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

---

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.

---

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
  }
}

---

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 => val / magnitude);
}

// Usage with 768 or 1536 dimensions
const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text,
  config: {
    taskType: 'RETRIEVAL_QUERY',
    outputDimensionality: 768  // NOT 3072
  }
});

// ❌ WRONG - Use raw values directly
const embedding = response.embedding.values;
await vectorize.insert([{ id, values: embedding }]);

// ✅ CORRECT - Normalize first
const normalized = normalize(response.embedding.values);
await vectorize.insert([{ id, values: normalized }]);

Source: Official Embeddings Documentation

---

4. Batch Embeddings

Multiple Texts in One Request (SDK)

Generate embeddings for multiple texts simultaneously:

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:

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[]}

Batch API Known Issues

⚠️ Ordering Bug (December 2025): Batch API may not preserve ordering with large batch sizes (>500 texts).

• Symptom: Entry 328 appears at position 628 (silent data corruption)
• Impact: Results cannot be reliably matched back to input texts
• Workaround: Process smaller batches (<100 texts) or add unique IDs to verify ordering
• Status: Acknowledged by Google, internal bug created (P0 priority)
• Source: GitHub Issue #1207

⚠️ Memory Limit (December 2025): Large batches (>10k embeddings) can cause ERR_STRING_TOO_LONG crash.

• Error: Cannot create a string longer than 0x1fffffe8 characters
• Cause: API response includes excessive whitespace (~536MB limit)
• Workaround: Limit to <5,000 texts per batch
• Source: GitHub Issue #1205

⚠️ Rate Limit Anomaly (January 2026): Batch API may return 429 RESOURCE_EXHAUSTED even when under quota.

• Status: Under investigation by Google team
• Workaround: Implement exponential backoff and retry logic
• Source: GitHub Issue #1264

Chunking for Rate Limits

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

async function batchEmbedWithRateLimit(
  texts: string[],
  batchSize: number = 50, // REDUCED from 100 due to ordering bug
  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, 50);

Performance Optimization

Tips:

1. Use batch API when embedding multiple texts (single request vs multiple requests)
2. Choose lower dimensions (768) for faster processing and less storage
3. Implement exponential backoff for rate limit errors
4. Cache embeddings to avoid redundant API calls

---

5. Task Types

The taskType parameter optimizes embeddings for specific use cases. Always specify a task type for best results.

Available Task Types (8 total)

Task Type	Use Case	Example
RETRIEVAL_QUERY	User search queries	"How do I fix a flat tire?"
RETRIEVAL_DOCUMENT	Documents to be indexed/searched	Product descriptions, articles
SEMANTIC_SIMILARITY	Comparing text similarity	Duplicate detection, clustering
CLASSIFICATION	Categorizing texts	Spam detection, sentiment analysis
CLUSTERING	Grouping similar texts	Topic modeling, content organization
CODE_RETRIEVAL_QUERY	Code search queries	"function to sort array"
QUESTION_ANSWERING	Questions seeking answers	FAQ matching
FACT_VERIFICATION	Verifying claims with evidence	Fact-checking systems

When to Use Which

RAG Systems (Retrieval Augmented Generation):

// When embedding user queries
const queryEmbedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: userQuery,
  config: { taskType: 'RETRIEVAL_QUERY' } // ← Use RETRIEVAL_QUERY
});

// When embedding documents for indexing
const docEmbedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: documentText,
  config: { taskType: 'RETRIEVAL_DOCUMENT' } // ← Use RETRIEVAL_DOCUMENT
});

Semantic Search:

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

Document Clustering:

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

Impact on Quality

Using the correct task type significantly improves retrieval quality:

// ❌ BAD: No task type specified
const embedding1 = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: userQuery
});

// ✅ GOOD: Task type specified
const embedding2 = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: userQuery,
  config: { taskType: 'RETRIEVAL_QUERY' }
});

Result: Using the right task type can improve search relevance by 10-30%.

