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</li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 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">Components reference</span></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Core components</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Vector Stores</span></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 Stores</h1></header><style>[data-ch-theme="github-dark"] { --ch-t-colorScheme: dark;--ch-t-foreground: #c9d1d9;--ch-t-background: #0d1117;--ch-t-lighter-inlineBackground: #0d1117e6;--ch-t-editor-background: #0d1117;--ch-t-editor-foreground: #c9d1d9;--ch-t-editor-lineHighlightBackground: #6e76811a;--ch-t-editor-rangeHighlightBackground: #ffffff0b;--ch-t-editor-infoForeground: #3794FF;--ch-t-editor-selectionBackground: #264F78;--ch-t-focusBorder: #1f6feb;--ch-t-tab-activeBackground: #0d1117;--ch-t-tab-activeForeground: #c9d1d9;--ch-t-tab-inactiveBackground: #010409;--ch-t-tab-inactiveForeground: #8b949e;--ch-t-tab-border: #30363d;--ch-t-tab-activeBorder: #0d1117;--ch-t-editorGroup-border: #30363d;--ch-t-editorGroupHeader-tabsBackground: #010409;--ch-t-editorLineNumber-foreground: #6e7681;--ch-t-input-background: #0d1117;--ch-t-input-foreground: #c9d1d9;--ch-t-input-border: #30363d;--ch-t-icon-foreground: #8b949e;--ch-t-sideBar-background: #010409;--ch-t-sideBar-foreground: #c9d1d9;--ch-t-sideBar-border: #30363d;--ch-t-list-activeSelectionBackground: #6e768166;--ch-t-list-activeSelectionForeground: #c9d1d9;--ch-t-list-hoverBackground: #6e76811a;--ch-t-list-hoverForeground: #c9d1d9; }</style>
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<!-- -->
|
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<p>Langflow's <strong>Vector Store</strong> components connect to your vector databases or create in-memory vector stores for storing and retrieving vector data in flows.</p>
|
||
<p>Vector databases and <strong>Vector Store</strong> components are specifically designed for storing and retrieving vector data, such as embeddings generated by language models. They are used to perform similarity searches, enabling applications like chatbots to retrieve relevant context from large datasets.</p>
|
||
<p>Other types of storage, like traditional structured databases and chat memory, are handled through other components like the <a href="/components-data#sql-database"><strong>SQL Database</strong> component</a> or the <a href="/components-helpers#message-history"><strong>Message History</strong> component</a>.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="use-a-vector-store-component-in-a-flow">Use a Vector Store component in a flow<a href="#use-a-vector-store-component-in-a-flow" class="hash-link" aria-label="Direct link to Use a Vector Store component in a flow" title="Direct link to Use a Vector Store component in a flow"></a></h2>
|
||
<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>For an tutorial using <strong>Vector Store</strong> components in a flow, see <a href="/chat-with-rag">Create a vector RAG chatbot</a>.</p></div></div>
|
||
<p>This example uses the <strong>Chroma DB</strong> vector store component. Your <strong>Vector Store</strong> component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The <strong>Vector Store</strong> component generates embeddings with the connected <a href="/components-embedding-models"><strong>Embedding Model</strong> component</a>, and stores them in the connected vector database.
|
||
This vector data can then be retrieved for workloads like Retrieval Augmented Generation (RAG).</p>
|
||
<p><img decoding="async" loading="lazy" alt="Embedding data into a vector store" src="/assets/images/vector-store-document-ingestion-6157311fb4d16e7f944d55254f0cc0e2.png" width="4000" height="2512" class="img_ev3q"></p>
|
||
<p>The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search.
|
||
The results are output from the <strong>Vector Store</strong> component as a <a href="/data-types#data"><code>Data</code></a> object and parsed into text.
|
||
This text fills the <code>{context}</code> variable in the <strong>Prompt Template</strong> component, which informs the <strong>OpenAI</strong> language model component's responses.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Retrieval from a vector store" src="/assets/images/vector-store-retrieval-af7257d77ff0259ab1a0980641d464ce.png" width="4000" height="1324" class="img_ev3q"></p>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="configure-vector-store-parameters">Configure vector store parameters<a href="#configure-vector-store-parameters" class="hash-link" aria-label="Direct link to Configure vector store parameters" title="Direct link to Configure vector store parameters"></a></h3>
|
||
<p>Most <strong>Vector Store</strong> components have the same utility within a flow, but each provider can offer different parameters and functionality.
|
||
Inspect a component's parameters to learn more about the inputs it accepts and how to configure it.</p>
|
||
<p>Many input parameters for <strong>Vector Store</strong> components are hidden by default in the visual editor.
|
||
You can toggle parameters through 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-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> in each <a href="/concepts-components#component-menus">component's header menu</a>.</p>
|
||
<p>For details about a specific provider's parameters, see the provider's documentation.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="astra-db">Astra DB<a href="#astra-db" class="hash-link" aria-label="Direct link to Astra DB" title="Direct link to Astra DB"></a></h2>
|
||
<p>This component implements an <a href="https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html" target="_blank" rel="noopener noreferrer">Astra DB Serverless vector store</a> with search capabilities.</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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>token</td><td>Astra DB Application Token</td><td>An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections.</td></tr><tr><td>environment</td><td>Environment</td><td>The environment for the Astra DB API Endpoint. For example, <code>dev</code> or <code>prod</code>.</td></tr><tr><td>database_name</td><td>Database</td><td>The name of the database that you want this component to connect to, or select <strong>New Database</strong> to create a new database. To create a new database, you must provide the database details, and then wait for the database to initialize.</td></tr><tr><td>api_endpoint</td><td>Astra DB API Endpoint</td><td>The API endpoint for the Astra DB instance. This supersedes the database selection.</td></tr><tr><td>collection_name</td><td>Collection</td><td>The name of the collection that you want to use with this flow, or click <strong>New Collection</strong> to create a new collection.</td></tr><tr><td>keyspace</td><td>Keyspace</td><td>An optional keyspace within Astra DB to use for the collection.</td></tr><tr><td>embedding_choice</td><td>Embedding Model or Astra Vectorize</td><td>Choose an embedding model or use Astra vectorize. If the collection has a vectorize integration, <strong>Astra Vectorize</strong> can be selected automatically.</td></tr><tr><td>embedding_model</td><td>Embedding Model</td><td>Specify the embedding model. Not required if the embedding choice is <strong>Astra Vectorize</strong> because the component automatically uses the integrated model.</td></tr><tr><td>number_of_results</td><td>Number of Search Results</td><td>The number of search results to return. Default:<code>4</code>.</td></tr><tr><td>search_type</td><td>Search Type</td><td>The search type to use. The options are <code>Similarity</code>, <code>Similarity with score threshold</code>, and <code>MMR (Max Marginal Relevance)</code>.</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>The minimum similarity score threshold for search results when using the <code>Similarity with score threshold</code> option.</td></tr><tr><td>advanced_search_filter</td><td>Search Metadata Filter</td><td>An optional dictionary of filters to apply to the search query.</td></tr><tr><td>autodetect_collection</td><td>Autodetect Collection</td><td>Boolean flag to determine whether to autodetect the collection.</td></tr><tr><td>content_field</td><td>Content Field</td><td>A field to use as the text content field for the vector store.</td></tr><tr><td>deletion_field</td><td>Deletion Based On Field</td><td>When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new data is loaded.</td></tr><tr><td>ignore_invalid_documents</td><td>Ignore Invalid Documents</td><td>Boolean flag to determine whether to ignore invalid documents at runtime.</td></tr><tr><td>astradb_vectorstore_kwargs</td><td>AstraDBVectorStore Parameters</td><td>An optional dictionary of additional parameters for the AstraDBVectorStore.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The Astra DB vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="generate-embeddings">Generate embeddings<a href="#generate-embeddings" class="hash-link" aria-label="Direct link to Generate embeddings" title="Direct link to Generate embeddings"></a></h3>
|
||
<p>The <strong>Astra DB</strong> component offers two methods for generating embeddings.</p>
|
||
<ul>
|
||
<li>
|
||
<p>*<strong>Embedding Model</strong>: Use your own embedding model by connecting an <a href="/components-embedding-models"><strong>Embedding Model</strong> component</a> in Langflow.</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Astra Vectorize</strong>: Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's <code>NV-Embed-QA</code> model hosted by DataStax.
