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

On this page

Rockset
=======

[Rockset](https://rockset.com) is a real-time analyitics SQL database that runs in the cloud. Rockset provides vector search capabilities, in the form of [SQL functions](https://rockset.com/docs/vector-functions/#vector-distance-functions), to support AI applications that rely on text similarity.

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

Install the rockset client.

    yarn add @rockset/client

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

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 @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

Below is an example showcasing how to use OpenAI and Rockset to answer questions about a text file:

    import * as rockset from "@rockset/client";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { RocksetStore } from "@langchain/community/vectorstores/rockset";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { readFileSync } from "fs";import { ChatPromptTemplate } from "@langchain/core/prompts";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";const store = await RocksetStore.withNewCollection(new OpenAIEmbeddings(), {  client: rockset.default.default(    process.env.ROCKSET_API_KEY ?? "",    `https://api.${process.env.ROCKSET_API_REGION ?? "usw2a1"}.rockset.com`  ),  collectionName: "langchain_demo",});const model = new ChatOpenAI({ model: "gpt-3.5-turbo-1106" });const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([  [    "system",    "Answer the user's questions based on the below context:\n\n{context}",  ],  ["human", "{input}"],]);const combineDocsChain = await createStuffDocumentsChain({  llm: model,  prompt: questionAnsweringPrompt,});const chain = await createRetrievalChain({  retriever: store.asRetriever(),  combineDocsChain,});const text = readFileSync("state_of_the_union.txt", "utf8");const docs = await new RecursiveCharacterTextSplitter().createDocuments([text]);await store.addDocuments(docs);const response = await chain.invoke({  input: "When was America founded?",});console.log(response.answer);await store.destroy();

#### API Reference:

*   [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [RocksetStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_rockset.RocksetStore.html) from `@langchain/community/vectorstores/rockset`
*   [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter`
*   [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts`
*   [createStuffDocumentsChain](https://api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents`
*   [createRetrievalChain](https://api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval`

* * *

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*   [Setup](#setup)
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