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

On this page

Redis
=====

[Redis](https://redis.io/) is a fast open source, in-memory data store. As part of the [Redis Stack](https://redis.io/docs/stack/get-started/), [RediSearch](https://redis.io/docs/stack/search/) is the module that enables vector similarity semantic search, as well as many other types of searching.

Compatibility

Only available on Node.js.

LangChain.js accepts [node-redis](https://github.com/redis/node-redis) as the client for Redis vectorstore.

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

1.  Run Redis with Docker on your computer following [the docs](https://redis.io/docs/stack/get-started/install/docker/#redisredis-stack)
2.  Install the node-redis JS client

*   npm
*   Yarn
*   pnpm

    npm install -S redis

    yarn add redis

    pnpm add redis

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

Index docs[​](#index-docs "Direct link to Index docs")
------------------------------------------------------

    import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({  url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [  new Document({    metadata: { foo: "bar" },    pageContent: "redis is fast",  }),  new Document({    metadata: { foo: "bar" },    pageContent: "the quick brown fox jumped over the lazy dog",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "lorem ipsum dolor sit amet",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "consectetur adipiscing elit",  }),];const vectorStore = await RedisVectorStore.fromDocuments(  docs,  new OpenAIEmbeddings(),  {    redisClient: client,    indexName: "docs",  });await client.disconnect();

#### API Reference:

*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis`
*   [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

Query docs[​](#query-docs "Direct link to Query docs")
------------------------------------------------------

    import { createClient } from "redis";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { ChatPromptTemplate } from "@langchain/core/prompts";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";const client = createClient({  url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), {  redisClient: client,  indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[  Document {    pageContent: "redis is fast",    metadata: { foo: "bar" }  }]*//* Search in the vector DB using filters */const filterRes = await vectorStore.similaritySearch("redis", 3, ["qux"]);console.log(filterRes);/*[  Document {    pageContent: "consectetur adipiscing elit",    metadata: { baz: "qux" },  },  Document {    pageContent: "lorem ipsum dolor sit amet",    metadata: { baz: "qux" },  }]*//* Usage as part of a chain */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: vectorStore.asRetriever(),  combineDocsChain,});const chainRes = await chain.invoke({ input: "What did the fox do?" });console.log(chainRes);/*  {    input: 'What did the fox do?',    chat_history: [],    context: [      Document {        pageContent: 'the quick brown fox jumped over the lazy dog',        metadata: [Object]      },      Document {        pageContent: 'lorem ipsum dolor sit amet',        metadata: [Object]      },      Document {        pageContent: 'consectetur adipiscing elit',        metadata: [Object]      },      Document { pageContent: 'redis is fast', metadata: [Object] }    ],    answer: 'The fox jumped over the lazy dog.'  }*/await client.disconnect();

#### 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`
*   [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis`
*   [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`

Create index with options[​](#create-index-with-options "Direct link to Create index with options")
---------------------------------------------------------------------------------------------------

To pass arguments for [index creation](https://redis.io/commands/ft.create/), you can utilize the [available options](https://github.com/redis/node-redis/blob/294cbf8367295ac81cbe51ce2932493ab80493f1/packages/search/lib/commands/CREATE.ts#L4) offered by [node-redis](https://github.com/redis/node-redis) through `createIndexOptions` parameter.

    import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({  url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [  new Document({    metadata: { foo: "bar" },    pageContent: "redis is fast",  }),  new Document({    metadata: { foo: "bar" },    pageContent: "the quick brown fox jumped over the lazy dog",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "lorem ipsum dolor sit amet",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "consectetur adipiscing elit",  }),];const vectorStore = await RedisVectorStore.fromDocuments(  docs,  new OpenAIEmbeddings(),  {    redisClient: client,    indexName: "docs",    createIndexOptions: {      TEMPORARY: 1000,    },  });await client.disconnect();

#### API Reference:

*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis`
*   [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

Delete an index[​](#delete-an-index "Direct link to Delete an index")
---------------------------------------------------------------------

    import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({  url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [  new Document({    metadata: { foo: "bar" },    pageContent: "redis is fast",  }),  new Document({    metadata: { foo: "bar" },    pageContent: "the quick brown fox jumped over the lazy dog",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "lorem ipsum dolor sit amet",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "consectetur adipiscing elit",  }),];const vectorStore = await RedisVectorStore.fromDocuments(  docs,  new OpenAIEmbeddings(),  {    redisClient: client,    indexName: "docs",  });await vectorStore.delete({ deleteAll: true });await client.disconnect();

#### API Reference:

*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis`
*   [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

* * *

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*   [Setup](#setup)
*   [Index docs](#index-docs)
*   [Query docs](#query-docs)
*   [Create index with options](#create-index-with-options)
*   [Delete an index](#delete-an-index)

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