|
||
For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html" target="_blank" rel="noopener noreferrer">Astra DB Serverless documentation</a>.</p>
|
||
<div class="theme-admonition theme-admonition-important 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>important</div><div class="admonitionContent_BuS1"><p>With vectorize, the embedding model you choose when you create a collection cannot be changed later.</p></div></div>
|
||
</li>
|
||
</ul>
|
||
<p>For an example of using the <strong>Astra DB</strong> component with an embedding model, see the <strong>Vector Store RAG</strong> template.</p>
|
||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="hybrid-search">Hybrid search<a href="#hybrid-search" class="hash-link" aria-label="Direct link to Hybrid search" title="Direct link to Hybrid search"></a></h3>
|
||
<p>The <strong>Astra DB</strong> component includes Astra DB's <a href="https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html" target="_blank" rel="noopener noreferrer">hybrid search</a> feature through the Astra DB Data API.</p>
|
||
<p>Hybrid search performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.</p>
|
||
<p>To use hybrid search through the <strong>Astra DB</strong> component, you must <a href="https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid" target="_blank" rel="noopener noreferrer">create a collection with that supports hybrid search</a>.</p>
|
||
<p>The following <strong>Astra DB</strong> component parameters are used for hybrid search:</p>
|
||
<ul>
|
||
<li><strong>Search Query</strong>: The query to use for vector search.</li>
|
||
<li><strong>Lexical Terms</strong>: A comma-separated string of keywords, like <code>features, data, attributes, characteristics</code>.</li>
|
||
<li><strong>Reranker</strong>: The re-ranker model to use for hybrid search, such as <code>nvidia/llama-3.2-nv.reranker</code>.</li>
|
||
</ul>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Hybrid search example</summary><div><div class="collapsibleContent_i85q"><p>To use hybrid search through the <strong>Astra DB</strong> component, do the following:</p><ol>
|
||
<li>
|
||
<p>Create a flow based on the <strong>Hybrid Search RAG</strong> template.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>OpenAI</strong> component, add your OpenAI API key.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Astra DB</strong> vector store component, add your <strong>Astra DB Application Token</strong>.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Database</strong> field, select your database.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Collection</strong> field, select or create a collection with hybrid search capabilities enabled.</p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Playground</strong>, enter a question about your data, such as <code>What are the features of my data?</code></p>
|
||
<p>Your query is sent to the <strong>OpenAI</strong> and <strong>Astra DB</strong> components.
|
||
The <strong>OpenAI</strong> component contains a prompt for creating the lexical query from your input:</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>You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>You should convert the query into:</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>2. A question to use as the basis for a QA embedding engine.</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Avoid common keywords associated with the user's subject matter.</span></div></div><br></code></div></div>
|
||
</li>
|
||
<li>
|
||
<p>To view the keywords and questions the <strong>OpenAI</strong> component generates from your collection, in the <strong>OpenAI</strong> component, 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-text-search" aria-hidden="true"><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>.</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>1. Keywords: features, data, attributes, characteristics</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>2. Question: What characteristics can be identified in my data?</span></div></div><br></code></div></div>
|
||
</li>
|
||
<li>
|
||
<p>To view the <a href="/data-types#dataframe">DataFrame</a> generated from the <strong>OpenAI</strong> component's response, in the <strong>Structured Output</strong> component, 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-text-search" aria-hidden="true"><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>.</p>
|
||
<p>The DataFrame is passed to a <strong>Parser</strong> component, which parses the contents of the <strong>Keywords</strong> column into a string.</p>
|
||
<p>This string of comma-separated words is passed to the <strong>Lexical Terms</strong> port of the <strong>Astra DB</strong> component.
|
||
Note that the <strong>Search Query</strong> port of the <strong>Astra DB</strong> component is connected to the <strong>Chat Input</strong> component.
|
||
The search query is vectorized, and both the <strong>Search Query</strong> and <strong>Lexical Terms</strong> content are sent to the reranker at the <code>find_and_rerank</code> endpoint.
|
||
The reranker compares the vector search results against the string of terms from the lexical search.
|
||
The highest-ranked results of your hybrid search are returned to the <strong>Playground</strong>.</p>
|
||
</li>
|
||
</ol></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="astra-db-graph">Astra DB Graph<a href="#astra-db-graph" class="hash-link" aria-label="Direct link to Astra DB Graph" title="Direct link to Astra DB Graph"></a></h2>
|
||
<p>This component implements a vector store using Astra DB with graph capabilities.
