2848" height="1324" class="img_ev3q"></p>
|
||
<ol start="8">
|
||
<li>
|
||
<p>Configure the <strong>Astra DB</strong> component.</p>
|
||
<ol>
|
||
<li>In the <strong>Astra DB Application Token</strong> field, add your <strong>Astra DB</strong> application token.
|
||
The component connects to your database and populates the menus with existing databases and collections.</li>
|
||
<li>Select your <strong>Database</strong>.
|
||
If you don't have a collection, select <strong>New database</strong>.
|
||
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>
|
||
<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>
|
||
</ol>
|
||
<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>
|
||
</li>
|
||
<li>
|
||
<p>If you don't have a collection, create a new one within the component.</p>
|
||
<ol>
|
||
<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>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="run-the-chatbot-with-retrieved-context">Run the chatbot with retrieved context<a href="#run-the-chatbot-with-retrieved-context" class="hash-link" aria-label="Direct link to Run the chatbot with retrieved context" title="Direct link to Run the chatbot with retrieved context"></a></h3>
|
||
<ol>
|
||
<li>Modify the <strong>Prompt</strong> component to contain variables for both <code>{user_question}</code> and <code>{context}</code>.
|
||
The <code>{context}</code> variable gives the bot additional context for answering <code>{user_question}</code> beyond what the LLM was trained on.</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>Given the context</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>{context}</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Answer the question</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>{user_question}</span></div></div><br></code></div></div>
|
||
<ol start="2">
|
||
<li>In the <strong>File</strong> component, upload a text file from your local machine with data you want to ingest into the <strong>Astra DB</strong> component database.
|
||
This example uploads an up-to-date CSV about Oscar winners.</li>
|
||
<li>Click <strong>Playground</strong> to start a chat session.</li>
|
||
<li>Ask the bot: <code>Who won the Oscar in 2024 for best movie?</code></li>
|
||
<li>The bot's response should be similar to this:</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>The Oscar for Best Picture in 2024 was awarded to "Oppenheimer,"</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>produced by Emma Thomas, Charles Roven, and Christopher Nolan.</span></div></div><br></code></div></div>
|
||
<p>Adding an <strong>Astra DB</strong> vector store brought your chatbot all the way into 2024.
|
||
You have successfully added RAG to your chatbot application using the <strong>Astra DB</strong> component.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="next-steps">Next steps<a href="#next-steps" class="hash-link" aria-label="Direct link to Next steps" title="Direct link to Next steps"></a></h2>
|
||
<p>This example used movie data, but the RAG pattern can be used with any data you want to load and chat with.</p>
|
||
<p>Make the <strong>Astra DB</strong> database the brain that <a href="/agents-overview">Agents</a> use to make decisions.</p>
|
||
<p>Expose this flow as an <a href="/concepts-api">API</a> and call it from your external applications.</p>
|
||
<p>For more on the <strong>Astra DB</strong> component, see <a href="/components-vector-stores#astra-db-vector-store">Astra DB vector store</a>.</p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/get-started-installation"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Install Langflow</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/starter-projects-basic-prompting"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Basic prompting</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-basic-prompting-flow" class="table-of-contents__link toc-highlight">Build the basic prompting flow</a><ul><li><a href="#run-basic-prompting-flow" class="table-of-contents__link toc-highlight">Run the Basic Prompting flow</a></li></ul></li><li><a href="#add-vector-rag-to-your-application" class="table-of-contents__link toc-highlight">Add vector RAG to your application</a></li><li><a href="#add-vector-rag-with-the-astra-db-component" class="table-of-contents__link toc-highlight">Add vector RAG with the Astra DB component</a><ul><li><a href="#run-the-chatbot-with-retrieved-context" class="table-of-contents__link toc-highlight">Run the chatbot with retrieved context</a></li></ul></li><li><a href="#next-steps" class="table-of-contents__link toc-highlight">Next steps</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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|
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