google-gemini-embeddings

What this skill does

# 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.

---

## Table of Contents

1. [Quick Start](#1-quick-start)
2. [gemini-embedding-001 Model](#2-gemini-embedding-001-model)
3. [Basic Embeddings](#3-basic-embeddings)
4. [Batch Embeddings](#4-batch-embeddings)
5. [Task Types](#5-task-types)
6. [RAG Patterns](#6-rag-patterns)
7. [Semantic Search](#7-semantic-search)
8. [Document Clustering](#8-document-clustering)
9. [Error Handling](#9-error-handling)
10. [Best Practices](#10-best-practices)

---

## 1. Quick Start

### Installation

Install the Google Generative AI SDK:

```bash
npm install @google/genai@^1.27.0
```

For TypeScript projects:

```bash
npm install -D typescript@^5.0.0
```

### Environment Setup

Set your Gemini API key as an environment variable:

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

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

### First Embedding Example

```typescript
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.

---

## 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

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

---

## 3. Basic Embeddings

### SDK Approach (Node.js)

**Single text embedding**:

```typescript
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**:

```typescript
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

```typescript
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
```

---

## 4. Batch Embeddings

### Multiple Texts in One Request (SDK)

Generate embeddings for multiple texts simultaneously:

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

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

const texts = [
  "What is the meaning of life?",
  "How does photosynthesis work?",
  "Tell me about the history of the internet."
];

const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  contents: texts, // Array of strings
  config: {
    taskType: 'RETRIEVAL_DOCUMENT',
    outputDimensionality: 768
  }
});

// Process each embedding
response.embeddings.forEach((embedding, index) => {
  console.log(`Text ${index}: ${texts[index]}`);
  console.log(`Embedding: ${embedding.values.slice(0, 5)}...`);
  console.log(`Dimensions: ${embedding.values.length}`);
});
```

### Batch REST API (fetch)

Use the `batchEmbedContents` endpoint:

```typescript
const response = await fetch(
  'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents',
  {
    method: 'POST',
    headers: {
      'x-goog-api-key': apiKey,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      requests: texts.map(text => ({
        model: 'models/gemini-embedding-001',
        content: {
          parts: [{ text }]
        },
        taskType: 'RETRIEVAL_DOCUMENT'
      }))
    })
  }
);

const data = await response.json();
// data.embeddings: Array of {values: number[]}
```

### Chunking for Rate Limits

When processing large datasets, chunk requests to stay within rate limits:

```typescript
async function batchEmbedWithRateLimit(
  texts: string[],
  batchSize: number = 100, // Free tier: 100 RPM
  delayMs: number = 60000 // 1 minute delay between batches
): Promise<number[][]> {
  const allEmbeddings: number[][] = [];

  for (let i = 0; i < texts.length; i += batchSize) {
    const batch = texts.slice(i, i + batchSize);

    console.log(`Processing batch ${i / batchSize + 1} (${batch.length} texts)`);

    const response = await ai.models.embedContent({
      model: 'gemini-embedding-001',
      contents: batch,
      config: {
        taskType: 'RETRIEVAL_DOCUMENT',
        outputDimensionality: 768
      }
    });

    allEmbeddings.push(...response.embeddings.map(e => e.values));

    // Wait before next batch (except last batch)
    if (i + batchSize < texts.length) {
      await new Promise(resolve => setTimeout(resolve, delayMs));
    }
  }

  return allEmbeddings;
}

// Usage
const embeddings = await batchEmbedWithRateLimit(documents, 100);
```

### Performance Optimization

**Tips**:
1. Use batch API when embedding multiple texts (single request vs multiple requests)
2. Choose low