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<p>Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation.</p>
<p>Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data.</p>
<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>
<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>
<p>This example uses the <strong>Astra DB vector store</strong> component. Your vector store component&#x27;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>
<p>This vector data can then be retrieved for workloads like Retrieval Augmented Generation.</p>
<p><img decoding="async" loading="lazy" src="/assets/images/vector-store-retrieval-452b8316c734a28f85fcddf9ff7a32e4.png" width="2898" height="1110" class="img_ev3q"></p>
<p>The user&#x27;s chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search.
The results are output from the vector database component as a <a href="/concepts-objects">Data</a> object and parsed into text.
This text fills the <code>{context}</code> variable in the <strong>Prompt</strong> component, which informs the <strong>Open AI model</strong> component&#x27;s responses.</p>
<p>Alternatively, connect the vector database component&#x27;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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs">Inputs<a href="#inputs" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs">Outputs<a href="#outputs" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>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>
<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&#x27;s built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA&#x27;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="/starter-projects-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>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-1">Inputs<a href="#inputs-1" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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 will be 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 (options: &quot;cosine&quot;, &quot;euclidean&quot;, &quot;dot_product&quot;)</td></tr><tr><td>setup_mode</td><td>Setup Mode</td><td>Configuration mode for setting up the vector store (options: &quot;Sync&quot;, &quot;Async&quot;, &quot;Off&quot;)</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 (options: &quot;Similarity&quot;, &quot;Graph Traversal&quot;, &quot;Hybrid&quot;)</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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-1">Outputs<a href="#outputs-1" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>Astra DB 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 <code>Data</code> objects.</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-2">Inputs<a href="#inputs-2" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-2">Outputs<a href="#outputs-2" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>Cassandra</td><td>A 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>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-3">Inputs<a href="#inputs-3" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>Contact points for the database or AstraDB database ID (required)</td></tr><tr><td>username</td><td>Username</td><td>Username for the database (leave empty for AstraDB)</td></tr><tr><td>token</td><td>Password / AstraDB Token</td><td>User password for the database or AstraDB token (required)</td></tr><tr><td>keyspace</td><td>Keyspace</td><td>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 will be stored (required)</td></tr><tr><td>setup_mode</td><td>Setup Mode</td><td>Configuration mode for setting up the Cassandra table (options: &quot;Sync&quot;, &quot;Off&quot;, default: &quot;Sync&quot;)</td></tr><tr><td>cluster_kwargs</td><td>Cluster arguments</td><td>Optional dictionary of additional keyword arguments for the Cassandra cluster</td></tr><tr><td>search_query</td><td>Search Query</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 (list of Data objects)</td></tr><tr><td>embedding</td><td>Embedding</td><td>Embedding model to use</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 (options: &quot;Traversal&quot;, &quot;MMR traversal&quot;, &quot;Similarity&quot;, &quot;Similarity with score threshold&quot;, &quot;MMR (Max Marginal Relevance)&quot;, default: &quot;Traversal&quot;)</td></tr><tr><td>depth</td><td>Depth of traversal</td><td>The maximum depth of edges to traverse (for &quot;Traversal&quot; or &quot;MMR traversal&quot; search types, default: 1)</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>Minimum similarity score threshold for search results (for &quot;Similarity with score threshold&quot; search type)</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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-3">Outputs<a href="#outputs-3" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>A 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 <code>Data</code> objects.</td></tr></tbody></table>
<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.
