!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

You are viewing the **preview** v0.2 docs. View the **stable** v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386).

[

![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png)

](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com)

[More](#)

*   [People](/v0.2/docs/people/)
*   [Community](/v0.2/docs/community)
*   [Tutorials](/v0.2/docs/additional_resources/tutorials)
*   [Contributing](/v0.2/docs/contributing)

[v0.2](#)

*   [v0.2](/v0.2/docs/introduction)
*   [v0.1](https://js.langchain.com/v0.1/docs/get_started/introduction)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Providers](/v0.2/docs/integrations/platforms/)
    
    *   [Providers](/v0.2/docs/integrations/platforms/)
    *   [Anthropic](/v0.2/docs/integrations/platforms/anthropic)
    *   [AWS](/v0.2/docs/integrations/platforms/aws)
    *   [Google](/v0.2/docs/integrations/platforms/google)
    *   [Microsoft](/v0.2/docs/integrations/platforms/microsoft)
    *   [OpenAI](/v0.2/docs/integrations/platforms/openai)
*   [Components](/v0.2/docs/integrations/components)
    
    *   [Chat models](/v0.2/docs/integrations/chat/)
        
    *   [LLMs](/v0.2/docs/integrations/llms/)
        
    *   [Embedding models](/v0.2/docs/integrations/text_embedding)
        
    *   [Document loaders](/v0.2/docs/integrations/document_loaders)
        
    *   [Document transformers](/v0.2/docs/integrations/document_transformers)
        
    *   [Vector stores](/v0.2/docs/integrations/vectorstores)
        
        *   [Memory](/v0.2/docs/integrations/vectorstores/memory)
        *   [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb)
        *   [Astra DB](/v0.2/docs/integrations/vectorstores/astradb)
        *   [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch)
        *   [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb)
        *   [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra)
        *   [Chroma](/v0.2/docs/integrations/vectorstores/chroma)
        *   [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse)
        *   [CloseVector](/v0.2/docs/integrations/vectorstores/closevector)
        *   [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize)
        *   [Convex](/v0.2/docs/integrations/vectorstores/convex)
        *   [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase)
        *   [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch)
        *   [Faiss](/v0.2/docs/integrations/vectorstores/faiss)
        *   [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai)
        *   [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector)
        *   [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib)
        *   [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb)
        *   [Milvus](/v0.2/docs/integrations/vectorstores/milvus)
        *   [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index)
        *   [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas)
        *   [MyScale](/v0.2/docs/integrations/vectorstores/myscale)
        *   [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector)
        *   [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon)
        *   [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch)
        *   [PGVector](/v0.2/docs/integrations/vectorstores/pgvector)
        *   [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone)
        *   [Prisma](/v0.2/docs/integrations/vectorstores/prisma)
        *   [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant)
        *   [Redis](/v0.2/docs/integrations/vectorstores/redis)
        *   [Rockset](/v0.2/docs/integrations/vectorstores/rockset)
        *   [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore)
        *   [Supabase](/v0.2/docs/integrations/vectorstores/supabase)
        *   [Tigris](/v0.2/docs/integrations/vectorstores/tigris)
        *   [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer)
        *   [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm)
        *   [Typesense](/v0.2/docs/integrations/vectorstores/typesense)
        *   [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash)
        *   [USearch](/v0.2/docs/integrations/vectorstores/usearch)
        *   [Vectara](/v0.2/docs/integrations/vectorstores/vectara)
        *   [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres)
        *   [Voy](/v0.2/docs/integrations/vectorstores/voy)
        *   [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate)
        *   [Xata](/v0.2/docs/integrations/vectorstores/xata)
        *   [Zep](/v0.2/docs/integrations/vectorstores/zep)
    *   [Retrievers](/v0.2/docs/integrations/retrievers)
        
    *   [Tools](/v0.2/docs/integrations/tools)
        
    *   [Toolkits](/v0.2/docs/integrations/toolkits)
        
    *   [Stores](/v0.2/docs/integrations/stores/)
        

*   [](/v0.2/)
*   [Components](/v0.2/docs/integrations/components)
*   [Vector stores](/v0.2/docs/integrations/vectorstores)
*   SingleStore

On this page

SingleStore
===========

[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premise. It provides vector storage, as well as vector functions like [dot\_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean\_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching.