|
||
For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html" target="_blank" rel="noopener noreferrer">Astra DB Serverless documentation</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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>collection_name</td><td>Collection Name</td><td>The name of the collection within Astra DB where the vectors are stored. Required.</td></tr><tr><td>token</td><td>Astra DB Application Token</td><td>Authentication token for accessing Astra DB. Required.</td></tr><tr><td>api_endpoint</td><td>API Endpoint</td><td>API endpoint URL for the Astra DB service. Required.</td></tr><tr><td>search_input</td><td>Search Input</td><td>Query string for similarity search.</td></tr><tr><td>ingest_data</td><td>Ingest Data</td><td>Data to be ingested into the vector store.</td></tr><tr><td>keyspace</td><td>Keyspace</td><td>Optional keyspace within Astra DB to use for the collection.</td></tr><tr><td>embedding</td><td>Embedding Model</td><td>Embedding model to use.</td></tr><tr><td>metric</td><td>Metric</td><td>Distance metric for vector comparisons. The options are "cosine", "euclidean", "dot_product".</td></tr><tr><td>setup_mode</td><td>Setup Mode</td><td>Configuration mode for setting up the vector store. The options are "Sync", "Async", "Off".</td></tr><tr><td>pre_delete_collection</td><td>Pre Delete Collection</td><td>Boolean flag to determine whether to delete the collection before creating a new one.</td></tr><tr><td>number_of_results</td><td>Number of Results</td><td>Number of results to return in similarity search. Default: 4.</td></tr><tr><td>search_type</td><td>Search Type</td><td>Search type to use. The options are "Similarity", "Graph Traversal", "Hybrid".</td></tr><tr><td>traversal_depth</td><td>Traversal Depth</td><td>Maximum depth for graph traversal searches. Default: 1.</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>Minimum similarity score threshold for search results.</td></tr><tr><td>search_filter</td><td>Search Metadata Filter</td><td>Optional dictionary of filters to apply to the search query.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The Graph RAG vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cassandra">Cassandra<a href="#cassandra" class="hash-link" aria-label="Direct link to Cassandra" title="Direct link to Cassandra"></a></h2>
|
||
<p>This component creates a Cassandra vector store with search capabilities.
|
||
For more information, see the <a href="https://cassandra.apache.org/doc/latest/cassandra/vector-search/overview.html" target="_blank" rel="noopener noreferrer">Cassandra documentation</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>database_ref</td><td>String</td><td>Contact points for the database or Astra DB database ID.</td></tr><tr><td>username</td><td>String</td><td>Username for the database (leave empty for Astra DB).</td></tr><tr><td>token</td><td>SecretString</td><td>User password for the database or Astra DB token.</td></tr><tr><td>keyspace</td><td>String</td><td>Table or keyspace.</td></tr><tr><td>table_name</td><td>String</td><td>Name of the table or Astra DB collection.</td></tr><tr><td>ttl_seconds</td><td>Integer</td><td>Time-to-live for added texts.</td></tr><tr><td>batch_size</td><td>Integer</td><td>Number of data to process in a single batch.</td></tr><tr><td>setup_mode</td><td>String</td><td>Configuration mode for setting up the Cassandra table.</td></tr><tr><td>cluster_kwargs</td><td>Dict</td><td>Additional keyword arguments for the Cassandra cluster.</td></tr><tr><td>search_query</td><td>String</td><td>Query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>Data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search.</td></tr><tr><td>search_type</td><td>String</td><td>Type of search to perform.</td></tr><tr><td>search_score_threshold</td><td>Float</td><td>Minimum similarity score for search results.</td></tr><tr><td>search_filter</td><td>Dict</td><td>Metadata filters for search query.</td></tr><tr><td>body_search</td><td>String</td><td>Document textual search terms.</td></tr><tr><td>enable_body_search</td><td>Boolean</td><td>Flag to enable body search.</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>vector_store</td><td>Cassandra</td><td>The Cassandra vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <code>Data</code> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cassandra-graph">Cassandra Graph<a href="#cassandra-graph" class="hash-link" aria-label="Direct link to Cassandra Graph" title="Direct link to Cassandra Graph"></a></h2>
|
||
<p>This component implements a Cassandra Graph vector store with search capabilities.</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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>database_ref</td><td>Contact Points / Astra Database ID</td><td>The contact points for the database or Astra DB database ID. Required.</td></tr><tr><td>username</td><td>Username</td><td>The username for the database. Leave this field empty for Astra DB.</td></tr><tr><td>token</td><td>Password / Astra DB Token</td><td>The user password for the database or Astra DB token. Required.</td></tr><tr><td>keyspace</td><td>Keyspace</td><td>The table or keyspace. Required.</td></tr><tr><td>table_name</td><td>Table Name</td><td>The name of the table or Astra DB collection where vectors are stored. Required.</td></tr><tr><td>setup_mode</td><td>Setup Mode</td><td>The configuration mode for setting up the Cassandra table. The options are "Sync" or "Off". Default: "Sync".</td></tr><tr><td>cluster_kwargs</td><td>Cluster arguments</td><td>An optional dictionary of additional keyword arguments for the Cassandra cluster.</td></tr><tr><td>search_query</td><td>Search Query</td><td>The query string for similarity search.</td></tr><tr><td>ingest_data</td><td>Ingest Data</td><td>The list of data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embedding</td><td>The embedding model to use.</td></tr><tr><td>number_of_results</td><td>Number of Results</td><td>The number of results to return in similarity search. Default: 4.</td></tr><tr><td>search_type</td><td>Search Type</td><td>The search type to use. The options are "Traversal", "MMR traversal", "Similarity", "Similarity with score threshold", or "MMR (Max Marginal Relevance)". Default: "Traversal".</td></tr><tr><td>depth</td><td>Depth of traversal</td><td>The maximum depth of edges to traverse. Used for "Traversal" or "MMR traversal" search types. Default: 1.</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>The minimum similarity score threshold for search results. Used for "Similarity with score threshold" search types.</td></tr><tr><td>search_filter</td><td>Search Metadata Filter</td><td>An optional dictionary of filters to apply to the search query.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The Cassandra Graph vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="chroma-db">Chroma DB<a href="#chroma-db" class="hash-link" aria-label="Direct link to Chroma DB" title="Direct link to Chroma DB"></a></h2>
|
||
<p>The <strong>Chroma DB</strong> component creates an ephemeral, Chroma vector database with search capabilities that you can use for experimentation and vector storage.
|
||
For more information, see the <a href="https://docs.trychroma.com/" target="_blank" rel="noopener noreferrer">Chroma documentation</a>.</p>
|
||
<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Chroma DB sample flow</summary><div><div class="collapsibleContent_i85q"><ol>
|
||
<li>
|
||
<p>To use this component in a flow, connect it to a component that outputs <code>Data</code> or <code>DataFrame</code>.</p>
|
||
<p>This example splits text from a <a href="/components-data#url"><strong>URL</strong> component</a>, and then computes embeddings with the connected <strong>OpenAI Embeddings</strong> component. Chroma DB computes embeddings by default, but you can connect your own embeddings model, as seen in this example.</p>
|
||
<p><img decoding="async" loading="lazy" alt="ChromaDB receiving split text" src="/assets/images/component-chroma-db-b69a08e861be3451fe6f2992e203f516.png" width="4000" height="2694" class="img_ev3q"></p>
|
||
</li>
|
||
<li>
|
||
<p>In the <strong>Chroma DB</strong> component, in the <strong>Collection</strong> field, enter a name for your embeddings collection.</p>
|
||
</li>
|
||
<li>
|
||
<p>Optional: To persist the Chroma database, in the <strong>Persist</strong> field, enter a directory to store the <code>chroma.sqlite3</code> file.
