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itemprop="name">Components</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Vector Stores</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Vector Stores</h1></header><h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="453bcf5664154e37a920f1b602bd39da">Astra DB<a class="hash-link" aria-label="Direct link to Astra DB" title="Direct link to Astra DB" href="/components-vector-stores#453bcf5664154e37a920f1b602bd39da">​</a></h3>
<p>The <code>Astra DB</code> initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Documents or Data for input.</li>
<li><strong>Embedding or Astra vectorize:</strong> External or server-side model Astra DB uses.</li>
<li><strong>Collection Name:</strong> Name of the Astra DB collection.</li>
<li><strong>Token:</strong> Authentication token for Astra DB.</li>
<li><strong>API Endpoint:</strong> API endpoint for Astra DB.</li>
<li><strong>Namespace:</strong> Astra DB namespace.</li>
<li><strong>Metric:</strong> Metric used by Astra DB.</li>
<li><strong>Batch Size:</strong> Batch size for operations.</li>
<li><strong>Bulk Insert Batch Concurrency:</strong> Concurrency level for bulk inserts.</li>
<li><strong>Bulk Insert Overwrite Concurrency:</strong> Concurrency level for overwriting during bulk inserts.</li>
<li><strong>Bulk Delete Concurrency:</strong> Concurrency level for bulk deletions.</li>
<li><strong>Setup Mode:</strong> Setup mode for the vector store.</li>
<li><strong>Pre Delete Collection:</strong> Option to delete the collection before setup.</li>
<li><strong>Metadata Indexing Include:</strong> Fields to include in metadata indexing.</li>
<li><strong>Metadata Indexing Exclude:</strong> Fields to exclude from metadata indexing.</li>
<li><strong>Collection Indexing Policy:</strong> Indexing policy for the collection.</li>
</ul>
<p>NOTE</p>
<p>Ensure you configure the necessary Astra DB token and API endpoint before starting.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="26f25d1933a9459bad2d6725f87beb11">Astra DB Search<a class="hash-link" aria-label="Direct link to Astra DB Search" title="Direct link to Astra DB Search" href="/components-vector-stores#26f25d1933a9459bad2d6725f87beb11">​</a></h3>
<p><code>Astra DBSearch</code> searches an existing Astra DB vector store for documents similar to the input. It uses the <code>Astra DB</code>component&#x27;s functionality for efficient retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as Similarity or MMR.</li>
<li><strong>Input Value:</strong> Value to search for.</li>
<li><strong>Embedding or Astra vectorize:</strong> External or server-side model Astra DB uses.</li>
<li><strong>Collection Name:</strong> Name of the Astra DB collection.</li>
<li><strong>Token:</strong> Authentication token for Astra DB.</li>
<li><strong>API Endpoint:</strong> API endpoint for Astra DB.</li>
<li><strong>Namespace:</strong> Astra DB namespace.</li>
<li><strong>Metric:</strong> Metric used by Astra DB.</li>
<li><strong>Batch Size:</strong> Batch size for operations.</li>
<li><strong>Bulk Insert Batch Concurrency:</strong> Concurrency level for bulk inserts.</li>
<li><strong>Bulk Insert Overwrite Concurrency:</strong> Concurrency level for overwriting during bulk inserts.</li>
<li><strong>Bulk Delete Concurrency:</strong> Concurrency level for bulk deletions.</li>
<li><strong>Setup Mode:</strong> Setup mode for the vector store.</li>
<li><strong>Pre Delete Collection:</strong> Option to delete the collection before setup.</li>
<li><strong>Metadata Indexing Include:</strong> Fields to include in metadata indexing.</li>
<li><strong>Metadata Indexing Exclude:</strong> Fields to exclude from metadata indexing.</li>
<li><strong>Collection Indexing Policy:</strong> Indexing policy for the collection.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="74730795605143cba53e1f4c4f2ef5d6">Chroma<a class="hash-link" aria-label="Direct link to Chroma" title="Direct link to Chroma" href="/components-vector-stores#74730795605143cba53e1f4c4f2ef5d6">​</a></h3>
<p><code>Chroma</code> sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Collection Name:</strong> Name of the collection.</li>
<li><strong>Persist Directory:</strong> Directory to persist the Vector Store.</li>
<li><strong>Server CORS Allow Origins (Optional):</strong> CORS allow origins for the Chroma server.</li>
<li><strong>Server Host (Optional):</strong> Host for the Chroma server.</li>
<li><strong>Server Port (Optional):</strong> Port for the Chroma server.</li>
<li><strong>Server gRPC Port (Optional):</strong> gRPC port for the Chroma server.</li>
<li><strong>Server SSL Enabled (Optional):</strong> SSL configuration for the Chroma server.</li>
<li><strong>Input:</strong> Input data for creating the Vector Store.</li>