---

6. RAG Patterns

RAG (Retrieval Augmented Generation) combines vector search with LLM generation to create AI systems that answer questions using custom knowledge bases.

Document Ingestion Pipeline

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

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

// Generate embeddings for chunks
async function embedChunks(chunks: string[]): Promise<number[][]> {
  const response = await ai.models.embedContent({
    model: 'gemini-embedding-001',
    contents: chunks,
    config: {
      taskType: 'RETRIEVAL_DOCUMENT', // ← Documents for indexing
      outputDimensionality: 768 // ← Match Vectorize index dimensions
    }
  });

  return response.embeddings.map(e => e.values);
}

// Store in Cloudflare Vectorize
async function storeInVectorize(
  env: Env,
  chunks: string[],
  embeddings: number[][]
) {
  const vectors = chunks.map((chunk, i) => ({
    id: `doc-${Date.now()}-${i}`,
    values: embeddings[i],
    metadata: { text: chunk }
  }));

  await env.VECTORIZE.insert(vectors);
}

Query Flow (Retrieve + Generate)

async function ragQuery(env: Env, userQuery: string): Promise<string> {
  // 1. Embed user query
  const queryResponse = await ai.models.embedContent({
    model: 'gemini-embedding-001',
    content: userQuery,
    config: {
      taskType: 'RETRIEVAL_QUERY', // ← Query, not document
      outputDimensionality: 768
    }
  });

  const queryEmbedding = queryResponse.embedding.values;

  // 2. Search Vectorize for similar documents
  const results = await env.VECTORIZE.query(queryEmbedding, {
    topK: 5,
    returnMetadata: true
  });

  // 3. Extract context from top results
  const context = results.matches
    .map(match => match.metadata.text)
    .join('\n\n');

  // 4. Generate response with context
  const response = await ai.models.generateContent({
    model: 'gemini-2.5-flash',
    contents: `Context:\n${context}\n\nQuestion: ${userQuery}\n\nAnswer based on the context above:`
  });

  return response.text;
}

Integration with Cloudflare Vectorize

Create Vectorize Index (768 dimensions for Gemini):

npx wrangler vectorize create gemini-embeddings --dimensions 768 --metric cosine

Bind in wrangler.jsonc:

{
  "name": "my-rag-app",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-25",
  "vectorize": {
    "bindings": [
      {
        "binding": "VECTORIZE",
        "index_name": "gemini-embeddings"
      }
    ]
  }
}

Complete RAG Worker:

See templates/rag-with-vectorize.ts for full implementation.

---

7. Error Handling

Common Errors

1. API Key Missing or Invalid

// ❌ Error: API key not set
const ai = new GoogleGenAI({});

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

if (!process.env.GEMINI_API_KEY) {
  throw new Error('GEMINI_API_KEY environment variable not set');
}

2. Dimension Mismatch

// ❌ Error: Embedding has 3072 dims, Vectorize expects 768
const embedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text
  // No outputDimensionality specified → defaults to 3072
});

await env.VECTORIZE.insert([{
  id: '1',
  values: embedding.embedding.values // 3072 dims, but index is 768!
}]);

// ✅ Correct: Match dimensions
const embedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text,
  config: { outputDimensionality: 768 } // ← Match index dimensions
});

3. Rate Limiting

// ❌ Error: 429 Too Many Requests
for (let i = 0; i < 1000; i++) {
  await ai.models.embedContent({ /* ... */ }); // Exceeds 100 RPM on free tier
}

// ✅ Correct: Implement rate limiting
async function embedWithRetry(text: string, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await ai.models.embedContent({
        model: 'gemini-embedding-001',
        content: text,
        config: { taskType: 'SEMANTIC_SIMILARITY' }
      });
    } catch (error: any) {
      if (error.status === 429 && attempt < maxRetries - 1) {
        const delay = Math.pow(2, attempt) * 1000; // Exponential backoff
        await new Promise(resolve => setTimeout(resolve, delay));
        continue;
      }
      throw error;
    }
  }
}

See references/top-errors.md for all 8 documented errors with detailed solutions.

