> ## 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.

# VertexAIEmbeddings integration

> Integrate with the VertexAIEmbeddings embedding model using LangChain JavaScript.

[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundation models available in Google Cloud.

This will help you get started with Google Vertex AI [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `VertexAIEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-google-vertexai/index/VertexAIEmbeddings).

## Overview

### Integration details

| Class                                                                                                                 | Package                                                                      | Local | Py support |                                                  Downloads                                                 |                                                 Version                                                 |
| :-------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------- | :---: | :--------: | :--------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
| [`VertexAIEmbeddings`](https://reference.langchain.com/javascript/langchain-google-vertexai/index/VertexAIEmbeddings) | [`@langchain/google-vertexai`](https://npmjs.com/@langchain/google-vertexai) |   ❌   |      ✅     | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/google-vertexai?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/google-vertexai?style=flat-square\&label=%20&) |

## Setup

LangChain.js supports two different authentication methods based on whether
you're running in a Node.js environment or a web environment.

To access Vertex AI embedding models you'll need to set up Google Vertex AI in your Google Cloud Platform (GCP) account, save the credentials file, and install the `@langchain/google-vertexai` integration package. On Node.js, that package uses [`@langchain/google-gauth`](https://github.com/langchain-ai/langchainjs/tree/main/libs/providers/langchain-google-gauth) for authentication (you do not need to install it separately).

### Credentials

Head to your [GCP account](https://console.cloud.google.com/) and generate a credentials file. Once you've done this set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json"
```

Alternatively, on your local machine you can run `gcloud auth application-default login` to use Application Default Credentials.

If running in a web environment, install `@langchain/google-vertexai-web` and set `GOOGLE_WEB_CREDENTIALS` to your service account JSON string (`GOOGLE_VERTEX_AI_WEB_CREDENTIALS` is deprecated).

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 `VertexAIEmbeddings` integration lives in the `@langchain/google-vertexai` package:

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @langchain/google-vertexai @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @langchain/google-vertexai @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @langchain/google-vertexai @langchain/core
  ```
</CodeGroup>

## Instantiation

Now we can instantiate our model object and embed text:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { VertexAIEmbeddings } from "@langchain/google-vertexai";
// Uncomment the following line if you're running in a web environment:
// import { VertexAIEmbeddings } from "@langchain/google-vertexai-web"

const embeddings = new VertexAIEmbeddings({
  model: "gemini-embedding-001",
  // ...
});
```

## 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"}}
[
    -0.02831101417541504,   0.022063178941607475,  -0.07454229146242142,
    0.006448323838412762,   0.001955120824277401, -0.017617391422390938,
       0.018649872392416,   -0.05262855067849159, 0.0006953597767278552,
  -0.0018249079585075378,   0.022437218576669693, 0.0036489504855126143,
   0.0018086736090481281,   0.016940006986260414, -0.007894322276115417,
    -0.04187627509236336,   0.039501357823610306,   0.06918870657682419,
   -0.006931832991540432,   0.049655742943286896,  0.021211417391896248,
   -0.029322246089577675,   -0.04546992480754852,  -0.01769082061946392,
    0.046703994274139404,    0.03127637133002281,  0.006355373188853264,
    0.014901148155331612,  -0.006893016863614321,  -0.05992589890956879,
   -0.009733330458402634,   0.015709295868873596, -0.017982766032218933,
     -0.0852997675538063,  -0.032453566789627075, 0.0014507169835269451,
     0.03345133736729622,   0.048862338066101074,  0.006664620712399483,
    -0.06287197023630142,   -0.02109423652291298,  0.018176473677158356,
   -0.022175665944814682,    0.03340170532464981, -0.008905526250600815,
    -0.03492079675197601,   -0.03819998353719711,  -0.05230168625712395,
    -0.05247239023447037,   0.048254698514938354,  0.046494755893945694,
   -0.029708227142691612,  -0.002180763054639101,  0.051957979798316956,
    -0.05483679473400116,    0.00700812041759491,  -0.08181990683078766,
    -0.02295914851129055,   0.026530204340815544,   0.04028692841529846,
    -0.05230272561311722,  -0.057705819606781006, -0.015022763051092625,
       0.002143724123016,    0.06361843645572662, -0.027828887104988098,
    0.006870461627840996,  -0.016140831634402275, -0.034440942108631134,
   -0.004059414379298687,  -0.042537953704595566,  -0.00984653178602457,
    -0.07701274752616882,    0.09815558046102524, -0.025801729410886765,
   -0.008693721145391464, -0.0010926402173936367, -0.027235493063926697,
     0.06945550441741943,   0.023456251248717308,  -0.02160717360675335,
     0.03252667561173439,    0.05874639376997948, -0.001329384627752006,
     0.03664775192737579,   -0.07353461533784866, -0.028453022241592407,
    -0.05666429176926613,  -0.012955721467733383, -0.041723109781742096,
     0.07209191471338272,     0.0326194241642952,   -0.0496046207845211,
   -0.025037819519639015,   0.004625750705599785,  -0.03622527793049812,
   -0.022546149790287018,  0.0053468807600438595,   0.03879072889685631,
     0.03238753229379654
]
```