<li><strong>Embedding:</strong> Embeddings used for the Vector Store.</li>
</ul>
<p>For detailed documentation and integration guides, please refer to the <a href="https://python.langchain.com/docs/integrations/vectorstores/chroma" target="_blank" rel="noopener noreferrer">Chroma Component Documentation</a>.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="5718072a155441f3a443b944ad4d638f">Chroma Search<a class="hash-link" aria-label="Direct link to Chroma Search" title="Direct link to Chroma Search" href="/components-vector-stores#5718072a155441f3a443b944ad4d638f">​</a></h3>
<p><code>ChromaSearch</code> searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Input text for search.</li>
<li><strong>Search Type:</strong> Type of search, such as Similarity or MMR.</li>
<li><strong>Collection Name:</strong> Name of the Chroma collection.</li>
<li><strong>Index Directory:</strong> Directory where the Chroma index is stored.</li>
<li><strong>Embedding:</strong> Embedding model used for vectorization.</li>
<li><strong>Server CORS Allow Origins (Optional):</strong> CORS allow origins for the Chroma server.</li>
<li><strong>Server Host (Optional):</strong> Host for the Chroma server.</li>
<li><strong>Server Port (Optional):</strong> Port for the Chroma server.</li>
<li><strong>Server gRPC Port (Optional):</strong> gRPC port for the Chroma server.</li>
<li><strong>Server SSL Enabled (Optional):</strong> SSL configuration for the Chroma server.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="6900a79347164f35af27ae27f0d64a6d">Couchbase<a class="hash-link" aria-label="Direct link to Couchbase" title="Direct link to Couchbase" href="/components-vector-stores#6900a79347164f35af27ae27f0d64a6d">​</a></h3>
<p><code>Couchbase</code> builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Embedding:</strong> Model used by Couchbase.</li>
<li><strong>Input:</strong> Documents or Data.</li>
<li><strong>Couchbase Cluster Connection String:</strong> Cluster Connection string.</li>
<li><strong>Couchbase Cluster Username:</strong> Cluster Username.</li>
<li><strong>Couchbase Cluster Password:</strong> Cluster Password.</li>
<li><strong>Bucket Name:</strong> Bucket identifier in Couchbase.</li>
<li><strong>Scope Name:</strong> Scope identifier in Couchbase.</li>
<li><strong>Collection Name:</strong> Collection identifier in Couchbase.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
</ul>
<p>For detailed documentation and integration guides, please refer to the <a href="https://python.langchain.com/docs/integrations/vectorstores/couchbase" target="_blank" rel="noopener noreferrer">Couchbase Component Documentation</a>.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="c77bb09425a3426f9677d38d8237d9ba">Couchbase Search<a class="hash-link" aria-label="Direct link to Couchbase Search" title="Direct link to Couchbase Search" href="/components-vector-stores#c77bb09425a3426f9677d38d8237d9ba">​</a></h3>
<p><code>CouchbaseSearch</code> leverages the Couchbase component to search for documents based on similarity metric.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used in the Vector Store.</li>
<li><strong>Couchbase Cluster Connection String:</strong> Cluster Connection string.</li>
<li><strong>Couchbase Cluster Username:</strong> Cluster Username.</li>
<li><strong>Couchbase Cluster Password:</strong> Cluster Password.</li>
<li><strong>Bucket Name:</strong> Bucket identifier.</li>
<li><strong>Scope Name:</strong> Scope identifier.</li>
<li><strong>Collection Name:</strong> Collection identifier in Couchbase.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="5b3f4e6592a847b69e07df2f674a03f0">FAISS<a class="hash-link" aria-label="Direct link to FAISS" title="Direct link to FAISS" href="/components-vector-stores#5b3f4e6592a847b69e07df2f674a03f0">​</a></h3>
<p>The <code>FAISS</code> component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Embedding:</strong> Model used for vectorizing inputs.</li>
<li><strong>Input:</strong> Documents to ingest.</li>
<li><strong>Folder Path:</strong> Save path for the FAISS index, relative to Langflow.</li>
</ul>
<p>For more details, see the <a href="https://faiss.ai/index.html" target="_blank" rel="noopener noreferrer">FAISS Component Documentation</a>.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="81ff12d7205940a3b14e3ddf304630f8">FAISS Search<a class="hash-link" aria-label="Direct link to FAISS Search" title="Direct link to FAISS Search" href="/components-vector-stores#81ff12d7205940a3b14e3ddf304630f8">​</a></h3>
<p><code>FAISSSearch</code> searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Embedding:</strong> Model used in the FAISS Vector Store.</li>
<li><strong>Folder Path:</strong> Path to load the FAISS index from, relative to Langflow.</li>
