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This will help you get started with OpenAIEmbeddings embedding models using LangChain. For detailed documentation on OpenAIEmbeddings features and configuration options, please refer to the API reference.

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Overview

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Integration details

ClassPackageLocalPy supportDownloadsVersion
OpenAIEmbeddings@langchain/openai❌✅NPM - DownloadsNPM - Version

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Setup

To access OpenAIEmbeddings embedding models you’ll need to create an OpenAI account, get an API key, and install the @langchain/openai integration package.

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Credentials

Head to platform.openai.com to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable: 
export OPENAI_API_KEY="your-api-key"
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain OpenAIEmbeddings integration lives in the @langchain/openai package: 
npm install @langchain/openai @langchain/core

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Instantiation

Now we can instantiate our model object and generate chat completions: 
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings({
  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
  batchSize: 512, // Default value if omitted is 512. Max is 2048
  model: "text-embedding-3-large",
});
If you’re part of an organization, you can set process.env.OPENAI_ORGANIZATION to your OpenAI organization id, or pass it in as organization when initializing the model.

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Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the Learn tab. Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document using the demo MemoryVectorStore. 
// Create a vector store with a sample text
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";

const text = "LangChain is the framework for building context-aware reasoning applications";

const vectorstore = await MemoryVectorStore.fromDocuments(
  [{ pageContent: text, metadata: {} }],
  embeddings,
);

// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);

// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");

retrievedDocuments[0].pageContent;

LangChain is the framework for building context-aware reasoning applications

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Direct Usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embedDocument(...) and embeddings.embedQuery(...) to create embeddings for the text(s) used in fromDocuments and the retriever’s invoke operations, respectively. You can directly call these methods to get embeddings for your own use cases.

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Embed single texts

You can embed queries for search with embedQuery. This generates a vector representation specific to the query: 
const singleVector = await embeddings.embedQuery(text);

console.log(singleVector.slice(0, 100));

[
    -0.01927683,  0.0037708976,  -0.032942563,  0.0037671267,  0.008175306,
   -0.012511838,  -0.009713832,   0.021403614,  -0.015377721, 0.0018684798,
    0.020574018,   0.022399133,   -0.02322873,   -0.01524951,  -0.00504169,
   -0.007375876,   -0.03448109, 0.00015130726,   0.021388533, -0.012564631,
   -0.020031009,   0.027406884,  -0.039217334,    0.03036327,  0.030393435,
   -0.021750538,   0.032610722,  -0.021162277,  -0.025898525,  0.018869571,
    0.034179416,  -0.013371604,  0.0037652412,   -0.02146395, 0.0012641934,
   -0.055688616,    0.05104287,  0.0024982197,  -0.019095825, 0.0037369595,
  0.00088757504,   0.025189597,  -0.018779071,   0.024978427,  0.016833287,
  -0.0025868358,  -0.011727491, -0.0021154736,  -0.017738303, 0.0013839195,
  -0.0131151825,   -0.05405959,   0.029729757,  -0.003393808,  0.019774588,
    0.028885076,   0.004355387,   0.026094612,    0.06479911,  0.038040817,
    -0.03478276,  -0.012594799,  -0.024767255, -0.0031430433,  0.017874055,
   -0.015294761,   0.005709139,   0.025355516,   0.044798266,   0.02549127,
    -0.02524993, 0.00014553308,  -0.019427665,  -0.023545485,  0.008748483,
    0.019850006,  -0.028417485,  -0.001860938,   -0.02318348, -0.010799851,
     0.04793565, -0.0048983963,    0.02193154,  -0.026411368,  0.026426451,
   -0.012149832,   0.035355937,  -0.047814984,  -0.027165547, -0.008228099,
   -0.007737882,   0.023726488,  -0.046487626,  -0.007783133, -0.019638835,
     0.01793439,  -0.018024892,  0.0030336871,  -0.019578502, 0.0042837397
]

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Embed multiple texts

You can embed multiple texts for indexing with embedDocuments. The internals used for this method may (but do not have to) differ from embedding queries: 
const text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs";

const vectors = await embeddings.embedDocuments([text, text2]);

console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));