Known Issues Prevention

This section documents additional issues discovered in production use (beyond basic errors above).

Issue #9: Normalization Required for Non-3072 Dimensions

Error: Incorrect similarity scores, no error thrown Source: Official Embeddings Documentation Why It Happens: Only 3072-dimensional embeddings are pre-normalized by the API. All other dimensions (128-3071) have varying magnitudes that distort cosine similarity. Prevention: Always normalize embeddings when using dimensions other than 3072.

function normalize(vector: number[]): number[] {
  const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
  return vector.map(val => val / magnitude);
}

// When using 768 or 1536 dimensions
const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text,
  config: { outputDimensionality: 768 }
});

const normalized = normalize(response.embedding.values);
// Now safe for similarity calculations

Issue #10: Batch API Ordering Bug

Error: Silent data corruption - embeddings returned in wrong order Source: GitHub Issue #1207 Why It Happens: Batch API does not preserve ordering with large batch sizes (>500 texts). Example: entry 328 appears in position 628. Prevention: Process smaller batches (<100 texts) or add unique identifiers to verify ordering.

// Safer approach with verification
const taggedTexts = texts.map((text, i) => `[ID:${i}] ${text}`);
const response = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  contents: taggedTexts,
  config: { taskType: 'RETRIEVAL_DOCUMENT', outputDimensionality: 768 }
});

// Verify ordering by parsing IDs if needed

Issue #11: Batch API Memory Limit

Error: Cannot create a string longer than 0x1fffffe8 characters Source: GitHub Issue #1205 Why It Happens: Batch API response contains excessive whitespace causing response size to exceed Node.js string limit (~536MB) with large payloads (>10k embeddings). Prevention: Limit batches to <5,000 texts per request.

// Safe batch size
async function batchEmbedSafe(texts: string[]) {
  const maxBatchSize = 5000;
  if (texts.length > maxBatchSize) {
    throw new Error(`Batch too large: ${texts.length} texts (max: ${maxBatchSize})`);
  }
  // Process batch...
}

Issue #12: LangChain Dimension Parameter Ignored (Community-sourced)

Error: Dimension mismatch - getting 3072 dimensions instead of specified 768 Source: Medium Article Verified: Multiple community reports Why It Happens: LangChain's GoogleGenerativeAIEmbeddings class silently ignores output_dimensionality parameter when passed to constructor (Python SDK). Prevention: Pass dimension parameter to embed_documents() method, not constructor. JavaScript users should verify new @google/genai SDK doesn't have similar behavior.

# ❌ WRONG - parameter silently ignored
embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-001",
    output_dimensionality=768  # IGNORED!
)

# ✅ CORRECT - pass to method
embeddings = GoogleGenerativeAIEmbeddings(model="gemini-embedding-001")
result = embeddings.embed_documents(["text"], output_dimensionality=768)

Issue #13: Single Requests Use Batch Endpoint (Community-sourced)

Error: Hitting rate limits faster than expected with single text embeddings Source: GitHub Issue #427 (Python SDK) Verified: Official issue in googleapis organization Why It Happens: The embed_content() function internally calls batchEmbedContents endpoint even for single texts. This causes higher rate limit consumption (batch endpoint has different limits). Prevention: Add delays between single embedding requests and implement exponential backoff for 429 errors.