<li><strong>Input:</strong> Search query.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="eba8892f7a204b97ad1c353e82948149">MongoDB Atlas<a class="hash-link" aria-label="Direct link to MongoDB Atlas" title="Direct link to MongoDB Atlas" href="/components-vector-stores#eba8892f7a204b97ad1c353e82948149">​</a></h3>
<p><code>MongoDBAtlas</code> builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Embedding:</strong> Model used by MongoDB Atlas.</li>
<li><strong>Input:</strong> Documents or Data.</li>
<li><strong>Collection Name:</strong> Collection identifier in MongoDB Atlas.</li>
<li><strong>Database Name:</strong> Database identifier.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
<li><strong>MongoDB Atlas Cluster URI:</strong> Cluster URI.</li>
<li><strong>Search Kwargs:</strong> Additional search parameters.</li>
</ul>
<p>NOTE</p>
<p>Ensure pymongo is installed for using MongoDB Atlas Vector Store.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="686ba0e30a54438cbc7153b81ee4b1df">MongoDB Atlas Search<a class="hash-link" aria-label="Direct link to MongoDB Atlas Search" title="Direct link to MongoDB Atlas Search" href="/components-vector-stores#686ba0e30a54438cbc7153b81ee4b1df">​</a></h3>
<p><code>MongoDBAtlasSearch</code> leverages the MongoDBAtlas component to search for documents based on similarity metrics.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used in the Vector Store.</li>
<li><strong>Collection Name:</strong> Collection identifier.</li>
<li><strong>Database Name:</strong> Database identifier.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
<li><strong>MongoDB Atlas Cluster URI:</strong> Cluster URI.</li>
<li><strong>Search Kwargs:</strong> Additional search parameters.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="7ceebdd84ab14f8e8589c13c58370e5b">PGVector<a class="hash-link" aria-label="Direct link to PGVector" title="Direct link to PGVector" href="/components-vector-stores#7ceebdd84ab14f8e8589c13c58370e5b">​</a></h3>
<p><code>PGVector</code> integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Value for the Vector Store.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>PostgreSQL Server Connection String:</strong> Server URL.</li>
<li><strong>Table:</strong> Table name in the PostgreSQL database.</li>
</ul>
<p>For more details, see the <a href="https://python.langchain.com/docs/integrations/vectorstores/pgvector" target="_blank" rel="noopener noreferrer">PGVector Component Documentation</a>.</p>
<p>NOTE</p>
<p>Ensure the PostgreSQL server is accessible and configured correctly.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="196bf22ea2844bdbba971b5082750943">PGVector Search<a class="hash-link" aria-label="Direct link to PGVector Search" title="Direct link to PGVector Search" href="/components-vector-stores#196bf22ea2844bdbba971b5082750943">​</a></h3>
<p><code>PGVectorSearch</code> extends <code>PGVector</code> to search for documents based on similarity metrics.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>PostgreSQL Server Connection String:</strong> Server URL.</li>
<li><strong>Table:</strong> Table name.</li>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="67abbe3e27c34fb4bcb35926ce831727">Pinecone<a class="hash-link" aria-label="Direct link to Pinecone" title="Direct link to Pinecone" href="/components-vector-stores#67abbe3e27c34fb4bcb35926ce831727">​</a></h3>
<p><code>Pinecone</code> constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Documents or Data.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
<li><strong>Namespace:</strong> Namespace used.</li>
<li><strong>Pinecone API Key:</strong> API key.</li>
<li><strong>Pinecone Environment:</strong> Environment settings.</li>
<li><strong>Search Kwargs:</strong> Additional search parameters.</li>
<li><strong>Pool Threads:</strong> Number of threads.</li>
</ul>
<p>NOTE</p>
<p>Ensure the Pinecone API key and environment are correctly configured.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="977944558cad4cf2ba332ea4f06bf485">Pinecone Search<a class="hash-link" aria-label="Direct link to Pinecone Search" title="Direct link to Pinecone Search" href="/components-vector-stores#977944558cad4cf2ba332ea4f06bf485">​</a></h3>
<p><code>PineconeSearch</code> searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Index Name:</strong> Index identifier.</li>
<li><strong>Namespace:</strong> Namespace used.</li>
<li><strong>Pinecone API Key:</strong> API key.</li>
<li><strong>Pinecone Environment:</strong> Environment settings.</li>
<li><strong>Search Kwargs:</strong> Additional search parameters.</li>
