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<header><h1>Vector store components in Langflow</h1></header>
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<p>Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation.</p>
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<p>Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data.</p>
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<p>Vector database components are distinct from <a href="/components-memories">memory components</a>, which are built specifically for storing and retrieving chat messages from external databases.</p>
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<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>
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<p>This example uses the <strong>Astra DB vector store</strong> component. Your vector store 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 Astra DB vector store generates embeddings with the connected <a href="/components-models">model</a> component, and stores them in the connected Astra DB database.</p>
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<p>This vector data can then be retrieved for workloads like Retrieval Augmented Generation.</p>
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<p><img decoding="async" loading="lazy" src="/assets/images/vector-store-retrieval-3201f1ea2f13a5f8a87652c81a5386c6.png" width="2734" height="1402" class="img_ev3q"></p>
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<p>The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search.
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The results are output from the vector database component as a <a href="/concepts-objects">Data</a> object and parsed into text.
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This text fills the <code>{context}</code> variable in the <strong>Prompt</strong> component, which informs the <strong>Open AI model</strong> component's responses.</p>
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<p>Alternatively, connect the vector database component's <strong>Retriever</strong> port to a <a href="/components-tools#retriever-tool">retriever tool</a>, and then to an <a href="/components-agents">agent</a> component. This enables the agent to use your vector database as a tool and make decisions based on the available data.</p>
|
||
<p><img decoding="async" loading="lazy" src="/assets/images/vector-store-agent-retrieval-tool-79066d11008a74c40390f55f7c73bd71.png" width="2414" height="1602" class="img_ev3q"></p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="astra-db-vector-store">Astra DB Vector Store<a href="#astra-db-vector-store" class="hash-link" aria-label="Direct link to Astra DB Vector Store" title="Direct link to Astra DB Vector Store"></a></h2>
|
||
<p>This component implements a Vector Store using Astra DB with search capabilities.</p>
|
||
<p>For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html" target="_blank" rel="noopener noreferrer">DataStax 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>token</td><td>Astra DB Application Token</td><td>The authentication token for accessing Astra DB.</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 database name for the Astra DB instance.</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 within Astra DB where the vectors are stored.</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.</td></tr><tr><td>embedding_model</td><td>Embedding Model</td><td>Specify the embedding model. Not required for Astra vectorize collections.</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>A 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>A 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="/concepts-objects#data-object">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 Vector Store</strong> component offers two methods for generating embeddings.</p>
|
||
<ol>
|
||
<li>
|
||
<p><strong>Embedding Model</strong>: Use your own embedding model by connecting an <a href="/components-embedding-models">Embeddings</a> component 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.</p>
|
||
</li>
|
||
</ol>
|
||
<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>The embedding model selection is made when creating a new collection and cannot be changed later.</p></div></div>
|
||
<p>For an example of using the <strong>Astra DB Vector Store</strong> component with an embedding model, see the <a href="/vector-store-rag">Vector Store RAG starter project</a>.</p>
|
||
<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">Astra DB Serverless documentation</a>.</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 <strong>hybrid search</strong>, which is enabled by default.</p>
|
||
<p>The component fields related to hybrid search are <strong>Search Query</strong>, <strong>Lexical Terms</strong>, and <strong>Reranker</strong>.</p>
|
||
<ul>
|
||
<li><strong>Search Query</strong> finds results by vector similarity.</li>
|
||
<li><strong>Lexical Terms</strong> is a comma-separated string of keywords, like <code>features, data, attributes, characteristics</code>.</li>
|
||
<li><strong>Reranker</strong> is the re-ranker model used in the hybrid search.
|
||
The re-ranker model is <code>nvidia/llama-3.2-nv.reranker</code>.</li>
|
||
</ul>
|
||
<p><a href="https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html" target="_blank" rel="noopener noreferrer">Hybrid search</a> performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.</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>To use hybrid search, your collection must be created with vector, lexical, and rerank capabilities enabled. These capabilities are enabled by default when you create a collection in a database in the AWS us-east-2 region.
