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

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Overview

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Integration details

ClassPackageLocalPy supportDownloadsVersion
CohereEmbeddings@langchain/cohere❌✅NPM - DownloadsNPM - Version

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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.

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Credentials

Head to cohere.com to sign up to Cohere and generate an API key. Once you’ve done this set the COHERE_API_KEY environment variable: 
export COHERE_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 CohereEmbeddings integration lives in the @langchain/cohere package: 
npm install @langchain/cohere @langchain/core

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Instantiation

Now we can instantiate our model object and generate chat completions: 
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",
});

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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 ChatCohere constructor. Note: If a custom client is provided both COHERE_API_KEY environment variable and apiKey parameter in the constructor will be ignored 
import { CohereEmbeddings } from "@langchain/cohere";
import { CohereClient } from "cohere-ai";

const client = new CohereClient({
  token: "<your-api-key>",
  environment: "<your-cohere-deployment-url>", //optional
  // other params
});

const embeddingsWithCustomClient = new CohereEmbeddings({
  client,
  // other params...
});

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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));

[
   -0.022979736,  -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
]

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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));

[
   -0.028869629,  -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
]

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API reference

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