> ## 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. # CohereEmbeddings integration > Integrate with the CohereEmbeddings embedding model using LangChain JavaScript. This will help you get started with CohereEmbeddings [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-cohere/CohereEmbeddings). ## Overview ### Integration details | Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/cohere/) | Downloads | Version | | :------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :---: | :-----------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | | [`CohereEmbeddings`](https://reference.langchain.com/javascript/langchain-cohere/CohereEmbeddings) | [`@langchain/cohere`](https://www.npmjs.com/package/@langchain/cohere) | ❌ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/cohere?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/cohere?style=flat-square\&label=%20&) | ## Setup To access Cohere embedding models you'll need to create a Cohere account, get an API key, and install the `@langchain/cohere` integration package. ### Credentials Head to [cohere.com](https://cohere.com) to sign up to `Cohere` and generate an API key. Once you've done this set the `COHERE_API_KEY` environment variable: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} export COHERE_API_KEY="your-api-key" ``` If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) 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 CohereEmbeddings integration lives in the `@langchain/cohere` package: ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/cohere @langchain/core ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/cohere @langchain/core ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/cohere @langchain/core ``` ## Instantiation Now we can instantiate our model object and embed text: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { CohereEmbeddings } from "@langchain/cohere"; const embeddings = new CohereEmbeddings({ apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.COHERE_API_KEY batchSize: 48, // Default value if omitted is 48. Max value is 96 model: "embed-english-v3.0", }); ``` ### Custom client for Cohere on Azure, Cohere on AWS Bedrock, and standalone Cohere instance We can instantiate a custom `CohereClient` and pass it to the `CohereEmbeddings` constructor. **Note:** If a custom client is provided both `COHERE_API_KEY` environment variable and apiKey parameter in the constructor will be ignored ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { CohereEmbeddings } from "@langchain/cohere"; import { CohereClient } from "cohere-ai"; const client = new CohereClient({ token: "", environment: "", //optional // other params }); const embeddingsWithCustomClient = new CohereEmbeddings({ client, // other params... }); ``` ## 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"}} [ -0.022979736, -0.030212402, -0.08886719, -0.08569336, 0.007030487, -0.0010671616, -0.033813477, 0.08843994, 0.0119018555, 0.049926758, -0.03616333, 0.007408142, 0.00034809113, -0.005744934, -0.016021729, -0.015296936, -0.0011606216, -0.02458191, -0.044006348, -0.0335083, 0.024658203, -0.051086426, 0.0020427704, 0.06298828, 0.020507812, 0.037475586, 0.05117798, 0.0059814453, 0.025360107, 0.0060577393, 0.02255249, -0.070129395, 0.024017334, 0.022766113, -0.042755127, -0.024673462, -0.0236969, -0.0073623657, 0.002161026, 0.011329651, 0.038330078, -0.03050232, 0.0022201538, -0.007911682, -0.0023536682, 0.029937744, -0.027297974, -0.064086914, 0.027267456, 0.016738892, 0.0028972626, 0.015510559, -0.01725769, 0.011497498, -0.012954712, 0.002380371, -0.03366089, -0.02746582, 0.014022827, 0.04196167, 0.007698059, -0.027069092, 0.025405884, -0.029815674, 0.013298035, 0.01737976, 0.07269287, 0.017822266, 0.0012550354, -0.009597778, -0.02961731, 0.0049057007, 0.01965332, -0.009994507, -0.019561768, -0.004764557, 0.019317627, -0.0045433044, 0.031143188, -0.018188477, -0.0026893616, 0.0050964355, -0.044189453, 0.02029419, -0.019088745, 0.02166748, -0.011657715, -0.025405884, -0.028030396, -0.0051460266, -0.010818481, -0.000364542, -0.028686523, 0.015029907, 0.0013790131, -0.0069770813, -0.030639648, -0.051208496, 0.005279541, -0.0109939575 ] ``` ### 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"}} [ -0.028869629, -0.030410767, -0.099121094, -0.07116699, -0.012748718, -0.0059432983, -0.04360962, 0.07965088, -0.027114868, 0.057403564, -0.013549805, 0.014480591, 0.021697998, -0.026870728, 0.0071983337, -0.0099105835, -0.0034332275, -0.026031494, -0.05206299, -0.045288086, 0.027450562, -0.060333252, -0.019210815, 0.039794922, 0.0055351257, 0.046325684, 0.017837524, -0.012619019, 0.023147583, -0.008201599, 0.022155762, -0.035888672, 0.016921997, 0.027679443, -0.023605347, -0.0022029877, -0.025253296, 0.013076782, 0.0049705505, -0.0024280548, 0.021957397, -0.008644104, -0.00004029274, -0.003501892, -0.012641907, 0.01600647, -0.014312744, -0.037841797, 0.011764526, -0.019622803, -0.01928711, -0.017044067, -0.017547607, 0.028533936, -0.019073486, -0.0061073303, -0.024520874, 0.01638794, 0.017852783, -0.0013303757, -0.023040771, -0.01713562, 0.027786255, -0.02583313, 0.03060913, 0.00013923645, 0.01977539, 0.025283813, -0.00068569183, 0.032806396, -0.021392822, -0.016174316, 0.016464233, 0.006023407, -0.0025043488, -0.033813477, 0.023269653, 0.012329102, 0.030334473, 0.014419556, -0.026245117, -0.018356323, -0.016433716, 0.022628784, -0.024108887, 0.02897644, -0.017105103, -0.009208679, -0.015541077, -0.020004272, -0.005153656, 0.03741455, -0.050750732, 0.012176514, -0.017501831, -0.014503479, 0.0052223206, -0.03250122, 0.008666992, -0.015823364 ] [ -0.047332764, -0.049957275, -0.07458496, -0.034332275, -0.057922363, -0.0112838745, -0.06994629, 0.06347656, -0.03326416, 0.019897461, 0.0103302, 0.04660034, -0.059753418, -0.027511597, 0.012245178, -0.03164673, -0.010215759, -0.00687027, -0.03314209, -0.019866943, 0.008399963, -0.042144775, -0.03781128, 0.025970459, 0.007335663, 0.04107666, -0.015991211, 0.0158844, -0.008483887, -0.008399963, 0.01777649, -0.01109314, 0.01864624, 0.014328003, -0.005264282, 0.077697754, 0.017684937, 0.0020427704, 0.032470703, -0.0029354095, 0.003063202, 0.0008301735, 0.016281128, -0.005897522, -0.023254395, 0.004043579, -0.021987915, -0.015419006, 0.0009803772, 0.044677734, -0.0045814514, 0.0039901733, -0.019058228, 0.063964844, -0.012496948, -0.027755737, 0.01574707, -0.03781128, 0.0038909912, -0.00002193451, 0.00013685226, 0.027832031, 0.015182495, -0.008590698, 0.03933716, -0.0020141602, -0.050567627, 0.02017212, 0.020523071, 0.07287598, 0.0031375885, -0.05227661, -0.01838684, -0.0019626617, -0.0039482117, 0.02494812, 0.0009508133, 0.008583069, 0.02923584, 0.028198242, -0.030334473, -0.014076233, -0.017990112, 0.0026245117, -0.017150879, 0.004497528, -0.00365448, -0.0012168884, 0.011741638, 0.012886047, 0.00084400177, 0.060638428, -0.024002075, 0.022415161, -0.015823364, -0.0026760101, 0.028625488, 0.041015625, 0.006893158, -0.01902771 ] ``` *** ## API reference For detailed documentation of all `CohereEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-cohere/CohereEmbeddings). ***
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