Get text embeddings

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

Text embeddings are dense vector representations of text. For example, the gemini-embedding-001 model generates 3072-dimensional vectors for a given piece of text. These dense vector embeddings are created using deep-learning methods similar to those used by large language models.

Unlike sparse vectors that map words to numbers, dense vectors are designed to represent the semantic meaning of text. The primary benefit of using dense vector embeddings is the ability to perform semantic search. Instead of searching for direct word or syntax matches, you can search for text passages that align with the meaning of your query, even if the passages don't use the same phrasing.

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

Questions and their answers aren't semantically similar
Figure 1. Get Text Embedding.

Before you begin

  1. 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.

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

    Go to project selector

  3. Enable the Vertex AI API.

    Enable the API

    4.

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

    Go to project selector

  5. Enable the Vertex AI API.

    Enable the API

    6.
  6. Choose a task type for your embeddings job.

Supported models

You can get text embeddings by using the following models:

Model name Description Output Dimensions Max sequence length Supported text languages
gemini-embedding-001 State-of-the-art performance across English, multilingual and code tasks. It unifies the previously specialized models like text-embedding-005 and text-multilingual-embedding-002 and achieves better performance in their respective domains. Read our Tech Report for more detail. up to 3072 2048 tokens Supported text languages
text-embedding-005 Specialized in English and code tasks. up to 768 2048 tokens English
text-multilingual-embedding-002 Specialized in multilingual tasks. up to 768 2048 tokens Supported text languages

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 for non-Gemini models, and a single input text for Gemini Embedding models. 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.

Gen AI SDK for Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI: 

# 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?",
    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