Get text embeddings

This document describes how to create a text embedding using the Vertex AI Text embeddings API.

Vertex AI text embeddings API uses dense vector representations: gemini-embedding-001, for example, uses 3072-dimensional vectors. Dense vector embedding models use deep-learning methods similar to the ones used by large language models. Unlike sparse vectors, which tend to directly map words to numbers, dense vectors are designed to better represent the meaning of a piece of text. The benefit of using dense vector embeddings in generative AI is that instead of searching for direct word or syntax matches, you can better search for passages that align to the meaning of the query, even if the passages don't use the same language.

The vectors are normalized, so you can use cosine similarity, dot product, or Euclidean distance to provide the same similarity rankings.

• To learn more about embeddings, see the embeddings APIs overview.
• To learn about text embedding models, see Text embeddings.
• For information about which languages each embeddings model supports, see Supported text languages.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Enable the Vertex AI API.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Enable the Vertex AI API.

Enable the API

1. Choose a task type for your embeddings job.

Supported models

You can get text embeddings by using the following models:

For superior embedding quality, gemini-embedding-001 is our large model designed to provide the highest performance. Note that gemini-embedding-001 supports one instance per request.

Get text embeddings for a snippet of text

You can get text embeddings for a snippet of text by using the Vertex AI API or the Vertex AI SDK for Python.

API limits

For each request, you're limited to 250 input texts. The API has a maximum input token limit of 20,000. Inputs exceeding this limit result in a 400 error. Each individual input text is further limited to 2048 tokens; any excess is silently truncated. You can also disable silent truncation by setting autoTruncate to false.

For more information, see Text embedding limits.

Choose an embedding dimension

All models produce a full-length embedding vector by default. For gemini-embedding-001, this vector has 3072 dimensions, and other models produce 768-dimensional vectors. However, by using the output_dimensionality parameter, users can control the size of the output embedding vector. Selecting a smaller output dimensionality can save storage space and increase computational efficiency for downstream applications, while sacrificing little in terms of quality.

The following examples use the gemini-embedding-001 model.

pip install --upgrade google-genai

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

from google import genai
from google.genai.types import EmbedContentConfig

client = genai.Client()
response = client.models.embed_content(
    model="gemini-embedding-001",
    contents=[
        "How do I get a driver's license/learner's permit?",
        "How long is my driver's license valid for?",
        "Driver's knowledge test study guide",
    ],
    config=EmbedContentConfig(
        task_type="RETRIEVAL_DOCUMENT",  # Optional
        output_dimensionality=3072,  # Optional
        title="Driver's License",  # Optional
    ),
)
print(response)
# Example response:
# embeddings=[ContentEmbedding(values=[-0.06302902102470398, 0.00928034819662571, 0.014716853387653828, -0.028747491538524628, ... ],
# statistics=ContentEmbeddingStatistics(truncated=False, token_count=13.0))]
# metadata=EmbedContentMetadata(billable_character_count=112)

Add an embedding to a vector database

After you've generated your embedding you can add embeddings to a vector database, like Vector Search. This enables low-latency retrieval, and is critical as the size of your data increases.

To learn more about Vector Search, see Overview of Vector Search.

What's next

• To learn more about rate limits, see Generative AI on Vertex AI rate limits.
• To get batch predictions for embeddings, see Get batch text embeddings predictions
• To learn more about multimodal embeddings, see Get multimodal embeddings
• To tune an embedding, see Tune text embeddings
• To learn more about the research behind text-embedding-005 and text-multilingual-embedding-002, see the research paper Gecko: Versatile Text Embeddings Distilled from Large Language Models.