|
||
This example uses <code>./chroma-db</code> to create a directory relative to where Langflow is running.</p>
|
||
</li>
|
||
<li>
|
||
<p>To load data and embeddings into your Chroma database, in the <strong>Chroma DB</strong> component, 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-play" aria-hidden="true"><polygon points="6 3 20 12 6 21 6 3"></polygon></svg> <strong>Run component</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>When loading duplicate documents, enable the <strong>Allow Duplicates</strong> option in Chroma DB if you want to store multiple copies of the same content, or disable it to automatically deduplicate your data.</p></div></div>
|
||
</li>
|
||
<li>
|
||
<p>To view the split data, in the <strong>Split Text</strong> component, 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-text-search" aria-hidden="true"><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>.</p>
|
||
</li>
|
||
<li>
|
||
<p>To query your loaded data, open the <strong>Playground</strong> and query your database.
|
||
Your input is converted to vector data and compared to the stored vectors in a vector similarity search.</p>
|
||
</li>
|
||
</ol></div></div></details>
|
||
<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>collection_name</td><td>String</td><td>The name of the Chroma collection. Default: "langflow".</td></tr><tr><td>persist_directory</td><td>String</td><td>The directory to persist the Chroma database.</td></tr><tr><td>search_query</td><td>String</td><td>The query to search for in the vector store.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to ingest into the vector store (list of <code>Data</code> objects).</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use for the vector store.</td></tr><tr><td>chroma_server_cors_allow_origins</td><td>String</td><td>The CORS allow origins for the Chroma server.</td></tr><tr><td>chroma_server_host</td><td>String</td><td>The host for the Chroma server.</td></tr><tr><td>chroma_server_http_port</td><td>Integer</td><td>The HTTP port for the Chroma server.</td></tr><tr><td>chroma_server_grpc_port</td><td>Integer</td><td>The gRPC port for the Chroma server.</td></tr><tr><td>chroma_server_ssl_enabled</td><td>Boolean</td><td>Enable SSL for the Chroma server.</td></tr><tr><td>allow_duplicates</td><td>Boolean</td><td>Allow duplicate documents in the vector store.</td></tr><tr><td>search_type</td><td>String</td><td>The type of search to perform: "Similarity" or "MMR".</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return from the search. Default: <code>10</code>.</td></tr><tr><td>limit</td><td>Integer</td><td>The limit of the number of records to compare when <code>Allow Duplicates</code> is false.</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>vector_store</td><td>Chroma</td><td>The Chroma vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="clickhouse">Clickhouse<a href="#clickhouse" class="hash-link" aria-label="Direct link to Clickhouse" title="Direct link to Clickhouse"></a></h2>
|
||
<p>This component implements a Clickhouse vector store with search capabilities.
|
||
For more information, see the <a href="https://clickhouse.com/docs/en/intro" target="_blank" rel="noopener noreferrer">Clickhouse Documentation</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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>host</td><td>hostname</td><td>The Clickhouse server hostname. Required. Default: "localhost".</td></tr><tr><td>port</td><td>port</td><td>The Clickhouse server port. Required. Default: 8123.</td></tr><tr><td>database</td><td>database</td><td>The Clickhouse database name. Required.</td></tr><tr><td>table</td><td>Table name</td><td>The Clickhouse table name. Required.</td></tr><tr><td>username</td><td>The ClickHouse user name.</td><td>Username for authentication. Required.</td></tr><tr><td>password</td><td>The password for username.</td><td>Password for authentication. Required.</td></tr><tr><td>index_type</td><td>index_type</td><td>Type of the index. The options are "annoy" and "vector_similarity". Default: "annoy".</td></tr><tr><td>metric</td><td>metric</td><td>Metric to compute distance. The options are "angular", "euclidean", "manhattan", "hamming", "dot". Default: "angular".</td></tr><tr><td>secure</td><td>Use https/TLS</td><td>Overrides inferred values from the interface or port arguments. Default: false.</td></tr><tr><td>index_param</td><td>Param of the index</td><td>Index parameters. Default: "'L2Distance',100".</td></tr><tr><td>index_query_params</td><td>index query params</td><td>Additional index query parameters.</td></tr><tr><td>search_query</td><td>Search Query</td><td>The query string for similarity search.</td></tr><tr><td>ingest_data</td><td>Ingest Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embedding</td><td>The embedding model to use.</td></tr><tr><td>number_of_results</td><td>Number of Results</td><td>The number of results to return in similarity search. Default: 4.</td></tr><tr><td>score_threshold</td><td>Score threshold</td><td>The threshold for similarity scores.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The Clickhouse vector store.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of Data objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="couchbase">Couchbase<a href="#couchbase" class="hash-link" aria-label="Direct link to Couchbase" title="Direct link to Couchbase"></a></h2>
|
||
<p>This component creates a Couchbase vector store with search capabilities.
|
||
For more information, see the <a href="https://docs.couchbase.com/home/index.html" target="_blank" rel="noopener noreferrer">Couchbase documentation</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>couchbase_connection_string</td><td>SecretString</td><td>Couchbase Cluster connection string. Required.</td></tr><tr><td>couchbase_username</td><td>String</td><td>Couchbase username. Required.</td></tr><tr><td>couchbase_password</td><td>SecretString</td><td>Couchbase password. Required.</td></tr><tr><td>bucket_name</td><td>String</td><td>Name of the Couchbase bucket. Required.</td></tr><tr><td>scope_name</td><td>String</td><td>Name of the Couchbase scope. Required.</td></tr><tr><td>collection_name</td><td>String</td><td>Name of the Couchbase collection. Required.</td></tr><tr><td>index_name</td><td>String</td><td>Name of the Couchbase index. Required.</td></tr><tr><td>search_query</td><td>String</td><td>The query to search for in the vector store.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The list of data to ingest into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use for the vector store.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return from the search. Default: 4.</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>vector_store</td><td>CouchbaseVectorStore</td><td>A Couchbase vector store instance configured with the specified parameters.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="elasticsearch">Elasticsearch<a href="#elasticsearch" class="hash-link" aria-label="Direct link to Elasticsearch" title="Direct link to Elasticsearch"></a></h2>
|
||
<p>This component creates an Elasticsearch vector store with search capabilities.
