VertexAIEmbeddings

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

Overview

Integration details

Class	Package	Local	Py support	Package downloads	Package latest
VertexAIEmbeddings	@langchain/google-vertexai	❌	✅

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 ChatVertexAI models you’ll need to setup Google VertexAI in your Google Cloud Platform (GCP) account, save the credentials file, and install the @langchain/google-vertexai integration package.

Credentials

Head to your GCP account and generate a credentials file. Once you’ve done this set the GOOGLE_APPLICATION_CREDENTIALS environment variable:

export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json"

If running in a web environment, you should set the GOOGLE_VERTEX_AI_WEB_CREDENTIALS environment variable as a JSON stringified object, and install the @langchain/google-vertexai-web package:

GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...}

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

Installation

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

tip

See this section for general instructions on installing integration packages.

npm

npm i @langchain/google-vertexai

yarn

yarn add @langchain/google-vertexai

pnpm

pnpm add @langchain/google-vertexai

Instantiation

Now we can instantiate our model object and embed text:

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: "text-embedding-004",
  // ...
});

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 working with external knowledge tutorials.

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/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

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:

const singleVector = await embeddings.embedQuery(text);

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

[
    -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:

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));

[
    -0.02831101417541504,   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://api.js.langchain.com/classes/langchain_google_vertexai.GoogleVertexAIEmbeddings.html

Related

• Embedding model conceptual guide
• Embedding model how-to guides