<li><strong>Pool Threads:</strong> Number of threads.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="88df77f3044e4ac6980950835a919fb0">Qdrant<a class="hash-link" aria-label="Direct link to Qdrant" title="Direct link to Qdrant" href="/components-vector-stores#88df77f3044e4ac6980950835a919fb0">​</a></h3>
<p><code>Qdrant</code> allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Documents or Data.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>API Key:</strong> Qdrant API key.</li>
<li><strong>Collection Name:</strong> Collection identifier.</li>
<li><strong>Advanced Settings:</strong> Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="5ba5f8dca0f249d7ad00778f49901e6c">Qdrant Search<a class="hash-link" aria-label="Direct link to Qdrant Search" title="Direct link to Qdrant Search" href="/components-vector-stores#5ba5f8dca0f249d7ad00778f49901e6c">​</a></h3>
<p><code>QdrantSearch</code> extends <code>Qdrant</code> to search for documents similar to the input based on advanced similarity metrics.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>API Key:</strong> Qdrant API key.</li>
<li><strong>Collection Name:</strong> Collection identifier.</li>
<li><strong>Advanced Settings:</strong> Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="a0fb8a9d244a40eb8439d0f8c22a2562">Redis<a class="hash-link" aria-label="Direct link to Redis" title="Direct link to Redis" href="/components-vector-stores#a0fb8a9d244a40eb8439d0f8c22a2562">​</a></h3>
<p><code>Redis</code> manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Index Name:</strong> Default index name.</li>
<li><strong>Input:</strong> Data for building the Redis Vector Store.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Schema:</strong> Optional schema file (.yaml) for document structure.</li>
<li><strong>Redis Server Connection String:</strong> Server URL.</li>
<li><strong>Redis Index:</strong> Optional index name.</li>
</ul>
<p>For detailed documentation, refer to the <a href="https://python.langchain.com/docs/integrations/vectorstores/redis" target="_blank" rel="noopener noreferrer">Redis Documentation</a>.</p>
<p>NOTE</p>
<p>Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="80aea4da515f490e979c8576099ee880">Redis Search<a class="hash-link" aria-label="Direct link to Redis Search" title="Direct link to Redis Search" href="/components-vector-stores#80aea4da515f490e979c8576099ee880">​</a></h3>
<p><code>RedisSearch</code> searches a Redis Vector Store for documents similar to the input.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Index Name:</strong> Default index name.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Schema:</strong> Optional schema file (.yaml) for document structure.</li>
<li><strong>Redis Server Connection String:</strong> Server URL.</li>
<li><strong>Redis Index:</strong> Optional index name.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="e86fb3cc507e4b5494f0a421f94e853b">Supabase<a class="hash-link" aria-label="Direct link to Supabase" title="Direct link to Supabase" href="/components-vector-stores#e86fb3cc507e4b5494f0a421f94e853b">​</a></h3>
<p><code>Supabase</code> initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Input:</strong> Documents or data.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Query Name:</strong> Optional query name.</li>
<li><strong>Search Kwargs:</strong> Advanced search parameters.</li>
<li><strong>Supabase Service Key:</strong> Service key.</li>
<li><strong>Supabase URL:</strong> Instance URL.</li>
<li><strong>Table Name:</strong> Optional table name.</li>
</ul>
<p>NOTE</p>
<p>Ensure the Supabase service key, URL, and table name are properly configured.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="fd02d550b9b2457f91f2f4073656cb09">Supabase Search<a class="hash-link" aria-label="Direct link to Supabase Search" title="Direct link to Supabase Search" href="/components-vector-stores#fd02d550b9b2457f91f2f4073656cb09">​</a></h3>
<p><code>SupabaseSearch</code> searches a Supabase Vector Store for documents similar to the input.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Query Name:</strong> Optional query name.</li>
<li><strong>Search Kwargs:</strong> Advanced search parameters.</li>
<li><strong>Supabase Service Key:</strong> Service key.</li>
<li><strong>Supabase URL:</strong> Instance URL.</li>
<li><strong>Table Name:</strong> Optional table name.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="b4e05230b62a47c792a89c5511af97ac">Vectara<a class="hash-link" aria-label="Direct link to Vectara" title="Direct link to Vectara" href="/components-vector-stores#b4e05230b62a47c792a89c5511af97ac">​</a></h3>
<p><code>Vectara</code> sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Vectara Customer ID:</strong> Customer ID.</li>
<li><strong>Vectara Corpus ID:</strong> Corpus ID.</li>
<li><strong>Vectara API Key:</strong> API key.</li>