|
||
For more information, see the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html" target="_blank" rel="noopener noreferrer">Elasticsearch documentation</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>es_url</td><td>String</td><td>Elasticsearch server URL.</td></tr><tr><td>es_user</td><td>String</td><td>Username for Elasticsearch authentication.</td></tr><tr><td>es_password</td><td>SecretString</td><td>Password for Elasticsearch authentication.</td></tr><tr><td>index_name</td><td>String</td><td>Name of the Elasticsearch index.</td></tr><tr><td>strategy</td><td>String</td><td>Strategy for vector search. The options are "approximate_k_nearest_neighbors" or "script_scoring".</td></tr><tr><td>distance_strategy</td><td>String</td><td>Strategy for distance calculation. The options are "COSINE", "EUCLIDEAN_DISTANCE", or "DOT_PRODUCT".</td></tr><tr><td>search_query</td><td>String</td><td>Query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>Data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search. Default: <code>4</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>vector_store</td><td>ElasticsearchStore</td><td>The Elasticsearch vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="faiss">FAISS<a href="#faiss" class="hash-link" aria-label="Direct link to FAISS" title="Direct link to FAISS"></a></h2>
|
||
<p>This component creates a FAISS vector store with search capabilities.
|
||
For more information, see the <a href="https://faiss.ai/index.html" target="_blank" rel="noopener noreferrer">FAISS documentation</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>index_name</td><td>String</td><td>The name of the FAISS index. Default: "langflow_index".</td></tr><tr><td>persist_directory</td><td>String</td><td>Path to save the FAISS index. It is relative to where Langflow is running.</td></tr><tr><td>search_query</td><td>String</td><td>The query to search for in the vector store.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The list of data to ingest into the vector store.</td></tr><tr><td>allow_dangerous_deserialization</td><td>Boolean</td><td>Set to True to allow loading pickle files from untrusted sources. Default: True.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use for the vector store.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return from the search. Default: 4.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The FAISS vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="graph-rag">Graph RAG<a href="#graph-rag" class="hash-link" aria-label="Direct link to Graph RAG" title="Direct link to Graph RAG"></a></h2>
|
||
<p>This component performs Graph RAG traversal in a vector store, enabling graph-based document retrieval.
|
||
For more information, see the <a href="https://datastax.github.io/graph-rag/" target="_blank" rel="noopener noreferrer">Graph RAG documentation</a>.</p>
|
||
<p>For an example flow, see the <strong>Graph RAG</strong> template in Langflow.</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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>embedding_model</td><td>Embedding Model</td><td>Specify the embedding model. This isn't required for collections embedded with <a href="https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html" target="_blank" rel="noopener noreferrer">Astra vectorize</a>.</td></tr><tr><td>vector_store</td><td>Vector Store Connection</td><td>Connection to the vector store.</td></tr><tr><td>edge_definition</td><td>Edge Definition</td><td>Edge definition for the graph traversal. For more information, see the <a href="https://datastax.github.io/graph-rag/reference/graph_retriever/edges/" target="_blank" rel="noopener noreferrer">GraphRAG documentation</a>.</td></tr><tr><td>strategy</td><td>Traversal Strategies</td><td>The strategy to use for graph traversal. Strategy options are dynamically loaded from available strategies.</td></tr><tr><td>search_query</td><td>Search Query</td><td>The query to search for in the vector store.</td></tr><tr><td>graphrag_strategy_kwargs</td><td>Strategy Parameters</td><td>Optional dictionary of additional parameters for the retrieval strategy. For more information, see the <a href="https://datastax.github.io/graph-rag/reference/graph_retriever/strategies/" target="_blank" rel="noopener noreferrer">strategy documentation</a>.</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>search_results</td><td>List[Data]</td><td>Results of the graph-based document retrieval as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="hyper-converged-database-hcd">Hyper-Converged Database (HCD)<a href="#hyper-converged-database-hcd" class="hash-link" aria-label="Direct link to Hyper-Converged Database (HCD)" title="Direct link to Hyper-Converged Database (HCD)"></a></h2>
|
||
<p>This component implements a vector store using HCD.</p>
|
||
<p>To use the HCD vector store, add your deployment's collection name, username, password, and HCD Data API endpoint.
|
||
The endpoint must be formatted like <code>http[s]://**DOMAIN_NAME** or **IP_ADDRESS**[:port]</code>, for example, <code>http://192.0.2.250:8181</code>.</p>
|
||
<p>Replace <strong>DOMAIN_NAME</strong> or <strong>IP_ADDRESS</strong> with the domain name or IP address of your HCD Data API connection.</p>
|
||
<p>To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader.</p>
|
||
<p><img decoding="async" loading="lazy" alt="HCD vector store embeddings ingestion" src="/assets/images/component-hcd-example-flow-b82057600ce5e9e4a0f7ea0a61dcbf7f.png" width="2294" height="1684" class="img_ev3q"></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>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>collection_name</td><td>Collection Name</td><td>The name of the collection within HCD where the vectors will be stored. Required.</td></tr><tr><td>username</td><td>HCD Username</td><td>Authentication username for accessing HCD. Default is "hcd-superuser". Required.</td></tr><tr><td>password</td><td>HCD Password</td><td>Authentication password for accessing HCD. Required.</td></tr><tr><td>api_endpoint</td><td>HCD API Endpoint</td><td>API endpoint URL for the HCD service. Required.</td></tr><tr><td>search_input</td><td>Search Input</td><td>Query string for similarity search.</td></tr><tr><td>ingest_data</td><td>Ingest Data</td><td>Data to be ingested into the vector store.</td></tr><tr><td>namespace</td><td>Namespace</td><td>Optional namespace within HCD to use for the collection. Default is "default_namespace".</td></tr><tr><td>ca_certificate</td><td>CA Certificate</td><td>Optional CA certificate for TLS connections to HCD.</td></tr><tr><td>metric</td><td>Metric</td><td>Optional distance metric for vector comparisons. Options are "cosine", "dot_product", "euclidean".</td></tr><tr><td>batch_size</td><td>Batch Size</td><td>Optional number of data to process in a single batch.</td></tr><tr><td>bulk_insert_batch_concurrency</td><td>Bulk Insert Batch Concurrency</td><td>Optional concurrency level for bulk insert operations.