Tune text embeddings

This page shows you how to tune the text embedding models like textembedding-gecko and textembedding-gecko-multilingual.

Foundation embedding models are pre-trained on a massive dataset of text, providing a strong baseline for many tasks. For scenarios requiring specialized knowledge or highly tailored performance, model tuning enables you to fine-tune the model's representations using your own relevant data. Tuning is supported for stable versions of textembedding-gecko and textembedding-gecko-multilingual models.

Text embedding models support supervised tuning. Supervised tuning uses labeled examples that demonstrate the type of output you'd like from your text embedding model during inference. Text embedding models don't support tuning by using Reinforcement learning from human feedback (RLHF).

To learn more about model tuning, see How model tuning works.

Expected quality improvement

Vertex AI uses a parameter efficient tuning method for customization. This methodology shows significant gains in quality of up to 41% (average 12%) on experiments performed on public retrieval benchmark datasets.

Use case for tuning an embedding model

Tuning a text embeddings model can enable your model to adapt to the embeddings to a specific domain or task. This can be useful if the pre-trained embeddings model is not well-suited to your specific needs. For example, you might fine-tune an embeddings model on a specific dataset of customer support tickets for your company. This could help a chatbot understand the different types of customer support issues your customers typically have, and be able to answer their questions more effectively. Without tuning, the model doesn't know the specifics of your customer support tickets or the solutions to specific problems for your product.

Tuning workflow

The model tuning workflow on Vertex AI for textembedding-gecko and textembedding-gecko-multilingual is as follows:

  • Prepare your model tuning dataset.
  • Upload the model tuning dataset to a Cloud Storage bucket.
  • Configure your project for Vertex AI Pipelines.
  • Create a model tuning job.
  • Deploy the tuned model to a Vertex AI endpoint of the same name. Unlike text or Codey model tuning jobs, a text embedding tuning job doesn't deploy your tuned models to a Vertex AI endpoint.

Prepare your embeddings dataset

The dataset used to tune an embeddings model includes data that align with the task that you want the model to perform.

Dataset format for tuning an embeddings model

The training dataset consists of the following files, which need to be in Cloud Storage. The path of the files are defined by parameters when launching the tuning pipeline. The three types of files are the corpus file, query file, and labels. Only train labels are necessary, but you may also provide validation and test labels for greater control.

  • Corpus file: The path is defined by parameter corpus_path. It's a JSONL file where each line has the fields _id, title, and text with string values. _id and text are required, while title is optional. Here is an example corpus.jsonl file:

    {"_id": "doc1", "title": "Get an introduction to generative AI on Vertex AI", "text": "Vertex AI's Generative AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models. Learn how you can use Generative AI Studio to test models using prompt samples, design and save prompts, tune a foundation model, and convert between speech and text."}
    {"_id": "doc2", "title": "Use gen AI for summarization, classification, and extraction", "text": "Learn how to create text prompts for handling any number of tasks with Vertex AI's generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI's PaLM API for text lets you design prompts with flexibility in terms of their structure and format."}
    {"_id": "doc3", "title": "Custom ML training overview and documentation", "text": "Get an overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions."}
    {"_id": "doc4", "text": "Text embeddings are useful for clustering, information retrieval, retrieval-augmented generation (RAG), and more."}
    {"_id": "doc5", "title": "Text embedding tuning", "text": "Google's text embedding models can be tuned on Vertex AI."}
  • Query file: The query file contains your example queries. The path is defined by the parameter queries_path. The query file is in JSONL format and has the same fields as the corpus file. Here is an example queries.jsonl file:

    {"_id": "query1", "text": "Does Vertex support generative AI?"}
    {"_id": "query2", "text": "What can I do with Vertex GenAI offerings?"}
    {"_id": "query3", "text": "How do I train my models using Vertex?"}
    {"_id": "query4", "text": "What is a text embedding?"}
    {"_id": "query5", "text": "Can text embedding models be tuned on Vertex?"}
    {"_id": "query6", "text": "embeddings"}
    {"_id": "query7", "text": "embeddings for rag"}
    {"_id": "query8", "text": "custom model training"}
    {"_id": "query9", "text": "Google Cloud PaLM API"}
  • Training labels: The path is defined by the parameter train_label_path. The train_label_path is the Cloud Storage URI to the train label data location and is specified when you create your tuning job. The labels need to be a TSV file with a header. A subset of the queries and the corpus need be included in your training labels file. The file must have the columns query-id, corpus-id and score. The query-id is a string that matches the _id key from the query file, the corpus-id is a string that matches the _id in the corpus file. Score is a non-negative integer value. Any score greater than zero indicates that the document is related to the query. Larger numbers indicate a greater level of relevance. If the score is omitted, the default value is 1. Here is an example train_labels.tsv file:

    query-id  corpus-id   score
    query1    doc1    1
    query2    doc2    1
    query3    doc3    2
    query3    doc5  1
    query4    doc4  1
    query4    doc5  1
    query5    doc5  2
    query6    doc4  1
    query6    doc5  1
    query7    doc4  1
    query8    doc3  1
    query9    doc2  1
  • Test labels: Optional. The test labels have the same format as the training labels and are specified by the test_label_path parameter. If no test_label_path is provided, the test labels will be autosplit from the training labels.

  • Validation labels: Optional. The validation labels have the same format as the training labels and are specified by the validation_label_path parameter. If no validation_label_path is provided, the validation labels will be autosplit from the training labels.

Dataset size requirements

The provided dataset files must meet the following constraints:

  • The number of queries must be between 9 and 40,000.
  • The number of documents in the corpus must be between 9 and 500,000.
  • Each dataset label file must include at least 3 query IDs, and across all dataset splits there must be at least 9 query IDs.
  • The total number of labels must be less than 500,000.

Configure your project for Vertex AI Pipelines

Tuning is executed within your project using the Vertex AI Pipelines platform.

Configuring permissions

The pipeline executes training code under two service agents. These service agents must be granted specific roles in order to begin training using your project and dataset.

Compute Engine default service account

This service account requires:

  • Storage Object Viewer access to each dataset file you created in Cloud Storage.

  • Storage Object User access to the output Cloud Storage directory of your pipeline, PIPELINE_OUTPUT_DIRECTORY.

  • Vertex AI User access to your project.

Instead of the Compute Engine default service account, you can specify a custom service account. For more information, see Configure a service account with granular permissions.

Vertex AI Tuning Service Agent

This service account requires:

  • Storage Object Viewer access to each dataset file you created in Cloud Storage.

  • Storage Object User access to the output Cloud Storage directory of your pipeline, PIPELINE_OUTPUT_DIRECTORY.

For more information about configuring Cloud Storage dataset permissions, see Configure a Cloud Storage bucket for pipeline artifacts.

Using Accelerators

Tuning requires GPU accelerators. Any of the following accelerators can be used for the text embedding tuning pipeline:


Launching a tuning job requires adequate Restricted image training GPUs quota for the accelerator type and region you have selected, for example Restricted image training Nvidia V100 GPUs per region. To increase the quota of your project, see request additional quota.

Not all accelerators are available in all regions. See Using accelerators in Vertex AI for more information.

Create an embedding model tuning job

You can create an embedding model tuning job by using the Google Cloud console or REST API.


To create an embedding model tuning job, use the projects.locations.pipelineJobs.create method.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: Your Google Cloud project ID.
  • PIPELINE_OUTPUT_DIRECTORY: Path for the pipeline output artifacts, starting with "gs://".

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/pipelineJobs

Request JSON body:

  "displayName": "tune_text_embeddings_model_sample",
  "runtimeConfig": {
    "gcsOutputDirectory": "PIPELINE_OUTPUT_DIRECTORY",
    "parameterValues": {
      "corpus_path": "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/corpus.jsonl",
      "queries_path": "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/queries.jsonl",
      "train_label_path": "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/train.tsv",
      "test_label_path": "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/test.tsv",
      "task_type": "DEFAULT",
      "batch_size": "128",
      "train_steps": "1000",
      "output_dimensionality": "768",
      "learning_rate_multiplier": "1.0"
  "templateUri": "https://us-kfp.pkg.dev/ml-pipeline/llm-text-embedding/tune-text-embedding-model/v1.1.3"

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

After launching the pipeline, follow the progress of your tuning job through the Google Cloud console.