[
    -0.01927683,  0.0037708976,  -0.032942563,  0.0037671267,  0.008175306,
   -0.012511838,  -0.009713832,   0.021403614,  -0.015377721, 0.0018684798,
    0.020574018,   0.022399133,   -0.02322873,   -0.01524951,  -0.00504169,
   -0.007375876,   -0.03448109, 0.00015130726,   0.021388533, -0.012564631,
   -0.020031009,   0.027406884,  -0.039217334,    0.03036327,  0.030393435,
   -0.021750538,   0.032610722,  -0.021162277,  -0.025898525,  0.018869571,
    0.034179416,  -0.013371604,  0.0037652412,   -0.02146395, 0.0012641934,
   -0.055688616,    0.05104287,  0.0024982197,  -0.019095825, 0.0037369595,
  0.00088757504,   0.025189597,  -0.018779071,   0.024978427,  0.016833287,
  -0.0025868358,  -0.011727491, -0.0021154736,  -0.017738303, 0.0013839195,
  -0.0131151825,   -0.05405959,   0.029729757,  -0.003393808,  0.019774588,
    0.028885076,   0.004355387,   0.026094612,    0.06479911,  0.038040817,
    -0.03478276,  -0.012594799,  -0.024767255, -0.0031430433,  0.017874055,
   -0.015294761,   0.005709139,   0.025355516,   0.044798266,   0.02549127,
    -0.02524993, 0.00014553308,  -0.019427665,  -0.023545485,  0.008748483,
    0.019850006,  -0.028417485,  -0.001860938,   -0.02318348, -0.010799851,
     0.04793565, -0.0048983963,    0.02193154,  -0.026411368,  0.026426451,
   -0.012149832,   0.035355937,  -0.047814984,  -0.027165547, -0.008228099,
   -0.007737882,   0.023726488,  -0.046487626,  -0.007783133, -0.019638835,
     0.01793439,  -0.018024892,  0.0030336871,  -0.019578502, 0.0042837397
]
[
   -0.010181213,   0.023419594,   -0.04215527, -0.0015320902,  -0.023573855,
  -0.0091644935,  -0.014893179,   0.019016149,  -0.023475688,  0.0010219777,
    0.009255648,    0.03996757,   -0.04366983,   -0.01640774,  -0.020194141,
    0.019408813,  -0.027977299,  -0.022017224,   0.013539891,  -0.007769135,
    0.032647192,  -0.015089511,  -0.022900717,   0.023798235,   0.026084099,
   -0.024625633,   0.035003178,  -0.017978394,  -0.049615882,   0.013364594,
    0.031132633,   0.019142363,   0.023195215,  -0.038396914,   0.005584942,
   -0.031946007,   0.053682756, -0.0036356465,   0.011240003,  0.0056690844,
  -0.0062791156,   0.044146635,  -0.037387207,    0.01300699,   0.018946031,
   0.0050415234,   0.029618073,  -0.021750772,  -0.000649473, 0.00026951815,
   -0.014710871,  -0.029814405,    0.04204308,  -0.014710871,  0.0039616977,
   -0.021512369,   0.054608323,   0.021484323,    0.02790718,  -0.010573876,
   -0.023952495,  -0.035143413,  -0.048802506, -0.0075798146,   0.023279356,
   -0.022690361,  -0.016590048,  0.0060477243,   0.014100839,   0.005476258,
   -0.017221114, -0.0100059165,  -0.017922299,  -0.021989176,    0.01830094,
     0.05516927,   0.001033372,  0.0017310516,   -0.00960624,  -0.037864015,
    0.013063084,   0.006591143,  -0.010160177,  0.0011394264,    0.04953174,
    0.004806626,   0.029421741,  -0.037751824,   0.003618117,   0.007162609,
    0.027696826, -0.0021070621,  -0.024485396, -0.0042141243,   -0.02801937,
   -0.019605145,   0.016281527,  -0.035143413,    0.01640774,   0.042323552
]

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Specifying dimensions

With the text-embedding-3 class of models, you can specify the size of the embeddings you want returned. For example by default text-embedding-3-large returns embeddings of dimension 3072: 
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddingsDefaultDimensions = new OpenAIEmbeddings({
  model: "text-embedding-3-large",
});

const vectorsDefaultDimensions = await embeddingsDefaultDimensions.embedDocuments(["some text"]);
console.log(vectorsDefaultDimensions[0].length);

3072
But by passing in dimensions: 1024 we can reduce the size of our embeddings to 1024: 
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings1024 = new OpenAIEmbeddings({
  model: "text-embedding-3-large",
  dimensions: 1024,
});

const vectors1024 = await embeddings1024.embedDocuments(["some text"]);
console.log(vectors1024[0].length);

1024

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Custom URLs

You can customize the base URL the SDK sends requests to by passing a configuration parameter like this: 
import { OpenAIEmbeddings } from "@langchain/openai";

const model = new OpenAIEmbeddings({
  configuration: {
    baseURL: "https://your_custom_url.com",
  },
});
You can also pass other ClientOptions parameters accepted by the official SDK. If you are hosting on Azure OpenAI, see the dedicated page instead.

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API reference

For detailed documentation of all OpenAIEmbeddings features and configurations head to the API reference.
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