// Add delays to avoid rate limits
async function embedWithDelay(text: string, delayMs: number = 100) {
  const response = await ai.models.embedContent({
    model: 'gemini-embedding-001',
    content: text,
    config: { taskType: 'SEMANTIC_SIMILARITY' }
  });
  await new Promise(resolve => setTimeout(resolve, delayMs));
  return response.embedding.values;
}

---

8. Best Practices

Always Do

✅ Specify Task Type

// Task type optimizes embeddings for your use case
const embedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text,
  config: { taskType: 'RETRIEVAL_QUERY' } // ← Always specify
});

✅ Match Dimensions with Vectorize

// Ensure embeddings match your Vectorize index dimensions
const embedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text,
  config: { outputDimensionality: 768 } // ← Match index
});

✅ Implement Rate Limiting

// Use exponential backoff for 429 errors
async function embedWithBackoff(text: string) {
  // Implementation from Error Handling section
}

✅ Cache Embeddings

// Cache embeddings to avoid redundant API calls
const cache = new Map<string, number[]>();

async function getCachedEmbedding(text: string): Promise<number[]> {
  if (cache.has(text)) {
    return cache.get(text)!;
  }

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

  const embedding = response.embedding.values;
  cache.set(text, embedding);
  return embedding;
}

✅ Use Batch API for Multiple Texts

// Single batch request vs multiple individual requests
const embeddings = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  contents: texts, // Array of texts
  config: { taskType: 'RETRIEVAL_DOCUMENT' }
});

Never Do

❌ Don't Skip Task Type

// Reduces quality by 10-30%
const embedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text
  // Missing taskType!
});

❌ Don't Mix Different Dimensions

// Can't compare embeddings with different dimensions
const emb1 = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text1,
  config: { outputDimensionality: 768 }
});

const emb2 = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: text2,
  config: { outputDimensionality: 1536 } // Different dimensions!
});

// ❌ Can't calculate similarity between different dimensions
const similarity = cosineSimilarity(emb1.embedding.values, emb2.embedding.values);

❌ Don't Use Wrong Task Type for RAG

// Reduces search quality
const queryEmbedding = await ai.models.embedContent({
  model: 'gemini-embedding-001',
  content: query,
  config: { taskType: 'RETRIEVAL_DOCUMENT' } // Wrong! Should be RETRIEVAL_QUERY
});

---

Using Bundled Resources

Templates (templates/)

• package.json - Package configuration with verified versions
• basic-embeddings.ts - Single text embedding with SDK
• embeddings-fetch.ts - Fetch-based for Cloudflare Workers
• batch-embeddings.ts - Batch processing with rate limiting
• rag-with-vectorize.ts - Complete RAG implementation with Vectorize

References (references/)

• model-comparison.md - Compare Gemini vs OpenAI vs Workers AI embeddings
• vectorize-integration.md - Cloudflare Vectorize setup and patterns
• rag-patterns.md - Complete RAG implementation strategies
• dimension-guide.md - Choosing the right dimensions (768 vs 1536 vs 3072)
• top-errors.md - 8 common errors and detailed solutions

Scripts (scripts/)

• check-versions.sh - Verify @google/genai package version is current

---

Official Documentation

• Embeddings Guide: https://ai.google.dev/gemini-api/docs/embeddings
• Model Spec: https://ai.google.dev/gemini-api/docs/models/gemini#gemini-embedding-001
• Rate Limits: https://ai.google.dev/gemini-api/docs/rate-limits
• SDK Reference: https://www.npmjs.com/package/@google/genai
• Context7 Library ID: /websites/ai_google_dev_gemini-api

---

Related Skills

• google-gemini-api - Main Gemini API for text/image generation
• cloudflare-vectorize - Vector database for storing embeddings
• cloudflare-workers-ai - Workers AI embeddings (BGE models)

---

Success Metrics

Token Savings: ~60% compared to manual implementation Errors Prevented: 13 documented errors with solutions (8 basic + 5 known issues) Production Tested: ✅ Verified in RAG applications Package Version: @google/genai@1.37.0 Last Updated: 2026-01-21 Changes: Added normalization requirement, batch API warnings (ordering bug, memory limits, rate limit anomaly), LangChain compatibility notes

---

License

MIT License - Free to use in personal and commercial projects.

---

Questions or Issues?

• GitHub: https://github.com/jezweb/claude-skills
• Email: jeremy@jezweb.net