For more information, see the <a href="https://docs.trychroma.com/" target="_blank" rel="noopener noreferrer">Chroma documentation</a>.</p>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-4">Inputs<a href="#inputs-4" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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: &quot;langflow&quot;.</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 Data 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>CORS allow origins for the Chroma server.</td></tr><tr><td>chroma_server_host</td><td>String</td><td>Host for the Chroma server.</td></tr><tr><td>chroma_server_http_port</td><td>Integer</td><td>HTTP port for the Chroma server.</td></tr><tr><td>chroma_server_grpc_port</td><td>Integer</td><td>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>Type of search to perform: &quot;Similarity&quot; or &quot;MMR&quot;.</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return from the search. 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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-4">Outputs<a href="#outputs-4" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>Chroma</td><td>Chroma vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-5">Inputs<a href="#inputs-5" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Display Name</th><th>Info</th></tr></thead><tbody><tr><td>host</td><td>hostname</td><td>Clickhouse server hostname (required, default: &quot;localhost&quot;)</td></tr><tr><td>port</td><td>port</td><td>Clickhouse server port (required, default: 8123)</td></tr><tr><td>database</td><td>database</td><td>Clickhouse database name (required)</td></tr><tr><td>table</td><td>Table name</td><td>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 (options: &quot;annoy&quot;, &quot;vector_similarity&quot;, default: &quot;annoy&quot;)</td></tr><tr><td>metric</td><td>metric</td><td>Metric to compute distance (options: &quot;angular&quot;, &quot;euclidean&quot;, &quot;manhattan&quot;, &quot;hamming&quot;, &quot;dot&quot;, default: &quot;angular&quot;)</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: &quot;&#x27;L2Distance&#x27;,100&quot;)</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>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>embedding</td><td>Embedding</td><td>Embedding model to use</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>score_threshold</td><td>Score threshold</td><td>Threshold for similarity scores</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-5">Outputs<a href="#outputs-5" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>Built Clickhouse vector store</td></tr><tr><td>search_results</td><td>Search Results</td><td>Results of the similarity search as a list of Data objects</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-6">Inputs<a href="#inputs-6" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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 data to ingest into the vector store (list of Data objects).</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 (advanced).</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-6">Outputs<a href="#outputs-6" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-7">Inputs<a href="#inputs-7" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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 (&quot;approximate_k_nearest_neighbors&quot; or &quot;script_scoring&quot;)</td></tr><tr><td>distance_strategy</td><td>String</td><td>Strategy for distance calculation (&quot;COSINE&quot;, &quot;EUCLIDEAN_DISTANCE&quot;, &quot;DOT_PRODUCT&quot;)</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: 4)</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-7">Outputs<a href="#outputs-7" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>ElasticsearchStore</td><td>Elasticsearch vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-8">Inputs<a href="#inputs-8" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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: &quot;langflow_index&quot;.</td></tr><tr><td>persist_directory</td><td>String</td><td>Path to save the FAISS index. It will be 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 data to ingest into the vector store (list of Data objects or documents).</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 (advanced).</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 (advanced).</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-8">Outputs<a href="#outputs-8" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>FAISS</td><td>A FAISS vector store instance configured with the specified parameters.</td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="hyper-converged-database-hcd-vector-store">Hyper-Converged Database (HCD) Vector Store<a href="#hyper-converged-database-hcd-vector-store" class="hash-link" aria-label="Direct link to Hyper-Converged Database (HCD) Vector Store" title="Direct link to Hyper-Converged Database (HCD) Vector Store">​</a></h2>
