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*   [Vector stores](/v0.2/docs/integrations/vectorstores)
*   ClickHouse

On this page

ClickHouse
==========

Compatibility

Only available on Node.js.

[ClickHouse](https://clickhouse.com/) is a robust and open-source columnar database that is used for handling analytical queries and efficient storage, ClickHouse is designed to provide a powerful combination of vector search and analytics.

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

1.  Launch a ClickHouse cluster. Refer to the [ClickHouse Installation Guide](https://clickhouse.com/docs/en/getting-started/install/) for details.
2.  After launching a ClickHouse cluster, retrieve the `Connection Details` from the cluster's `Actions` menu. You will need the host, port, username, and password.
3.  Install the required Node.js peer dependency for ClickHouse in your workspace.

You will need to install the following peer dependencies:

*   npm
*   Yarn
*   pnpm

    npm install -S @clickhouse/client mysql2

    yarn add @clickhouse/client mysql2

    pnpm add @clickhouse/client mysql2

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

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

    import { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";import { OpenAIEmbeddings } from "@langchain/openai";// Initialize ClickHouse store from textsconst vectorStore = await ClickHouseStore.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.CLICKHOUSE_HOST || "localhost",    port: process.env.CLICKHOUSE_PORT || 8443,    username: process.env.CLICKHOUSE_USER || "username",    password: process.env.CLICKHOUSE_PASSWORD || "password",    database: process.env.CLICKHOUSE_DATABASE || "default",    table: process.env.CLICKHOUSE_TABLE || "vector_table",  });// Sleep 1 second to ensure that the search occurs after the successful insertion of data.// eslint-disable-next-line no-promise-executor-returnawait new Promise((resolve) => setTimeout(resolve, 1000));// Perform similarity search without filteringconst results = await vectorStore.similaritySearch("hello world", 1);console.log(results);// Perform similarity search with filteringconst filteredResults = await vectorStore.similaritySearch("hello world", 1, {  whereStr: "metadata.name = '1'",});console.log(filteredResults);

#### API Reference:

*   [ClickHouseStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_clickhouse.ClickHouseStore.html) from `@langchain/community/vectorstores/clickhouse`
*   [OpenAIEmbeddings](https://v02.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 { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";import { OpenAIEmbeddings } from "@langchain/openai";// Initialize ClickHouse storeconst vectorStore = await ClickHouseStore.fromExistingIndex(  new OpenAIEmbeddings(),  {    host: process.env.CLICKHOUSE_HOST || "localhost",    port: process.env.CLICKHOUSE_PORT || 8443,    username: process.env.CLICKHOUSE_USER || "username",    password: process.env.CLICKHOUSE_PASSWORD || "password",    database: process.env.CLICKHOUSE_DATABASE || "default",    table: process.env.CLICKHOUSE_TABLE || "vector_table",  });// Sleep 1 second to ensure that the search occurs after the successful insertion of data.// eslint-disable-next-line no-promise-executor-returnawait new Promise((resolve) => setTimeout(resolve, 1000));// Perform similarity search without filteringconst results = await vectorStore.similaritySearch("hello world", 1);console.log(results);// Perform similarity search with filteringconst filteredResults = await vectorStore.similaritySearch("hello world", 1, {  whereStr: "metadata.name = '1'",});console.log(filteredResults);

#### API Reference:

*   [ClickHouseStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_clickhouse.ClickHouseStore.html) from `@langchain/community/vectorstores/clickhouse`
*   [OpenAIEmbeddings](https://v02.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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