</td></tr><tr><td>bulk_insert_overwrite_concurrency</td><td>Bulk Insert Overwrite Concurrency</td><td>Optional concurrency level for bulk insert operations that overwrite existing data.</td></tr><tr><td>bulk_delete_concurrency</td><td>Bulk Delete Concurrency</td><td>Optional concurrency level for bulk delete operations.</td></tr><tr><td>setup_mode</td><td>Setup Mode</td><td>Configuration mode for setting up the vector store. Options are "Sync", "Async", "Off". Default is "Sync".</td></tr><tr><td>pre_delete_collection</td><td>Pre Delete Collection</td><td>Boolean flag to determine whether to delete the collection before creating a new one.</td></tr><tr><td>metadata_indexing_include</td><td>Metadata Indexing Include</td><td>Optional list of metadata fields to include in the indexing.</td></tr><tr><td>embedding</td><td>Embedding or Astra Vectorize</td><td>Allows either an embedding model or an Astra Vectorize configuration.</td></tr><tr><td>metadata_indexing_exclude</td><td>Metadata Indexing Exclude</td><td>Optional list of metadata fields to exclude from the indexing.</td></tr><tr><td>collection_indexing_policy</td><td>Collection Indexing Policy</td><td>Optional dictionary defining the indexing policy for the collection.</td></tr><tr><td>number_of_results</td><td>Number of Results</td><td>Number of results to return in similarity search. Default is 4.</td></tr><tr><td>search_type</td><td>Search Type</td><td>Search type to use. Options are "Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)". Default is "Similarity".</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>Minimum similarity score threshold for search results. Default is 0.</td></tr><tr><td>search_filter</td><td>Search Metadata Filter</td><td>Optional dictionary of filters to apply to the search query.</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>vector_store</td><td>HyperConvergedDatabaseVectorStore</td><td>The HCD vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="local-db">Local DB<a href="#local-db" class="hash-link" aria-label="Direct link to Local DB" title="Direct link to Local DB"></a></h2>
|
||
<p>The <strong>Local DB</strong> component is Langflow's enhanced version of Chroma DB.</p>
|
||
<p>The component adds a user-friendly interface with two modes (Ingest and Retrieve), automatic collection management, and built-in persistence in Langflow's cache directory.</p>
|
||
<p>Local DB includes <strong>Ingest</strong> and <strong>Retrieve</strong> modes.</p>
|
||
<p>The <strong>Ingest</strong> mode works similarly to <a href="#chroma-db">ChromaDB</a>, and persists your database to the Langflow cache directory. The Langflow cache directory location is specified in the <code>LANGFLOW_CONFIG_DIR</code> environment variable. For more information, see <a href="/concepts-flows#flow-storage-and-logs">Flow storage and logs</a>.</p>
|
||
<p>The <strong>Retrieve</strong> mode can query your <strong>Chroma DB</strong> collections.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Local DB retrieving vectors" src="/assets/images/component-local-db-5ba34611640c3e325d9dd3cdcb591d1f.png" width="4000" height="2188" class="img_ev3q"></p>
|
||
<p>For more information, see the <a href="https://docs.trychroma.com/" target="_blank" rel="noopener noreferrer">Chroma documentation</a>.</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"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>collection_name</td><td>String</td><td>The name of the Chroma collection. Default: "langflow".</td></tr><tr><td>persist_directory</td><td>String</td><td>Custom base directory to save the vector store. Collections are stored under <code>$DIRECTORY/vector_stores/$COLLECTION_NAME</code>. If not specified, it uses your system's cache folder.</td></tr><tr><td>existing_collections</td><td>String</td><td>Select a previously created collection to search through its stored data.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use for the vector store.</td></tr><tr><td>allow_duplicates</td><td>Boolean</td><td>If false, the component won't add documents that are already in the vector store.</td></tr><tr><td>search_type</td><td>String</td><td>Type of search to perform: "Similarity" or "MMR".</td></tr><tr><td>ingest_data</td><td>Data/DataFrame</td><td>Data to store. It is embedded and indexed for semantic search.</td></tr><tr><td>search_query</td><td>String</td><td>Enter text to search for similar content in the selected collection.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return. Default: 10.</td></tr><tr><td>limit</td><td>Integer</td><td>Limit the number of records to compare when Allow Duplicates is False.</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>vector_store</td><td>Chroma</td><td>A local Chroma vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>List<a href="/data-types#data">Data</a></td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="milvus">Milvus<a href="#milvus" class="hash-link" aria-label="Direct link to Milvus" title="Direct link to Milvus"></a></h2>
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<p>This component creates a Milvus vector store with search capabilities.
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||
For more information, see the <a href="https://milvus.io/docs" target="_blank" rel="noopener noreferrer">Milvus documentation</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>collection_name</td><td>String</td><td>Name of the Milvus collection.</td></tr><tr><td>collection_description</td><td>String</td><td>Description of the Milvus collection.</td></tr><tr><td>uri</td><td>String</td><td>Connection URI for Milvus.</td></tr><tr><td>password</td><td>SecretString</td><td>Password for Milvus.</td></tr><tr><td>username</td><td>SecretString</td><td>Username for Milvus.</td></tr><tr><td>batch_size</td><td>Integer</td><td>Number of data to process in a single batch.</td></tr><tr><td>search_query</td><td>String</td><td>Query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>Data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search.</td></tr><tr><td>search_type</td><td>String</td><td>Type of search to perform.</td></tr><tr><td>search_score_threshold</td><td>Float</td><td>Minimum similarity score for search results.</td></tr><tr><td>search_filter</td><td>Dict</td><td>Metadata filters for search query.</td></tr><tr><td>setup_mode</td><td>String</td><td>Configuration mode for setting up the vector store.</td></tr><tr><td>vector_dimensions</td><td>Integer</td><td>Number of dimensions of the vectors.</td></tr><tr><td>pre_delete_collection</td><td>Boolean</td><td>Whether to delete the collection before creating a new one.</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>vector_store</td><td>Milvus</td><td>A Milvus vector store instance configured with the specified parameters.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="mongodb-atlas">MongoDB Atlas<a href="#mongodb-atlas" class="hash-link" aria-label="Direct link to MongoDB Atlas" title="Direct link to MongoDB Atlas"></a></h2>
|
||
<p>This component creates a MongoDB Atlas vector store with search capabilities.