Compatibility

Only available on Node.js.

LangChain.js requires the `mysql2` library to create a connection to a SingleStoreDB instance.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

1.  Establish a SingleStoreDB environment. You have the flexibility to choose between [Cloud-based](https://docs.singlestore.com/managed-service/en/getting-started-with-singlestoredb-cloud.html) or [On-Premise](https://docs.singlestore.com/db/v8.1/en/developer-resources/get-started-using-singlestoredb-for-free.html) editions.
2.  Install the mysql2 JS client

*   npm
*   Yarn
*   pnpm

    npm install -S mysql2

    yarn add mysql2

    pnpm add mysql2

Usage[​](#usage "Direct link to Usage")
---------------------------------------

`SingleStoreVectorStore` manages a connection pool. It is recommended to call `await store.end();` before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.

### Standard usage[​](#standard-usage "Direct link to Standard usage")

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

Below is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the `SingleStoreVectorStore`:

    import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";import { OpenAIEmbeddings } from "@langchain/openai";export const run = async () => {  const vectorStore = await SingleStoreVectorStore.fromTexts(    ["Hello world", "Bye bye", "hello nice world"],    [{ id: 2 }, { id: 1 }, { id: 3 }],    new OpenAIEmbeddings(),    {      connectionOptions: {        host: process.env.SINGLESTORE_HOST,        port: Number(process.env.SINGLESTORE_PORT),        user: process.env.SINGLESTORE_USERNAME,        password: process.env.SINGLESTORE_PASSWORD,        database: process.env.SINGLESTORE_DATABASE,      },    }  );  const resultOne = await vectorStore.similaritySearch("hello world", 1);  console.log(resultOne);  await vectorStore.end();};

#### API Reference:

*   [SingleStoreVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_singlestore.SingleStoreVectorStore.html) from `@langchain/community/vectorstores/singlestore`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Metadata Filtering[​](#metadata-filtering "Direct link to Metadata Filtering")

If it is needed to filter results based on specific metadata fields, you can pass a filter parameter to narrow down your search to the documents that match all specified fields in the filter object:

    import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";import { OpenAIEmbeddings } from "@langchain/openai";export const run = async () => {  const vectorStore = await SingleStoreVectorStore.fromTexts(    ["Good afternoon", "Bye bye", "Boa tarde!", "Até logo!"],    [      { id: 1, language: "English" },      { id: 2, language: "English" },      { id: 3, language: "Portugese" },      { id: 4, language: "Portugese" },    ],    new OpenAIEmbeddings(),    {      connectionOptions: {        host: process.env.SINGLESTORE_HOST,        port: Number(process.env.SINGLESTORE_PORT),        user: process.env.SINGLESTORE_USERNAME,        password: process.env.SINGLESTORE_PASSWORD,        database: process.env.SINGLESTORE_DATABASE,      },      distanceMetric: "EUCLIDEAN_DISTANCE",    }  );  const resultOne = await vectorStore.similaritySearch("greetings", 1, {    language: "Portugese",  });  console.log(resultOne);  await vectorStore.end();};

#### API Reference:

*   [SingleStoreVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_singlestore.SingleStoreVectorStore.html) from `@langchain/community/vectorstores/singlestore`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

* * *

#### Was this page helpful?

  

#### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E).

[

Previous

Rockset

](/v0.2/docs/integrations/vectorstores/rockset)[

Next

Supabase

](/v0.2/docs/integrations/vectorstores/supabase)

*   [Setup](#setup)
*   [Usage](#usage)
    *   [Standard usage](#standard-usage)
    *   [Metadata Filtering](#metadata-filtering)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.