Embeddings

The Gemini API offers embedding models to generate embeddings for text, images, video, and other content. These resulting embeddings can then be used for tasks such as semantic search, classification, and clustering, providing more accurate, context-aware results than keyword-based approaches.

The latest model, gemini-embedding-2, is the first multimodal embedding model in the Gemini API. It maps text, images, video, audio, and documents into a unified embedding space, enabling cross-modal search, classification, and clustering across over 100 languages. See the multimodal embeddings section to learn more. For text-only use cases, gemini-embedding-001 remains available.

Building Retrieval Augmented Generation (RAG) systems is a common use case for AI products. Embeddings play a key role in significantly enhancing model outputs with improved factual accuracy, coherence, and contextual richness. If you prefer to use a managed RAG solution, we built the File Search tool which makes doing RAG easier to manage and more cost effective.

Generating embeddings

Use the embedContent method to generate text embeddings:

from google import genai

client = genai.Client()

result = client.models.embed_content(
        model="gemini-embedding-2",
        contents="What is the meaning of life?"
)

print(result.embeddings)

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

async function main() {

    const ai = new GoogleGenAI({});

    const response = await ai.models.embedContent({
        model: 'gemini-embedding-2',
        contents: 'What is the meaning of life?',
    });

    console.log(response.embeddings);
}

main();

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    contents := []*genai.Content{
        genai.NewContentFromText("What is the meaning of life?", genai.RoleUser),
    }
    result,