|
||
For more information, see the <a href="https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/vector-search-quick-start/" target="_blank" rel="noopener noreferrer">MongoDB Atlas documentation</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>mongodb_atlas_cluster_uri</td><td>SecretString</td><td>The connection URI for your MongoDB Atlas cluster. Required.</td></tr><tr><td>enable_mtls</td><td>Boolean</td><td>Enable mutual TLS authentication. Default: false.</td></tr><tr><td>mongodb_atlas_client_cert</td><td>SecretString</td><td>Client certificate combined with private key for mTLS authentication. Required if mTLS is enabled.</td></tr><tr><td>db_name</td><td>String</td><td>The name of the database to use. Required.</td></tr><tr><td>collection_name</td><td>String</td><td>The name of the collection to use. Required.</td></tr><tr><td>index_name</td><td>String</td><td>The name of the Atlas Search index, it should be a Vector Search. Required.</td></tr><tr><td>insert_mode</td><td>String</td><td>How to insert new documents into the collection. The options are "append" or "overwrite". Default: "append".</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding model to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in similarity search. Default: 4.</td></tr><tr><td>index_field</td><td>String</td><td>The field to index. Default: "embedding".</td></tr><tr><td>filter_field</td><td>String</td><td>The field to filter the index.</td></tr><tr><td>number_dimensions</td><td>Integer</td><td>Embedding context length. Default: 1536.</td></tr><tr><td>similarity</td><td>String</td><td>The method used to measure similarity between vectors. The options are "cosine", "euclidean", or "dotProduct". Default: "cosine".</td></tr><tr><td>quantization</td><td>String</td><td>Quantization reduces memory costs by converting 32-bit floats to smaller data types. The options are "scalar" or "binary".</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>vector_store</td><td>MongoDBAtlasVectorSearch</td><td>The MongoDB Atlas vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="opensearch">OpenSearch<a href="#opensearch" class="hash-link" aria-label="Direct link to OpenSearch" title="Direct link to OpenSearch"></a></h2>
|
||
<p>This component creates an OpenSearch vector store with search capabilities
|
||
For more information, see <a href="https://opensearch.org/platform/search/vector-database.html" target="_blank" rel="noopener noreferrer">OpenSearch documentation</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>opensearch_url</td><td>String</td><td>URL for OpenSearch cluster, such as <code>https://192.168.1.1:9200</code>.</td></tr><tr><td>index_name</td><td>String</td><td>The index name where the vectors are stored in OpenSearch cluster.</td></tr><tr><td>search_input</td><td>String</td><td>Enter a search query. Leave empty to retrieve all documents or if hybrid search is being used.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>search_type</td><td>String</td><td>The options are "similarity", "similarity_score_threshold", "mmr".</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</td></tr><tr><td>search_score_threshold</td><td>Float</td><td>The minimum similarity score threshold for search results.</td></tr><tr><td>username</td><td>String</td><td>The username for the opensource cluster.</td></tr><tr><td>password</td><td>SecretString</td><td>The password for the opensource cluster.</td></tr><tr><td>use_ssl</td><td>Boolean</td><td>Use SSL.</td></tr><tr><td>verify_certs</td><td>Boolean</td><td>Verify certificates.</td></tr><tr><td>hybrid_search_query</td><td>String</td><td>Provide a custom hybrid search query in JSON format. This allows you to combine vector similarity and keyword matching.</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>vector_store</td><td>OpenSearchVectorSearch</td><td>OpenSearch vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="pgvector">PGVector<a href="#pgvector" class="hash-link" aria-label="Direct link to PGVector" title="Direct link to PGVector"></a></h2>
|
||
<p>This component creates a PGVector vector store with search capabilities.
|
||
For more information, see the <a href="https://github.com/pgvector/pgvector" target="_blank" rel="noopener noreferrer">PGVector documentation</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>pg_server_url</td><td>SecretString</td><td>The PostgreSQL server connection string.</td></tr><tr><td>collection_name</td><td>String</td><td>The table name for the vector store.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The PGVector vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="pinecone">Pinecone<a href="#pinecone" class="hash-link" aria-label="Direct link to Pinecone" title="Direct link to Pinecone"></a></h2>
|
||
<p>This component creates a Pinecone vector store with search capabilities.
|
||
For more information, see the <a href="https://docs.pinecone.io/home" target="_blank" rel="noopener noreferrer">Pinecone documentation</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>index_name</td><td>String</td><td>The name of the Pinecone index.</td></tr><tr><td>namespace</td><td>String</td><td>The namespace for the index.</td></tr><tr><td>distance_strategy</td><td>String</td><td>The strategy for calculating distance between vectors.</td></tr><tr><td>pinecone_api_key</td><td>SecretString</td><td>The API key for Pinecone.</td></tr><tr><td>text_key</td><td>String</td><td>The key in the record to use as text.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>vector_store</td><td>Vector Store</td><td>The Pinecone vector store instance configured with the specified parameters.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="qdrant">Qdrant<a href="#qdrant" class="hash-link" aria-label="Direct link to Qdrant" title="Direct link to Qdrant"></a></h2>
|
||
<p>This component creates a Qdrant vector store with search capabilities.
|
||
For more information, see the <a href="https://qdrant.tech/documentation/" target="_blank" rel="noopener noreferrer">Qdrant documentation</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>collection_name</td><td>String</td><td>The name of the Qdrant collection.</td></tr><tr><td>host</td><td>String</td><td>The Qdrant server host.</td></tr><tr><td>port</td><td>Integer</td><td>The Qdrant server port.</td></tr><tr><td>grpc_port</td><td>Integer</td><td>The Qdrant gRPC port.</td></tr><tr><td>api_key</td><td>SecretString</td><td>The API key for Qdrant.</td></tr><tr><td>prefix</td><td>String</td><td>The prefix for Qdrant.</td></tr><tr><td>timeout</td><td>Integer</td><td>The timeout for Qdrant operations.</td></tr><tr><td>path</td><td>String</td><td>The path for Qdrant.</td></tr><tr><td>url</td><td>String</td><td>The URL for Qdrant.</td></tr><tr><td>distance_func</td><td>String</td><td>The distance function for vector similarity.</td></tr><tr><td>content_payload_key</td><td>String</td><td>The content payload key.</td></tr><tr><td>metadata_payload_key</td><td>String</td><td>The metadata payload key.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</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>vector_store</td><td>Qdrant</td><td>A Qdrant vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="redis">Redis<a href="#redis" class="hash-link" aria-label="Direct link to Redis" title="Direct link to Redis"></a></h2>
|
||
<p>This component creates a Redis vector store with search capabilities.
|
||
For more information, see the <a href="https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/vectors/" target="_blank" rel="noopener noreferrer">Redis documentation</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>redis_server_url</td><td>SecretString</td><td>The Redis server connection string.</td></tr><tr><td>redis_index_name</td><td>String</td><td>The name of the Redis index.</td></tr><tr><td>code</td><td>String</td><td>The custom code for Redis (advanced).</td></tr><tr><td>schema</td><td>String</td><td>The schema for Redis index.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</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>vector_store</td><td>Redis</td><td>Redis vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="supabase">Supabase<a href="#supabase" class="hash-link" aria-label="Direct link to Supabase" title="Direct link to Supabase"></a></h2>
|
||
<p>This component creates a connection to a Supabase vector store with search capabilities.
|
||
For more information, see the <a href="https://supabase.com/docs/guides/ai" target="_blank" rel="noopener noreferrer">Supabase documentation</a>.</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"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>supabase_url</td><td>String</td><td>The URL of the Supabase instance.</td></tr><tr><td>supabase_service_key</td><td>SecretString</td><td>The service key for Supabase authentication.</td></tr><tr><td>table_name</td><td>String</td><td>The name of the table in Supabase.</td></tr><tr><td>query_name</td><td>String</td><td>The name of the query to use.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</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>vector_store</td><td>SupabaseVectorStore</td><td>A Supabase vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="upstash">Upstash<a href="#upstash" class="hash-link" aria-label="Direct link to Upstash" title="Direct link to Upstash"></a></h2>
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<p>This component creates an Upstash vector store with search capabilities.
