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    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
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    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
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*   How to cache embedding results

On this page

How to cache embedding results
==============================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Embeddings](/v0.2/docs/concepts/#embedding-models)

Embeddings can be stored or temporarily cached to avoid needing to recompute them.

Caching embeddings can be done using a `CacheBackedEmbeddings` instance.

The cache backed embedder is a wrapper around an embedder that caches embeddings in a key-value store.

The text is hashed and the hash is used as the key in the cache.

The main supported way to initialized a `CacheBackedEmbeddings` is the `fromBytesStore` static method. This takes in the following parameters:

*   `underlyingEmbeddings`: The embeddings model to use.
*   `documentEmbeddingCache`: The cache to use for storing document embeddings.
*   `namespace`: (optional, defaults to "") The namespace to use for document cache. This namespace is used to avoid collisions with other caches. For example, you could set it to the name of the embedding model used.

**Attention:** Be sure to set the namespace parameter to avoid collisions of the same text embedded using different embeddings models.

In-memory[​](#in-memory "Direct link to In-memory")
---------------------------------------------------

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

Here's a basic test example with an in memory cache. This type of cache is primarily useful for unit tests or prototyping. Do not use this cache if you need to actually store the embeddings for an extended period of time:

    import { OpenAIEmbeddings } from "@langchain/openai";import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";import { InMemoryStore } from "@langchain/core/stores";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { TextLoader } from "langchain/document_loaders/fs/text";const underlyingEmbeddings = new OpenAIEmbeddings();const inMemoryStore = new InMemoryStore();const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(  underlyingEmbeddings,  inMemoryStore,  {    namespace: underlyingEmbeddings.modelName,  });const loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 1000,  chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);// No keys logged yet since the cache is emptyfor await (const key of inMemoryStore.yieldKeys()) {  console.log(key);}let time = Date.now();const vectorstore = await FaissStore.fromDocuments(  documents,  cacheBackedEmbeddings);console.log(`Initial creation time: ${Date.now() - time}ms`);/*  Initial creation time: 1905ms*/// The second time is much faster since the embeddings for the input docs have already been added to the cachetime = Date.now();const vectorstore2 = await FaissStore.fromDocuments(  documents,  cacheBackedEmbeddings);console.log(`Cached creation time: ${Date.now() - time}ms`);/*  Cached creation time: 8ms*/// Many keys logged with hashed valuesconst keys = [];for await (const key of inMemoryStore.yieldKeys()) {  keys.push(key);}console.log(keys.slice(0, 5));/*  [    'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64',    'text-embedding-ada-0023b424f5ed1271a6f5601add17c1b58b7c992772e',    'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111',    'text-embedding-ada-00262f72e0c2d711c6b861714ee624b28af639fdb13',    'text-embedding-ada-00262d58882330038a4e6e25ea69a938f4391541874'  ]*/

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [CacheBackedEmbeddings](https://v02.api.js.langchain.com/classes/langchain_embeddings_cache_backed.CacheBackedEmbeddings.html) from `langchain/embeddings/cache_backed`
*   [InMemoryStore](https://v02.api.js.langchain.com/classes/langchain_core_stores.InMemoryStore.html) from `@langchain/core/stores`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [FaissStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`

Redis[​](#redis "Direct link to Redis")
---------------------------------------

Here's an example with a Redis cache.

You'll first need to install `ioredis` as a peer dependency and pass in an initialized client:

*   npm
*   Yarn
*   pnpm

    npm install ioredis

    yarn add ioredis

    pnpm add ioredis

    import { Redis } from "ioredis";import { OpenAIEmbeddings } from "@langchain/openai";import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { TextLoader } from "langchain/document_loaders/fs/text";import { RedisByteStore } from "@langchain/community/storage/ioredis";const underlyingEmbeddings = new OpenAIEmbeddings();// Requires a Redis instance running at http://localhost:6379.// See https://github.com/redis/ioredis for full config options.const redisClient = new Redis();const redisStore = new RedisByteStore({  client: redisClient,});const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(  underlyingEmbeddings,  redisStore,  {    namespace: underlyingEmbeddings.modelName,  });const loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 1000,  chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);let time = Date.now();const vectorstore = await FaissStore.fromDocuments(  documents,  cacheBackedEmbeddings);console.log(`Initial creation time: ${Date.now() - time}ms`);/*  Initial creation time: 1808ms*/// The second time is much faster since the embeddings for the input docs have already been added to the cachetime = Date.now();const vectorstore2 = await FaissStore.fromDocuments(  documents,  cacheBackedEmbeddings);console.log(`Cached creation time: ${Date.now() - time}ms`);/*  Cached creation time: 33ms*/// Many keys logged with hashed valuesconst keys = [];for await (const key of redisStore.yieldKeys()) {  keys.push(key);}console.log(keys.slice(0, 5));/*  [    'text-embedding-ada-002fa9ac80e1bf226b7b4dfc03ea743289a65a727b2',    'text-embedding-ada-0027dbf9c4b36e12fe1768300f145f4640342daaf22',    'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64',    'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111',    'text-embedding-ada-002c00f818c345da13fed9f2697b4b689338143c8c7'  ]*/

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [CacheBackedEmbeddings](https://v02.api.js.langchain.com/classes/langchain_embeddings_cache_backed.CacheBackedEmbeddings.html) from `langchain/embeddings/cache_backed`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [FaissStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`
*   [RedisByteStore](https://v02.api.js.langchain.com/classes/langchain_community_storage_ioredis.RedisByteStore.html) from `@langchain/community/storage/ioredis`

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You've now learned how to use caching to avoid recomputing embeddings.

Next, check out the [full tutorial on retrieval-augmented generation](/v0.2/docs/tutorials/rag).

* * *

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