> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-ramonn-1789138246-3410f5f.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# VertexAIEmbeddings integration

> Integrate with the VertexAIEmbeddings embedding model using LangChain JavaScript.

[Gemini Enterprise Agent Platform](https://cloud.google.com/products/gemini-enterprise-agent-platform) is a service that exposes all foundation models available in Google Cloud.

This will help you get started with Gemini Enterprise Agent Platform [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 Gemini Enterprise Agent Platform embedding models you'll need to set up Gemini Enterprise Agent Platform 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={null}
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={null}
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### Installation

The LangChain Gemini Enterprise Agent Platform embeddings integration lives in the `@langchain/google-vertexai` package:

<CodeGroup>
  ```bash npm theme={null}
  npm install @langchain/google-vertexai @langchain/core
  ```

  ```bash yarn theme={null}
  yarn add @langchain/google-vertexai @langchain/core
  ```

  ```bash pnpm theme={null}
  pnpm add @langchain/google-vertexai @langchain/core
  ```
</CodeGroup>

## Instantiation

Now we can instantiate our model object and embed text:

```typescript theme={null}
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={null}
// 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={null}
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={null}
const singleVector = await embeddings.embedQuery(text);

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

```text theme={null}
[
    -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={null}
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={null}
[
    -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).

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/embeddings/google_vertex_ai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>