<p>This component implements a Vector Store using HCD.</p>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-9">Inputs<a href="#inputs-9" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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: &quot;hcd-superuser&quot;, 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: &quot;default_namespace&quot;)</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: &quot;cosine&quot;, &quot;dot_product&quot;, &quot;euclidean&quot;)</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: &quot;Sync&quot;, &quot;Async&quot;, &quot;Off&quot;, default: &quot;Sync&quot;)</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: 4)</td></tr><tr><td>search_type</td><td>Search Type</td><td>Search type to use (options: &quot;Similarity&quot;, &quot;Similarity with score threshold&quot;, &quot;MMR (Max Marginal Relevance)&quot;, default: &quot;Similarity&quot;)</td></tr><tr><td>search_score_threshold</td><td>Search Score Threshold</td><td>Minimum similarity score threshold for search results (default: 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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-9">Outputs<a href="#outputs-9" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>An HCD vector store instance The results of the similarity search as a list of <code>Data</code> objects.</td></tr><tr><td>search_results</td><td>Search Results</td><td>The results of the similarity search as a list of <code>Data</code> objects.</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-10">Inputs<a href="#inputs-10" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-10">Outputs<a href="#outputs-10" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-11">Inputs<a href="#inputs-11" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>MongoDB Atlas Cluster URI</td></tr><tr><td>db_name</td><td>String</td><td>Database name</td></tr><tr><td>collection_name</td><td>String</td><td>Collection name</td></tr><tr><td>index_name</td><td>String</td><td>Index name</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></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-11">Outputs<a href="#outputs-11" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>MongoDBAtlasVectorSearch</td><td>MongoDB Atlas vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-12">Inputs<a href="#inputs-12" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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 (e.g. <a href="https://192.168.1.1:9200" target="_blank" rel="noopener noreferrer">https://192.168.1.1:9200</a>)</td></tr><tr><td>index_name</td><td>String</td><td>The index name where the vectors will be 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>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>search_type</td><td>String</td><td>Valid values are &quot;similarity&quot;, &quot;similarity_score_threshold&quot;, &quot;mmr&quot;</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search</td></tr><tr><td>search_score_threshold</td><td>Float</td><td>Minimum similarity score threshold for search results</td></tr><tr><td>username</td><td>String</td><td>username for the opensource cluster</td></tr><tr><td>password</td><td>SecretString</td><td>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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-12">Outputs<a href="#outputs-12" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-13">Inputs<a href="#inputs-13" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>PostgreSQL server connection string</td></tr><tr><td>collection_name</td><td>String</td><td>Table name for the vector store</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></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-13">Outputs<a href="#outputs-13" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>PGVector</td><td>PGVector vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-14">Inputs<a href="#inputs-14" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>index_name</td><td>String</td><td>Name of the Pinecone index</td></tr><tr><td>namespace</td><td>String</td><td>Namespace for the index</td></tr><tr><td>distance_strategy</td><td>String</td><td>Strategy for calculating distance between vectors</td></tr><tr><td>pinecone_api_key</td><td>SecretString</td><td>API key for Pinecone</td></tr><tr><td>text_key</td><td>String</td><td>Key in the record to use as text</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></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-14">Outputs<a href="#outputs-14" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>Pinecone</td><td>Pinecone vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-15">Inputs<a href="#inputs-15" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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 Qdrant collection</td></tr><tr><td>host</td><td>String</td><td>Qdrant server host</td></tr><tr><td>port</td><td>Integer</td><td>Qdrant server port</td></tr><tr><td>grpc_port</td><td>Integer</td><td>Qdrant gRPC port</td></tr><tr><td>api_key</td><td>SecretString</td><td>API key for Qdrant</td></tr><tr><td>prefix</td><td>String</td><td>Prefix for Qdrant</td></tr><tr><td>timeout</td><td>Integer</td><td>Timeout for Qdrant operations</td></tr><tr><td>path</td><td>String</td><td>Path for Qdrant</td></tr><tr><td>url</td><td>String</td><td>URL for Qdrant</td></tr><tr><td>distance_func</td><td>String</td><td>Distance function for vector similarity</td></tr><tr><td>content_payload_key</td><td>String</td><td>Key for content payload</td></tr><tr><td>metadata_payload_key</td><td>String</td><td>Key for metadata payload</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></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-15">Outputs<a href="#outputs-15" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>Qdrant</td><td>Qdrant vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-16">Inputs<a href="#inputs-16" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>Redis server connection string</td></tr><tr><td>redis_index_name</td><td>String</td><td>Name of the Redis index</td></tr><tr><td>code</td><td>String</td><td>Custom code for Redis (advanced)</td></tr><tr><td>schema</td><td>String</td><td>Schema for Redis index</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>number_of_results</td><td>Integer</td><td>Number of results to return in search</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Embedding function to use</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-16">Outputs<a href="#outputs-16" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-17">Inputs<a