<li><strong>Files Url:</strong> Optional URLs for file initialization.</li>
<li><strong>Input:</strong> Optional data for corpus upsert.</li>
</ul>
<p>For more information, consult the <a href="https://python.langchain.com/docs/integrations/vectorstores/vectara" target="_blank" rel="noopener noreferrer">Vectara Component Documentation</a>.</p>
<p>NOTE</p>
<p>If inputs or files_url are provided, they will be processed accordingly.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="31a47221c23f4fbba4a7465cf1d89eb0">Vectara Search<a class="hash-link" aria-label="Direct link to Vectara Search" title="Direct link to Vectara Search" href="/components-vector-stores#31a47221c23f4fbba4a7465cf1d89eb0">​</a></h3>
<p><code>VectaraSearch</code> searches a Vectara Vector Store for documents based on the provided input.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Vectara Customer ID:</strong> Customer ID.</li>
<li><strong>Vectara Corpus ID:</strong> Corpus ID.</li>
<li><strong>Vectara API Key:</strong> API key.</li>
<li><strong>Files Url:</strong> Optional URLs for file initialization.</li>
</ul>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="57c7969574b1418dbb079ac5fc8cd857">Weaviate<a class="hash-link" aria-label="Direct link to Weaviate" title="Direct link to Weaviate" href="/components-vector-stores#57c7969574b1418dbb079ac5fc8cd857">​</a></h3>
<p><code>Weaviate</code> facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Weaviate URL:</strong> Default instance URL.</li>
<li><strong>Search By Text:</strong> Indicates whether to search by text.</li>
<li><strong>API Key:</strong> Optional API key for authentication.</li>
<li><strong>Index Name:</strong> Optional index name.</li>
<li><strong>Text Key:</strong> Default text extraction key.</li>
<li><strong>Input:</strong> Document or record.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Attributes:</strong> Optional additional attributes.</li>
</ul>
<p>For more details, see the <a href="https://python.langchain.com/docs/integrations/vectorstores/weaviate" target="_blank" rel="noopener noreferrer">Weaviate Component Documentation</a>.</p>
<p>NOTE</p>
<p>Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly.</p>
<hr>
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="6d4e616dfd6143b28dc055bc1c40ecae">Weaviate Search<a class="hash-link" aria-label="Direct link to Weaviate Search" title="Direct link to Weaviate Search" href="/components-vector-stores#6d4e616dfd6143b28dc055bc1c40ecae">​</a></h3>
<p><code>WeaviateSearch</code> searches a Weaviate Vector Store for documents similar to the input.</p>
<p><strong>Parameters:</strong></p>
<ul>
<li><strong>Search Type:</strong> Type of search, such as &quot;Similarity&quot; or &quot;MMR&quot;.</li>
<li><strong>Input Value:</strong> Search query.</li>
<li><strong>Weaviate URL:</strong> Default instance URL.</li>
<li><strong>Search By Text:</strong> Indicates whether to search by text.</li>
<li><strong>API Key:</strong> Optional API key for authentication.</li>
<li><strong>Index Name:</strong> Optional index name.</li>
<li><strong>Text Key:</strong> Default text extraction key.</li>
<li><strong>Embedding:</strong> Model used.</li>
<li><strong>Attributes:</strong> Optional additional attributes.</li>
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href="/components-vector-stores#686ba0e30a54438cbc7153b81ee4b1df">MongoDB Atlas Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#7ceebdd84ab14f8e8589c13c58370e5b">PGVector</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#196bf22ea2844bdbba971b5082750943">PGVector Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#67abbe3e27c34fb4bcb35926ce831727">Pinecone</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#977944558cad4cf2ba332ea4f06bf485">Pinecone Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#88df77f3044e4ac6980950835a919fb0">Qdrant</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#5ba5f8dca0f249d7ad00778f49901e6c">Qdrant Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#a0fb8a9d244a40eb8439d0f8c22a2562">Redis</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#80aea4da515f490e979c8576099ee880">Redis Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#e86fb3cc507e4b5494f0a421f94e853b">Supabase</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#fd02d550b9b2457f91f2f4073656cb09">Supabase Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#b4e05230b62a47c792a89c5511af97ac">Vectara</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#31a47221c23f4fbba4a7465cf1d89eb0">Vectara Search</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-vector-stores#57c7969574b1418dbb079ac5fc8cd857">Weaviate</a></li><li><a class="table-of-contents__link toc-highlight" 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