Embeddings

The Gemini API supports several embedding models that generate embeddings for words, phrases, code, and sentences. The resulting embeddings can then be used for tasks such as semantic search, text classification, and clustering, among many others.

What are embeddings?

Embeddings are numerical representations of text (or other media formats) that capture relationships between inputs. Text embeddings work by converting text into arrays of floating point numbers, called vectors. These vectors are designed to capture the meaning of the text. The length of the embedding array is called the vector's dimensionality. A passage of text might be represented by a vector containing hundreds of dimensions.

Embeddings capture semantic meaning and context, which results in text with similar meanings having "closer" embeddings. For example, the sentence "I took my dog to the vet" and "I took my cat to the vet" would have embeddings that are close to each other in the vector space.

You can use embeddings to compare different texts and understand how they relate. For example, if the embeddings of the text "cat" and "dog" are close together you can infer that these words are similar in meaning, context, or both. This enables a variety of common AI use cases.

Before you begin

Before calling the Gemini API, ensure you have your SDK of choice installed, and a Gemini API key configured and ready to use.

Generate embeddings

Use the embedContent method to generate text embeddings:

from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")

result = client.models.embed_content(
        model="gemini-embedding-exp-03-07",
        contents="What is the meaning of life?")

print(result.embeddings)

import { GoogleGenAI } from "@google/genai";

async function main() {

    const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

    const response = await ai.models.embedContent({
        model: 'gemini-embedding-exp-03-07',
        contents: 'What is the meaning of life?',
    });

    console.log(response.embeddings);
}

main();

ctx := context.Background()

client, err := genai.NewClient(ctx, option.WithAPIKey(os.Getenv("GEMINI_API_KEY")))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

em := client.EmbeddingModel("gemini-embedding-exp-03-07")
res, err := em.EmbedContent(ctx, genai.Text("What is the meaning of life?"))

if err != nil {
    panic(err)
}
fmt.Println(res.Embedding.Values)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-exp-03-07:embedContent?key=$GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"model": "models/gemini-embedding-exp-03-07",
     "content": {
     "parts":[{
     "text": "What is the meaning of life?"}]}
    }'

You can also generate embeddings for multiple chunks at once by passing them in as a list of strings.

Task types

When building Retrieval Augmented Generation (RAG) systems, a common design is to use text embeddings to perform a similarity search. In some cases this can lead to degraded quality, because questions and their answers are not semantically similar. For example, a question like "Why is the sky blue?" and its answer "The scattering of sunlight causes the blue color," have distinctly different meanings as statements, which means that a RAG system won't automatically recognize their relation.

Task types enable you to generate optimized embeddings for specific tasks, saving you time and cost and improving performance.

from google import genai
from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

result = client.models.embed_content(
        model="gemini-embedding-exp-03-07",
        contents="What is the meaning of life?",
        config=types.EmbedContentConfig(task_type="SEMANTIC_SIMILARITY")
)
print(result.embeddings)

import { GoogleGenAI } from "@google/genai";

async function main() {

    const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

    const response = await ai.models.embedContent({
        model: 'gemini-embedding-exp-03-07',
        contents: 'What is the meaning of life?',
        config: {
            taskType: "SEMANTIC_SIMILARITY",
        }
    });

    console.log(response.embeddings);
}

main();

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-exp-03-07:embedContent?key=$GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"model": "models/gemini-embedding-exp-03-07",
     "content": {
     "parts":[{
     "text": "What is the meaning of life?"}]},
     "taskType": "SEMANTIC_SIMILARITY"
    }'

Supported task types

Use cases

Text embeddings are used in a variety of common AI use cases, such as:

• Information retrieval: You can use embeddings to retrieve semantically similar text given a piece of input text.

  Document search tutorialtask

• Clustering: Comparing groups of embeddings can help identify hidden trends.

  Embedding clustering tutorialbubble_chart

• Vector database: As you take different embedding use cases to production, it is common to store embeddings in a vector database.

  Vector database tutorialbolt

• Classification: You can train a model using embeddings to classify documents into categories.

  Classification tutorialtoken

Embedding models

The Gemini API offers three models that generate text embeddings:

• gemini-embedding-exp-03-07
• text-embedding-004
• embedding-001

What's next

Check out the embeddings quickstart notebook.