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For more information, see the <a href="https://upstash.com/docs/introduction" target="_blank" rel="noopener noreferrer">Upstash documentation</a>.</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"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>index_url</td><td>String</td><td>The URL of the Upstash index.</td></tr><tr><td>index_token</td><td>SecretString</td><td>The token for the Upstash index.</td></tr><tr><td>text_key</td><td>String</td><td>The key in the record to use as text.</td></tr><tr><td>namespace</td><td>String</td><td>The namespace for the index.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>metadata_filter</td><td>String</td><td>Filter documents by metadata.</td></tr><tr><td>ingest_data</td><td>Data</td><td>The data to be ingested into the vector store.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</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>vector_store</td><td>UpstashVectorStore</td><td>An Upstash vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="vectara">Vectara<a href="#vectara" class="hash-link" aria-label="Direct link to Vectara" title="Direct link to Vectara"></a></h2>
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<p>This component creates a Vectara vector store with search capabilities.
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For more information, see the <a href="https://docs.vectara.com/docs/" target="_blank" rel="noopener noreferrer">Vectara documentation</a>.</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"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vectara_customer_id</td><td>String</td><td>The Vectara customer ID.</td></tr><tr><td>vectara_corpus_id</td><td>String</td><td>The Vectara corpus ID.</td></tr><tr><td>vectara_api_key</td><td>SecretString</td><td>The Vectara API key.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding function to use (optional).</td></tr><tr><td>ingest_data</td><td>List[Document/Data]</td><td>The data to be ingested into the vector store.</td></tr><tr><td>search_query</td><td>String</td><td>The query for similarity search.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>The number of results to return in search.</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>vector_store</td><td>VectaraVectorStore</td><td>Vectara vector store instance.</td></tr><tr><td>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/data-types#data">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="vectara-rag">Vectara RAG<a href="#vectara-rag" class="hash-link" aria-label="Direct link to Vectara RAG" title="Direct link to Vectara RAG"></a></h2>
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<p>This component enabled Vectara's full end-to-end RAG capabilities with reranking options.
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For more information, see the <a href="https://docs.vectara.com/docs/" target="_blank" rel="noopener noreferrer">Vectara documentation</a>.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="weaviate">Weaviate<a href="#weaviate" class="hash-link" aria-label="Direct link to Weaviate" title="Direct link to Weaviate"></a></h2>
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<p>This component facilitates a Weaviate vector store setup, optimizing text and document indexing and retrieval.
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For more information, see the <a href="https://weaviate.io/developers/weaviate" target="_blank" rel="noopener noreferrer">Weaviate Documentation</a>.</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"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>weaviate_url</td><td>String</td><td>The default instance URL.</td></tr><tr><td>search_by_text</td><td>Boolean</td><td>Indicates whether to search by text.</td></tr><tr><td>api_key</td><td>SecretString</td><td>The optional API key for authentication.</td></tr><tr><td>index_name</td><td>String</td><td>The optional index name.</td></tr><tr><td>text_key</td><td>String</td><td>The default text extraction key.</td></tr><tr><td>input</td><td>Document</td><td>The document or record.</td></tr><tr><td>embedding</td><td>Embeddings</td><td>The embedding model used.</td></tr><tr><td>attributes</td><td>List[String]</td><td>Optional additional attributes.</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>vector_store</td><td>WeaviateVectorStore</td><td>The Weaviate vector store instance.</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-data"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Data</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/components-processing"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Processing components</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-a-vector-store-component-in-a-flow" class="table-of-contents__link toc-highlight">Use a Vector Store component in a flow</a><ul><li><a href="#configure-vector-store-parameters" class="table-of-contents__link toc-highlight">Configure vector store parameters</a></li></ul></li><li><a href="#astra-db" class="table-of-contents__link toc-highlight">Astra DB</a><ul><li><a href="#generate-embeddings" class="table-of-contents__link toc-highlight">Generate embeddings</a></li><li><a href="#hybrid-search" class="table-of-contents__link toc-highlight">Hybrid search</a></li></ul></li><li><a href="#astra-db-graph" class="table-of-contents__link toc-highlight">Astra DB Graph</a></li><li><a href="#cassandra" class="table-of-contents__link toc-highlight">Cassandra</a></li><li><a href="#cassandra-graph" class="table-of-contents__link toc-highlight">Cassandra Graph</a></li><li><a href="#chroma-db" class="table-of-contents__link toc-highlight">Chroma DB</a></li><li><a href="#clickhouse" class="table-of-contents__link toc-highlight">Clickhouse</a></li><li><a href="#couchbase" class="table-of-contents__link toc-highlight">Couchbase</a></li><li><a href="#elasticsearch" class="table-of-contents__link toc-highlight">Elasticsearch</a></li><li><a href="#faiss" class="table-of-contents__link toc-highlight">FAISS</a></li><li><a href="#graph-rag" class="table-of-contents__link toc-highlight">Graph RAG</a></li><li><a href="#hyper-converged-database-hcd" class="table-of-contents__link toc-highlight">Hyper-Converged Database (HCD)</a></li><li><a href="#local-db" class="table-of-contents__link toc-highlight">Local DB</a></li><li><a href="#milvus" class="table-of-contents__link toc-highlight">Milvus</a></li><li><a href="#mongodb-atlas" class="table-of-contents__link toc-highlight">MongoDB Atlas</a></li><li><a href="#opensearch" class="table-of-contents__link toc-highlight">OpenSearch</a></li><li><a href="#pgvector" class="table-of-contents__link toc-highlight">PGVector</a></li><li><a href="#pinecone" class="table-of-contents__link toc-highlight">Pinecone</a></li><li><a href="#qdrant" class="table-of-contents__link toc-highlight">Qdrant</a></li><li><a href="#redis" class="table-of-contents__link toc-highlight">Redis</a></li><li><a href="#supabase" class="table-of-contents__link toc-highlight">Supabase</a></li><li><a href="#upstash" class="table-of-contents__link toc-highlight">Upstash</a></li><li><a href="#vectara" class="table-of-contents__link toc-highlight">Vectara</a></li><li><a href="#vectara-rag" class="table-of-contents__link toc-highlight">Vectara RAG</a></li><li><a href="#weaviate" class="table-of-contents__link toc-highlight">Weaviate</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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