Go to Google Cloud console


To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import re

from google.cloud.aiplatform import initializer as aiplatform_init
from vertexai.preview.language_models import TextEmbeddingModel

def tune_embedding_model(
    api_endpoint: str,
    base_model_name: str = "text-embedding-004",
    task_type: str = "DEFAULT",
    corpus_path: str = "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/corpus.jsonl",
    queries_path: str = "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/queries.jsonl",
    train_label_path: str = "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/train.tsv",
    test_label_path: str = "gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/test.tsv",
    batch_size: int = 128,
    train_steps: int = 1000,
    output_dimensionality: int = 768,
    learning_rate_multiplier: float = 1.0,
):  # noqa: ANN201
    match = re.search(r"^(\w+-\w+)", api_endpoint)
    location = match.group(1) if match else "us-central1"
    base_model = TextEmbeddingModel.from_pretrained(base_model_name)
    tuning_job = base_model.tune_model(
    return tuning_job


Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.aiplatform.v1.CreatePipelineJobRequest;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.PipelineJob;
import com.google.cloud.aiplatform.v1.PipelineJob.RuntimeConfig;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.protobuf.Value;
import java.io.IOException;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

public class EmbeddingModelTuningSample {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running this sample.
    String apiEndpoint = "us-central1-aiplatform.googleapis.com:443";
    String project = "PROJECT";
    String baseModelVersionId = "BASE_MODEL_VERSION_ID";
    String taskType = "DEFAULT";
    String pipelineJobDisplayName = "PIPELINE_JOB_DISPLAY_NAME";
    String outputDir = "OUTPUT_DIR";
    String queriesPath = "QUERIES_PATH";
    String corpusPath = "CORPUS_PATH";
    String trainLabelPath = "TRAIN_LABEL_PATH";
    String testLabelPath = "TEST_LABEL_PATH";
    double learningRateMultiplier = 1.0;
    int outputDimensionality = 768;
    int batchSize = 128;
    int trainSteps = 1000;


  public static PipelineJob createEmbeddingModelTuningPipelineJob(
      String apiEndpoint,
      String project,
      String baseModelVersionId,
      String taskType,
      String pipelineJobDisplayName,
      String outputDir,
      String queriesPath,
      String corpusPath,
      String trainLabelPath,
      String testLabelPath,
      double learningRateMultiplier,
      int outputDimensionality,
      int batchSize,
      int trainSteps)
      throws IOException {
    Matcher matcher = Pattern.compile("^(?<Location>\\w+-\\w+)").matcher(apiEndpoint);
    String location = matcher.matches() ? matcher.group("Location") : "us-central1";
    String templateUri =
    PipelineServiceSettings settings =
    try (PipelineServiceClient client = PipelineServiceClient.create(settings)) {
      Map<String, Value> parameterValues =
              "base_model_version_id", valueOf(baseModelVersionId),
              "task_type", valueOf(taskType),
              "queries_path", valueOf(queriesPath),
              "corpus_path", valueOf(corpusPath),
              "train_label_path", valueOf(trainLabelPath),
              "test_label_path", valueOf(testLabelPath),
              "learning_rate_multiplier", valueOf(learningRateMultiplier),
              "output_dimensionality", valueOf(outputDimensionality),
              "batch_size", valueOf(batchSize),
              "train_steps", valueOf(trainSteps));
      PipelineJob pipelineJob =
      CreatePipelineJobRequest request =
              .setParent(LocationName.of(project, location).toString())
      return client.createPipelineJob(request);

  private static Value valueOf(String s) {
    return Value.newBuilder().setStringValue(s).build();

  private static Value valueOf(int n) {
    return Value.newBuilder().setNumberValue(n).build();

  private static Value valueOf(double n) {
    return Value.newBuilder().setNumberValue(n).build();


Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

async function main(
  pipelineJobDisplayName = 'embedding-customization-pipeline-sample',
  baseModelVersionId = 'text-embedding-004',
  taskType = 'DEFAULT',
  corpusPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/corpus.jsonl',
  queriesPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/queries.jsonl',
  trainLabelPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/train.tsv',
  testLabelPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/test.tsv',
  outputDimensionality = 768,
  learningRateMultiplier = 1.0,
  batchSize = 128,
  trainSteps = 1000
) {
  const aiplatform = require('@google-cloud/aiplatform');
  const {PipelineServiceClient} = aiplatform.v1;
  const {helpers} = aiplatform; // helps construct protobuf.Value objects.