|
||
For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid" target="_blank" rel="noopener noreferrer">DataStax documentation</a>.</p></div></div>
|
||
<p>To use <strong>Hybrid search</strong> in the <strong>Astra DB</strong> component, do the following:</p>
|
||
<ol>
|
||
<li>Click <strong>New Flow</strong> > <strong>RAG</strong> > <strong>Hybrid Search RAG</strong>.</li>
|
||
<li>In the <strong>OpenAI</strong> model component, add your <strong>OpenAI API key</strong>.</li>
|
||
<li>In the <strong>Astra DB</strong> vector store component, add your <strong>Astra DB Application Token</strong>.</li>
|
||
<li>In the <strong>Database</strong> field, select your database.</li>
|
||
<li>In the <strong>Collection</strong> field, select or create a collection with hybrid search capabilities enabled.</li>
|
||
<li>In the <strong>Playground</strong>, enter a question about your data, such as <code>What are the features of my data?</code>
|
||
Your query is sent to two components: an <strong>OpenAI</strong> model component and the <strong>Astra DB</strong> vector database component.
|
||
The <strong>OpenAI</strong> component contains a prompt for creating the lexical query from your input:</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>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>
|
||
<ol start="7">
|
||
<li>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-label="Inspect icon"><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>.</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>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>
|
||
<ol start="8">
|
||
<li>
|
||
<p>To view the <a href="/concepts-objects#dataframe-object">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-label="Inspect icon"><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>.
|
||
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 Astra DB port is connected to the <strong>Chat Input</strong> component from step 6.
|
||
This <strong>Search Query</strong> 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.</p>
|
||
<p>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>
|
||
<p>For more information, see the <a href="https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html" target="_blank" rel="noopener noreferrer">DataStax documentation</a>.</p>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="astradb-graph-vector-store">AstraDB Graph vector store<a href="#astradb-graph-vector-store" class="hash-link" aria-label="Direct link to AstraDB Graph vector store" title="Direct link to AstraDB Graph vector store"></a></h2>
|
||
<p>This component implements a Vector Store using AstraDB 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 AstraDB where the vectors are stored. Required.</td></tr><tr><td>token</td><td>Astra DB Application Token</td><td>Authentication token for accessing AstraDB. Required.</td></tr><tr><td>api_endpoint</td><td>API Endpoint</td><td>API endpoint URL for the AstraDB 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 AstraDB 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="/concepts-objects#data-object">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 AstraDB database ID.</td></tr><tr><td>username</td><td>String</td><td>Username for the database (leave empty for AstraDB).</td></tr><tr><td>token</td><td>SecretString</td><td>User password for the database or AstraDB token.</td></tr><tr><td>keyspace</td><td>String</td><td>Table Keyspace or AstraDB namespace.</td></tr><tr><td>table_name</td><td>String</td><td>Name of the table or AstraDB 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-vector-store">Cassandra Graph Vector Store<a href="#cassandra-graph-vector-store" class="hash-link" aria-label="Direct link to Cassandra Graph Vector Store" title="Direct link to Cassandra Graph Vector Store"></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 AstraDB database ID. Required.</td></tr><tr><td>username</td><td>Username</td><td>The username for the database. Leave this field empty for AstraDB.</td></tr><tr><td>token</td><td>Password / AstraDB Token</td><td>The user password for the database or AstraDB token. Required.</td></tr><tr><td>keyspace</td><td>Keyspace</td><td>The table Keyspace or AstraDB namespace. Required.</td></tr><tr><td>table_name</td><td>Table Name</td><td>The name of the table or AstraDB 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="/concepts-objects#data-object">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>This component creates a Chroma Vector Store with search capabilities.</p>
|
||
<p>The Chroma DB component creates an ephemeral vector database for experimentation and vector storage.</p>
|
||
<ol>
|
||
<li>To use this component in a flow, connect it to a component that outputs <strong>Data</strong> or <strong>DataFrame</strong>.
