> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # OpenAIEmbeddings integration > Integrate with the OpenAIEmbeddings embedding model using LangChain JavaScript. This will help you get started with OpenAIEmbeddings [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-openai/OpenAIEmbeddings). ## Overview ### Integration details | Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/openai/) | Downloads | Version | | :------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :---: | :-----------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | | [`OpenAIEmbeddings`](https://reference.langchain.com/javascript/langchain-openai/OpenAIEmbeddings) | [`@langchain/openai`](https://www.npmjs.com/package/@langchain/openai) | ❌ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/openai?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/openai?style=flat-square\&label=%20&) | ## 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. ### Credentials Head to [platform.openai.com](https://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: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} export OPENAI_API_KEY="your-api-key" ``` If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/home) API key by uncommenting below: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # export LANGSMITH_TRACING="true" # export LANGSMITH_API_KEY="your-api-key" ``` ### Installation The LangChain OpenAIEmbeddings integration lives in the `@langchain/openai` package: ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/openai @langchain/core ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/openai @langchain/core ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/openai @langchain/core ``` ## Instantiation Now we can instantiate our model object and generate chat completions: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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. ## 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](/oss/javascript/learn/). 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`](/oss/javascript/integrations/vectorstores/memory). ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} // 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; ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} LangChain is the framework for building context-aware reasoning applications ``` ## 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. ### Embed single texts You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const singleVector = await embeddings.embedQuery(text); console.log(singleVector.slice(0, 100)); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ -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 ] ``` ### 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: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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)); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ -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 ] ``` ## 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: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 3072 ``` By passing in `dimensions: 1024` we can reduce the size of our embeddings to 1024: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 1024 ``` ## Custom URLs You can customize the base URL the SDK sends requests to by passing a `configuration` parameter like this: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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](/oss/javascript/integrations/embeddings/azure_openai). *** ## API reference For detailed documentation of all `OpenAIEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-openai/OpenAIEmbeddings). ***
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