### 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"}}
[
    -0.02831101417541504,   0.022063178941607475,  -0.07454229146242142,
    0.006448323838412762,   0.001955120824277401, -0.017617391422390938,
       0.018649872392416,   -0.05262855067849159, 0.0006953597767278552,
  -0.0018249079585075378,   0.022437218576669693, 0.0036489504855126143,
   0.0018086736090481281,   0.016940006986260414, -0.007894322276115417,
    -0.04187627509236336,   0.039501357823610306,   0.06918870657682419,
   -0.006931832991540432,   0.049655742943286896,  0.021211417391896248,
   -0.029322246089577675,   -0.04546992480754852,  -0.01769082061946392,
    0.046703994274139404,    0.03127637133002281,  0.006355373188853264,
    0.014901148155331612,  -0.006893016863614321,  -0.05992589890956879,
   -0.009733330458402634,   0.015709295868873596, -0.017982766032218933,
     -0.0852997675538063,  -0.032453566789627075, 0.0014507169835269451,
     0.03345133736729622,   0.048862338066101074,  0.006664620712399483,
    -0.06287197023630142,   -0.02109423652291298,  0.018176473677158356,
   -0.022175665944814682,    0.03340170532464981, -0.008905526250600815,
    -0.03492079675197601,   -0.03819998353719711,  -0.05230168625712395,
    -0.05247239023447037,   0.048254698514938354,  0.046494755893945694,
   -0.029708227142691612,  -0.002180763054639101,  0.051957979798316956,
    -0.05483679473400116,    0.00700812041759491,  -0.08181990683078766,
    -0.02295914851129055,   0.026530204340815544,   0.04028692841529846,
    -0.05230272561311722,  -0.057705819606781006, -0.015022763051092625,
       0.002143724123016,    0.06361843645572662, -0.027828887104988098,
    0.006870461627840996,  -0.016140831634402275, -0.034440942108631134,
   -0.004059414379298687,  -0.042537953704595566,  -0.00984653178602457,
    -0.07701274752616882,    0.09815558046102524, -0.025801729410886765,
   -0.008693721145391464, -0.0010926402173936367, -0.027235493063926697,
     0.06945550441741943,   0.023456251248717308,  -0.02160717360675335,
     0.03252667561173439,    0.05874639376997948, -0.001329384627752006,
     0.03664775192737579,   -0.07353461533784866, -0.028453022241592407,
    -0.05666429176926613,  -0.012955721467733383, -0.041723109781742096,
     0.07209191471338272,     0.0326194241642952,   -0.0496046207845211,
   -0.025037819519639015,   0.004625750705599785,  -0.03622527793049812,
   -0.022546149790287018,  0.0053468807600438595,   0.03879072889685631,
     0.03238753229379654
]
[
  -0.00007261172140715644,    0.03209814056754112,  -0.10099327564239502,
   -0.0017932605696842074, -0.0016863049240782857,  0.009428824298083782,
     0.023065969347953796,  -0.018305035308003426,   0.03765229508280754,
      0.03357342258095741,  0.0018431750359013677,   0.03230319544672966,
     0.024983661249279976,    0.02752346731722355, -0.027390114963054657,
     -0.01945030689239502,   -0.05770668387413025,  0.046621184796094894,
     -0.03308689966797829,    0.03985097259283066, -0.021250328049063683,
    -0.001940526650287211,   -0.06034174561500549,  -0.05026412755250931,
      0.02385033667087555,   -0.03279203176498413,   0.02966252714395523,
      0.01294293999671936,  -0.009747475385665894,  -0.07896383106708527,
    -0.013269499875605106,  -0.011228476651012897,  0.022224457934498787,
    -0.018957728520035744,   -0.05092151463031769, -0.043285638093948364,
     0.016826728358864784,   0.010665969923138618,  0.021219193935394287,
     -0.08588971197605133,  -0.038367897272109985,  0.012244532816112041,
     0.009497410617768764,   0.017629485577344894, 0.0013116559712216258,
    -0.016468070447444916,   -0.04423798993229866,  -0.04043079912662506,
     -0.05485917255282402,  -0.007577189709991217,  0.028067218139767647,
    -0.022974666208028793,  0.0006999042234383523,  0.009812192991375923,
     -0.05387532711029053,  -0.016531387344002724, -0.015153753571212292,
      0.03397523611783981, -0.0018232968868687749,   0.01200891938060522,
    -0.013123664073646069,  -0.043459296226501465,  -0.01856262981891632,
     0.018269911408424377,   0.016155652701854706,  -0.05597233399748802,
     -0.05852395296096802,   0.020076945424079895, -0.033808667212724686,
    -0.008225022815167904,  -0.014589417725801468,  -0.01408824510872364,
     -0.06293410807847977,   0.026668129488825798,  -0.01397104375064373,
    -0.017627086490392685,   -0.03409220278263092, -0.018559949472546577,
      0.07163946330547333,   0.015611495822668076, -0.034166790544986725,
    -0.005098687019199133,    0.04163505882024765, -0.010681619867682457,
     0.027817489579319954,  -0.031076539307832718, -0.006825212389230728,
     -0.06810358166694641,   -0.03793689236044884,  -0.03981738165020943,
      0.09524374455213547,   -0.03607913851737976,  0.003638653317466378,
      0.02828306518495083,   0.018808560445904732, -0.047244682908058167,
     -0.06114668399095535,   -0.02395530976355076,  0.036157332360744476,
       0.0422002375125885
]
```

***

## API reference

For detailed documentation of all `VertexAIEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-google-vertexai/index/VertexAIEmbeddings).

***

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