|
||
This example splits text from a <a href="/components-data#url">URL</a> component, and 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.</li>
|
||
</ol>
|
||
<p><img decoding="async" loading="lazy" alt="ChromaDB receiving split text" src="/assets/images/component-chroma-db-4cd884c7b7fd4b4be27cda1aec03790b.png" width="1127" height="811" class="img_ev3q"></p>
|
||
<ol start="2">
|
||
<li>In the <strong>Chroma DB</strong> component, in the <strong>Collection</strong> field, enter a name for your embeddings collection.</li>
|
||
<li>Optionally, 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.</li>
|
||
<li>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-label="Play icon"><polygon points="6 3 20 12 6 21 6 3"></polygon></svg>.</li>
|
||
</ol>
|
||
<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>
|
||
<ol start="5">
|
||
<li>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-label="Inspect icon"><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>.</li>
|
||
<li>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.</li>
|
||
</ol>
|
||
<p>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>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 <code>False</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>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="/concepts-objects#data-object">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="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 <code>LANGFLOW_CONFIG_DIR</code>. For more information, see <a href="/environment-variables">Environment variables</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-465d080ccffb1c4db4e137eff9047ff5.png" width="1716" height="960" 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>
|
||
<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 will use 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, will not 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="/concepts-objects#data-object">Data</a></td><td>The results of the similarity search as a list of <a href="/concepts-objects#data-object">Data</a> objects.</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="/concepts-objects#data-object">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="/concepts-objects#data-object">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 (Retrieval Augmented Generation) 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.</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 is not 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="/concepts-objects#data-object">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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<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>
|
||
<p>This component creates a Milvus Vector Store with search capabilities.
|
||
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>
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<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.
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||
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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
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<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>
|
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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>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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
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||
<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.
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||
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="/concepts-objects#data-object">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="/concepts-objects#data-object">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="/concepts-objects#data-object">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="/concepts-objects#data-object">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>
|
||
<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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<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>
|
||
<p>This component creates an Upstash Vector Store with search capabilities.
|
||
For more information, see the <a href="https://upstash.com/docs/introduction" target="_blank" rel="noopener noreferrer">Upstash 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_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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<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>
|
||
<p>This component creates a Vectara Vector Store with search capabilities.
|
||
For more information, see the <a href="https://docs.vectara.com/docs/" target="_blank" rel="noopener noreferrer">Vectara 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>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="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="vectara-search">Vectara Search<a href="#vectara-search" class="hash-link" aria-label="Direct link to Vectara Search" title="Direct link to Vectara Search"></a></h2>
|
||
<p>This component searches a Vectara Vector Store for documents based on the provided input.
|
||
For more information, see the <a href="https://docs.vectara.com/docs/" target="_blank" rel="noopener noreferrer">Vectara 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>search_type</td><td>String</td><td>The type of search, such as "Similarity" or "MMR".</td></tr><tr><td>input_value</td><td>String</td><td>The search query.</td></tr><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>files_url</td><td>List[String]</td><td>Optional URLs for file initialization.</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>The results of the similarity search as a list of <a href="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details>
|
||
<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>
|
||
<p>This component facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval.
|
||
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>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="weaviate-search">Weaviate Search<a href="#weaviate-search" class="hash-link" aria-label="Direct link to Weaviate Search" title="Direct link to Weaviate Search"></a></h2>
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<p>This component searches a Weaviate Vector Store for documents similar to the input.
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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>search_type</td><td>String</td><td>The type of search, such as "Similarity" or "MMR"</td></tr><tr><td>input_value</td><td>String</td><td>The search query.</td></tr><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>A boolean value that 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>embedding</td><td>Embeddings</td><td>The embeddings 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>search_results</td><td>List[Data]</td><td>The results of the similarity search as a list of <a href="/concepts-objects#data-object">Data</a> objects.</td></tr></tbody></table></div></div></details></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/components-tools"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Tools</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/agents"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Use Langflow Agents</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar 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