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*   [Components](/v0.1/docs/integrations/components/)
*   [Vector stores](/v0.1/docs/integrations/vectorstores/)
*   MyScale

On this page

MyScale
=======

Compatibility

Only available on Node.js.

[MyScale](https://myscale.com/) is an emerging AI database that harmonizes the power of vector search and SQL analytics, providing a managed, efficient, and responsive experience.

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

1.  Launch a cluster through [MyScale's Web Console](https://console.myscale.com/). See [MyScale's official documentation](https://docs.myscale.com/en/quickstart/) for more information.
2.  After launching a cluster, view your `Connection Details` from your cluster's `Actions` menu. You will need the host, port, username, and password.
3.  Install the required Node.js peer dependency in your workspace.

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install -S @langchain/openai @clickhouse/client @langchain/community

    yarn add @langchain/openai @clickhouse/client @langchain/community

    pnpm add @langchain/openai @clickhouse/client @langchain/community

Index and Query Docs[​](#index-and-query-docs "Direct link to Index and Query Docs")
------------------------------------------------------------------------------------

    import { MyScaleStore } from "@langchain/community/vectorstores/myscale";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await MyScaleStore.fromTexts(  ["Hello world", "Bye bye", "hello nice world"],  [    { id: 2, name: "2" },    { id: 1, name: "1" },    { id: 3, name: "3" },  ],  new OpenAIEmbeddings(),  {    host: process.env.MYSCALE_HOST || "localhost",    port: process.env.MYSCALE_PORT || "8443",    username: process.env.MYSCALE_USERNAME || "username",    password: process.env.MYSCALE_PASSWORD || "password",    database: "default", // defaults to "default"    table: "your_table", // defaults to "vector_table"  });const results = await vectorStore.similaritySearch("hello world", 1);console.log(results);const filteredResults = await vectorStore.similaritySearch("hello world", 1, {  whereStr: "metadata.name = '1'",});console.log(filteredResults);

#### API Reference:

*   [MyScaleStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_myscale.MyScaleStore.html) from `@langchain/community/vectorstores/myscale`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

Query Docs From an Existing Collection[​](#query-docs-from-an-existing-collection "Direct link to Query Docs From an Existing Collection")
------------------------------------------------------------------------------------------------------------------------------------------

    import { MyScaleStore } from "@langchain/community/vectorstores/myscale";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await MyScaleStore.fromExistingIndex(  new OpenAIEmbeddings(),  {    host: process.env.MYSCALE_HOST || "localhost",    port: process.env.MYSCALE_PORT || "8443",    username: process.env.MYSCALE_USERNAME || "username",    password: process.env.MYSCALE_PASSWORD || "password",    database: "default", // defaults to "default"    table: "your_table", // defaults to "vector_table"  });const results = await vectorStore.similaritySearch("hello world", 1);console.log(results);const filteredResults = await vectorStore.similaritySearch("hello world", 1, {  whereStr: "metadata.name = '1'",});console.log(filteredResults);

#### API Reference:

*   [MyScaleStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_myscale.MyScaleStore.html) from `@langchain/community/vectorstores/myscale`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

* * *

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*   [Setup](#setup)
*   [Index and Query Docs](#index-and-query-docs)
*   [Query Docs From an Existing Collection](#query-docs-from-an-existing-collection)

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