  const client = new PipelineServiceClient({apiEndpoint});
  const match = apiEndpoint.match(/(?<L>\w+-\w+)/);
  const location = match ? match.groups.L : 'us-central1';
  const parent = `projects/${project}/locations/${location}`;
  const params = {
    base_model_version_id: baseModelVersionId,
    task_type: taskType,
    queries_path: queriesPath,
    corpus_path: corpusPath,
    train_label_path: trainLabelPath,
    test_label_path: testLabelPath,
    batch_size: batchSize,
    train_steps: trainSteps,
    output_dimensionality: outputDimensionality,
    learning_rate_multiplier: learningRateMultiplier,
  const runtimeConfig = {
    gcsOutputDirectory: outputDir,
    parameterValues: Object.fromEntries(
      Object.entries(params).map(([k, v]) => [k, helpers.toValue(v)])
  const pipelineJob = {
    displayName: pipelineJobDisplayName,
  async function createTuneJob() {
    const [response] = await client.createPipelineJob({parent, pipelineJob});
    console.log(`job_name: ${response.name}`);
    console.log(`job_state: ${response.state}`);

  await createTuneJob();


To tune a text embedding model by using the Google Cloud console, you can launch a customization pipeline using the following steps:

  1. In the Vertex AI section of the Google Cloud console, go to the Vertex AI Pipelines page.

    Go to Vertex AI Pipelines

  2. Click Create run to open the Create pipeline run pane.
  3. Click Select from existing pipelines and enter the following details:
    1. Select "ml-pipeline" from the select a resource drop-down.
    2. Select "llm-text-embedding" from the Repository drop-down.
    3. Select "tune-text-embedding-model" from the Pipeline or component drop-down.
    4. Select the version labeled "v1.1.3" from the Version drop-down.
  4. Specify a Run name to uniquely identify the pipeline run.
  5. In the Region drop-down list, select the region to create the pipeline run, which will be the same region in which your tuned model is created.
  6. Click Continue. The Runtime configuration pane appears.
  7. Under Cloud storage location, click Browse to select the Cloud Storage bucket for storing the pipeline output artifacts, and then click Select.
  8. Under Pipeline parameters, specify your parameters for the tuning pipeline. The three required parameters are corpus_path, queries_path, and train_label_path, with formats described in Prepare your embeddings dataset. For more detailed information about each parameter, refer to the REST tab of this section.
  9. Click Submit to create your pipeline run.

Use your tuned model

View tuned models in Model Registry

When your tuning job completes, the tuned model isn't automatically deployed to an endpoint. It will be available as a Model resource in Model Registry. You can view a list of models in your current project, including your tuned models, by using the Google Cloud console.

To view your tuned models in the Google Cloud console, go to the Vertex AI Model Registry page.

Go to Vertex AI Model Registry

Deploy your model

After you've tuned the embeddings model, you need to deploy the Model resource. To deploy your tuned embeddings model, see Deploy a model to an endpoint.

Unlike foundation models, tuned text embedding models are managed by the user. This includes managing serving resources, like machine type and accelerators. To prevent out-of-memory errors during prediction, it's recommended that you deploy using the NVIDIA_TESLA_A100 GPU type, which can support batch sizes up to 5 for any input length.

Similar to the textembedding-gecko foundation model, your tuned model supports up to 3072 tokens and can truncate longer inputs.

Get predictions on a deployed model

Once your tuned model is deployed, you can use one of the following commands to issue requests to the tuned model endpoint.

Example curl commands for tuned textembedding-gecko@001 models

To get predictions from a tuned version of textembedding-gecko@001, use the example curl command below.


curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json"  \
    ${ENDPOINT_URI}/v1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/${MODEL_ENDPOINT}:predict \
    -d '{
  "instances": [
      "content": "Dining in New York City"
      "content": "Best resorts on the east coast"

Example curl commands for non textembedding-gecko@001 models

Tuned versions of other models (for example, textembedding-gecko@003 and textembedding-gecko-multilingual@001) require 2 additional inputs: task_type and title. More documentation for these parameters can be found at curl command


curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json"  \
    ${ENDPOINT_URI}/v1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/${MODEL_ENDPOINT}:predict \
    -d '{
  "instances": [
      "content": "Dining in New York City",
      "task_type": "DEFAULT",
      "title": ""
      "content": "There are many resorts to choose from on the East coast...",
      "task_type": "RETRIEVAL_DOCUMENT",
      "title": "East Coast Resorts"

Example output

This output applies to both textembedding-gecko and textembedding-gecko-multilingual models, regardless of version.

 "predictions": [
   [ ... ],
   [ ... ],
 "deployedModelId": "...",
 "model": "projects/.../locations/.../models/...",
 "modelDisplayName": "tuned-text-embedding-model",
 "modelVersionId": "1"

What's next