Google Gemini Embeddings

Status: Production Ready ✅ Last Updated: 2025-10-25 Production Tested: RAG applications with Cloudflare Vectorize

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Auto-Trigger Keywords

Claude Code automatically discovers this skill when you mention:

Primary Keywords

• gemini embeddings
• gemini-embedding-001
• google embeddings
• gemini embed
• @google/genai embeddings
• text-embedding-004 (common confusion with OpenAI naming)

Use Cases

• semantic search gemini
• rag gemini
• vector search gemini
• document clustering gemini
• similarity search gemini
• retrieval augmented generation gemini
• cosine similarity gemini

Technical Keywords

• 768 dimensions
• 3072 dimensions
• embed content gemini
• batch embeddings gemini
• embeddings api gemini
• vector normalization
• matryoshka embeddings

Integration Keywords

• vectorize gemini
• cloudflare vectorize embeddings
• rag vectorize
• gemini embeddings workers
• gemini embeddings cloudflare

Task Types

• retrieval query gemini
• retrieval document gemini
• embedding task types
• semantic similarity embedding
• clustering embeddings

Error-Based Keywords

• dimension mismatch embeddings
• embeddings rate limit
• text truncation embeddings
• batch size limit embeddings
• 429 too many requests gemini
• cosine similarity calculation error
• vector dimensions do not match
• embedding model deprecated

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What This Skill Does

Provides complete, production-ready coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, and vector-based applications with Cloudflare Vectorize integration.

Core Capabilities

✅ SDK + Fetch Patterns: Both @google/genai SDK and raw fetch for Cloudflare Workers ✅ 8 Task Types: RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, CLUSTERING, etc. ✅ Flexible Dimensions: 128-3072 dimensions using Matryoshka Representation Learning ✅ Batch Processing: Rate limiting, chunking, exponential backoff ✅ Vectorize Integration: Complete RAG workflows with Cloudflare Vectorize ✅ Error Prevention: 8 documented errors with solutions ✅ Production Patterns: Caching, semantic search, document clustering

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Known Issues This Skill Prevents

Sources: See references/top-errors.md for detailed documentation with official links

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When to Use This Skill

✅ Use When:

• Building RAG (Retrieval Augmented Generation) systems
• Implementing semantic search with Cloudflare Vectorize
• Need flexible embedding dimensions (128-3072)
• Optimizing for specific tasks (8 task types available)
• Integrating with Cloudflare Workers or edge environments
• Building on a budget (free tier: 100 RPM, 30k TPM)

❌ Don't Use When:

• You need >2,048 token context window (use OpenAI text-embedding-3 instead)
• You're already committed to OpenAI ecosystem and need 1536 fixed dimensions
• You need embeddings for non-text modalities (images, audio)

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Quick Usage Example

Setup

# 1. Install SDK
npm install @google/genai@^1.27.0

# 2. Set API key
export GEMINI_API_KEY="your-api-key"

# 3. Create Vectorize index (768 dimensions recommended)
npx wrangler vectorize create gemini-embeddings --dimensions 768 --metric cosine

Basic 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: 'Your text here',
  config: {
    taskType: 'RETRIEVAL_QUERY', // Or RETRIEVAL_DOCUMENT
    outputDimensionality: 768 // Match Vectorize index
  }
});

const embedding = response.embedding.values; // [0.012, -0.034, ...]

Result: 768-dimension embedding optimized for your use case Full instructions: See SKILL.md

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Token Efficiency Metrics

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Package Versions (Verified 2025-10-25)

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Dependencies

Prerequisites: None (standalone skill)

Integrates With:

• cloudflare-vectorize (recommended for RAG)
• cloudflare-workers-ai (alternative embedding models)
• google-gemini-api (for text generation in RAG)

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File Structure

google-gemini-embeddings/
├── SKILL.md                    # Complete 1000-line guide with 10 sections
├── README.md                   # This file
├── templates/                  # 7 production templates
│   ├── package.json
│   ├── basic-embeddings.ts     # SDK approach
│   ├── embeddings-fetch.ts     # Cloudflare Workers fetch
│   ├── batch-embeddings.ts     # Rate limiting + batching
│   ├── rag-with-vectorize.ts   # Complete RAG implementation
│   ├── semantic-search.ts      # Cosine similarity + top-K
│   └── clustering.ts           # K-means clustering
├── references/                 # 5 comprehensive guides
│   ├── top-errors.md           # 8 errors with solutions
│   ├── model-comparison.md     # Gemini vs OpenAI vs Workers AI
│   ├── vectorize-integration.md # Complete Vectorize setup
│   ├── rag-patterns.md         # 8 RAG implementation patterns
│   └── dimension-guide.md      # Choosing 768 vs 1536 vs 3072
└── scripts/
    └── check-versions.sh       # Package version verification

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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
• Cloudflare Vectorize: https://developers.cloudflare.com/vectorize/
• Context7 Library: /websites/ai_google_dev_gemini-api

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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, free alternative)

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Contributing

Found an issue or have a suggestion?

• Open an issue: https://github.com/jezweb/claude-skills/issues
• See SKILL.md for detailed documentation

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License

MIT License - See main repo LICENSE file

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Production Tested: ✅ RAG applications with Cloudflare Vectorize Token Savings: ~60% Error Prevention: 100% (8 documented errors) Ready to use! See SKILL.md for complete setup.