href="#inputs-17" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>supabase_url</td><td>String</td><td>URL of the Supabase instance</td></tr><tr><td>supabase_service_key</td><td>SecretString</td><td>Service key for Supabase authentication</td></tr><tr><td>table_name</td><td>String</td><td>Name of the table in Supabase</td></tr><tr><td>query_name</td><td>String</td><td>Name of the query to use</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></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-17">Outputs<a href="#outputs-17" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>SupabaseVectorStore</td><td>Supabase vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-18">Inputs<a href="#inputs-18" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>Namespace for the index</td></tr><tr><td>search_query</td><td>String</td><td>Query for similarity search</td></tr><tr><td>metadata_filter</td><td>String</td><td>Filters documents by metadata</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 (optional)</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-18">Outputs<a href="#outputs-18" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>UpstashVectorStore</td><td>Upstash vector store instance</td></tr><tr><td>search_results</td><td>List[Data]</td><td>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-19">Inputs<a href="#inputs-19" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>Vectara customer ID</td></tr><tr><td>vectara_corpus_id</td><td>String</td><td>Vectara corpus ID</td></tr><tr><td>vectara_api_key</td><td>SecretString</td><td>Vectara API key</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Embedding function to use (optional)</td></tr><tr><td>ingest_data</td><td>List[Document/Data]</td><td>Data to be ingested into the vector store</td></tr><tr><td>search_query</td><td>String</td><td>Query for similarity search</td></tr><tr><td>number_of_results</td><td>Integer</td><td>Number of results to return in search</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-19">Outputs<a href="#outputs-19" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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>Results of similarity search</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-20">Inputs<a href="#inputs-20" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>search_type</td><td>String</td><td>Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;</td></tr><tr><td>input_value</td><td>String</td><td>Search query</td></tr><tr><td>vectara_customer_id</td><td>String</td><td>Vectara customer ID</td></tr><tr><td>vectara_corpus_id</td><td>String</td><td>Vectara corpus ID</td></tr><tr><td>vectara_api_key</td><td>SecretString</td><td>Vectara API key</td></tr><tr><td>files_url</td><td>List[String]</td><td>Optional URLs for file initialization</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-20">Outputs<a href="#outputs-20" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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 similarity search</td></tr></tbody></table>
<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>
<p>This component leverages Vectara&#x27;s Retrieval Augmented Generation (RAG) capabilities to search and summarize 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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-21">Inputs<a href="#inputs-21" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<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>Vectara customer ID</td></tr><tr><td>vectara_corpus_id</td><td>String</td><td>Vectara corpus ID</td></tr><tr><td>vectara_api_key</td><td>SecretString</td><td>Vectara API key</td></tr><tr><td>search_query</td><td>String</td><td>The query to receive an answer on</td></tr><tr><td>lexical_interpolation</td><td>Float</td><td>Hybrid search factor (0.005 to 0.1)</td></tr><tr><td>filter</td><td>String</td><td>Metadata filters to narrow the search</td></tr><tr><td>reranker</td><td>String</td><td>Reranker type (mmr, rerank_multilingual_v1, none)</td></tr><tr><td>reranker_k</td><td>Integer</td><td>Number of results to rerank (1 to 100)</td></tr><tr><td>diversity_bias</td><td>Float</td><td>Diversity bias for MMR reranker (0 to 1)</td></tr><tr><td>max_results</td><td>Integer</td><td>Maximum number of search results to summarize (1 to 100)</td></tr><tr><td>response_lang</td><td>String</td><td>Language code for the response (for example, &quot;eng&quot;, &quot;auto&quot;)</td></tr><tr><td>prompt</td><td>String</td><td>Prompt name for summarization</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-21">Outputs<a href="#outputs-21" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>answer</td><td>Message</td><td>Generated RAG response</td></tr></tbody></table>
<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>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-22">Inputs<a href="#inputs-22" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>weaviate_url</td><td>String</td><td>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>Optional API key for authentication</td></tr><tr><td>index_name</td><td>String</td><td>Optional index name</td></tr><tr><td>text_key</td><td>String</td><td>Default text extraction key</td></tr><tr><td>input</td><td>Document</td><td>Document or record</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Model used</td></tr><tr><td>attributes</td><td>List[String]</td><td>Optional additional attributes</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-22">Outputs<a href="#outputs-22" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>vector_store</td><td>WeaviateVectorStore</td><td>Weaviate vector store instance</td></tr></tbody></table>
<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>
<p>This component searches a Weaviate Vector Store for documents similar to the input.
