Google Gemini Embeddings

Complete production-ready guide for Google Gemini embeddings API

This skill provides comprehensive coverage of the gemini-embedding-001 model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering.

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Table of Contents

1. Quick Start
2. gemini-embedding-001 Model
3. Basic Embeddings
4. Batch Embeddings
5. Task Types
6. RAG Patterns
7. Error Handling
8. Best Practices

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1. Quick Start

Installation

Install the Google Generative AI SDK:

npm install @google/genai@^1.37.0

For TypeScript projects:

npm install -D typescript@^5.0.0

Environment Setup

Set your Gemini API key as an environment variable:

export GEMINI_API_KEY="your-api-key-here"

Get your API key from: https://aistudio.google.com/apikey

First Embedding Example

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

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: 'What is the meaning of life?',
  config: {
    taskType: 'RETRIEVAL_QUERY',
    outputDimensionality: 768
  }
});

console.log(response.embedding.values); // [0.012, -0.034, ...]
console.log(response.embedding.values.length); // 768

Result: A 768-dimension embedding vector representing the semantic meaning of the text.

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2. gemini-embedding-001 Model

Model Specifications

Current Model: gemini-embedding-001 (stable, production-ready)

• Status: Stable
• Experimental: gemini-embedding-exp-03-07 (deprecated October 2025, do not use)

Dimensions

The model supports flexible output dimensionality using Matryoshka Representation Learning:

Dimension	Use Case	Storage	Performance
768	Recommended for most use cases	Low	Fast
1536	Balance between accuracy and efficiency	Medium	Medium
3072	Maximum accuracy (default)	High	Slower
128-3071	Custom (any value in range)	Variable	Variable

Default: 3072 dimensions Recommended: 768, 1536, or 3072 for optimal performance

Context Window

• Input Limit: 2,048 tokens per text
• Input Type: Text only (no images, audio, or video)

Rate Limits

Tier	RPM	TPM	RPD	Requirements
Free	100	30,000	1,000	No billing account
Tier 1	3,000	1,000,000	-	Billing account linked
Tier 2	5,000	5,000,000	-	$250+ spending, 30-day wait
Tier 3	10,000	10,000,000	-	$1,000+ spending, 30-day wait

RPM = Requests Per Minute TPM = Tokens Per Minute RPD = Requests Per Day

Output Format

{
  embedding: {
    values: number[] // Array of floating-point numbers
  }
}

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3. Basic Embeddings

SDK Approach (Node.js)

Single text embedding:

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

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: 'The quick brown fox jumps over the lazy dog',
  config: {
    taskType: 'SEMANTIC_SIMILARITY',
    outputDimensionality: 768
  }
});

console.log(response.embedding.values);
// [0.00388, -0.00762, 0.01543, ...]

Fetch Approach (Cloudflare Workers)

For Workers/edge environments without SDK support:

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const apiKey = env.GEMINI_API_KEY;
    const text = "What is the meaning of life?";

    const response = await fetch(
      'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent',
      {
        method: 'POST',
        headers: {
          'x-goog-api-key': apiKey,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({
          content: {
            parts: [{ text }]
          },
          taskType: 'RETRIEVAL_QUERY',
          outputDimensionality: 768
        })
      }
    );

    const data = await response.json();

    // Response format:
    // {
    //   embedding: {
    //     values: [0.012, -0.034, ...]
    //   }
    // }

    return new Response(JSON.stringify(data), {
      headers: { 'Content-Type': 'application/json' }
    });
  }
};

Response Parsing

interface EmbeddingResponse {
  embedding: {
    values: number[];
  };
}

const response: EmbeddingResponse = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: 'Sample text',
  config: { taskType: 'SEMANTIC_SIMILARITY' }
});

const embedding: number[] = response.embedding.values;
const dimensions: number = embedding.length; // 3072 by default

Normalization Requirement

⚠️ CRITICAL: When using dimensions other than 3072, you MUST normalize embeddings before computing similarity. Only 3072-dimensional embeddings are pre-normalized by the API.

Why This Matters: Non-normalized embeddings have varying magnitudes that distort cosine similarity calculations, leading to incorrect search results.

Normalization Helper Function:

/**
 * Normalize embedding vector for accurate similarity calculations.
 * REQUIRED for dimensions other than 3072.
 *
 * @param vector - Embedding values from API response
 * @returns Normalized vector (unit length)
 */
function normalize(vector: number[]): number[] {
  const magnitude = Math.sqrt(
    vector.reduce((sum, val) => sum + val * val, 0)
  );
  return vector.map(val =>