For more information, see the <a href="https://weaviate.io/developers/weaviate" target="_blank" rel="noopener noreferrer">Weaviate Documentation</a>.</p>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-23">Inputs<a href="#inputs-23" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs">​</a></h3>
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>search_type</td><td>String</td><td>Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;</td></tr><tr><td>input_value</td><td>String</td><td>Search query</td></tr><tr><td>weaviate_url</td><td>String</td><td>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>Optional API key for authentication</td></tr><tr><td>index_name</td><td>String</td><td>Optional index name</td></tr><tr><td>text_key</td><td>String</td><td>Default text extraction key</td></tr><tr><td>embedding</td><td>Embeddings</td><td>Model used</td></tr><tr><td>attributes</td><td>List[String]</td><td>Optional additional attributes</td></tr></tbody></table>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-23">Outputs<a href="#outputs-23" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs">​</a></h3>
<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 similarity search</td></tr></tbody></table></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-overview"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Agents overview</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></li><li><a href="#astra-db-vector-store" class="table-of-contents__link toc-highlight">Astra DB Vector Store</a><ul><li><a href="#inputs" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs" class="table-of-contents__link toc-highlight">Outputs</a></li><li><a href="#generate-embeddings" class="table-of-contents__link toc-highlight">Generate embeddings</a></li></ul></li><li><a href="#astradb-graph-vector-store" class="table-of-contents__link toc-highlight">AstraDB Graph vector store</a><ul><li><a href="#inputs-1" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-1" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#cassandra" class="table-of-contents__link toc-highlight">Cassandra</a><ul><li><a href="#inputs-2" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-2" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#cassandra-graph-vector-store" class="table-of-contents__link toc-highlight">Cassandra Graph Vector Store</a><ul><li><a href="#inputs-3" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-3" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#chroma-db" class="table-of-contents__link toc-highlight">Chroma DB</a><ul><li><a href="#inputs-4" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-4" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#clickhouse" class="table-of-contents__link toc-highlight">Clickhouse</a><ul><li><a href="#inputs-5" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-5" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#couchbase" class="table-of-contents__link toc-highlight">Couchbase</a><ul><li><a href="#inputs-6" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-6" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#elasticsearch" class="table-of-contents__link toc-highlight">Elasticsearch</a><ul><li><a href="#inputs-7" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-7" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#faiss" class="table-of-contents__link toc-highlight">FAISS</a><ul><li><a href="#inputs-8" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-8" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#hyper-converged-database-hcd-vector-store" class="table-of-contents__link toc-highlight">Hyper-Converged Database (HCD) Vector Store</a><ul><li><a href="#inputs-9" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-9" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#milvus" class="table-of-contents__link toc-highlight">Milvus</a><ul><li><a href="#inputs-10" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-10" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#mongodb-atlas" class="table-of-contents__link toc-highlight">MongoDB Atlas</a><ul><li><a href="#inputs-11" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-11" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#opensearch" class="table-of-contents__link toc-highlight">Opensearch</a><ul><li><a href="#inputs-12" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-12" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#pgvector" class="table-of-contents__link toc-highlight">PGVector</a><ul><li><a href="#inputs-13" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-13" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#pinecone" class="table-of-contents__link toc-highlight">Pinecone</a><ul><li><a href="#inputs-14" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-14" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#qdrant" class="table-of-contents__link toc-highlight">Qdrant</a><ul><li><a href="#inputs-15" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-15" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#redis" class="table-of-contents__link toc-highlight">Redis</a><ul><li><a href="#inputs-16" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-16" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#supabase" class="table-of-contents__link toc-highlight">Supabase</a><ul><li><a href="#inputs-17" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-17" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#upstash" class="table-of-contents__link toc-highlight">Upstash</a><ul><li><a href="#inputs-18" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-18" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#vectara" class="table-of-contents__link toc-highlight">Vectara</a><ul><li><a href="#inputs-19" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-19" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#vectara-search" class="table-of-contents__link toc-highlight">Vectara Search</a><ul><li><a href="#inputs-20" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-20" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#vectara-rag" class="table-of-contents__link toc-highlight">Vectara RAG</a><ul><li><a href="#inputs-21" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-21" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#weaviate" class="table-of-contents__link toc-highlight">Weaviate</a><ul><li><a href="#inputs-22" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-22" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li><li><a href="#weaviate-search" class="table-of-contents__link toc-highlight">Weaviate Search</a><ul><li><a href="#inputs-23" class="table-of-contents__link toc-highlight">Inputs</a></li><li><a href="#outputs-23" class="table-of-contents__link toc-highlight">Outputs</a></li></ul></li></ul></div></div></div></div></main></div></div></div><div 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