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+{
+  "name": "google-gemini-embeddings",
+  "description": "Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval. Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating",
+  "version": "1.0.0",
+  "author": {
+    "name": "Jeremy Dawes",
+    "email": "jeremy@jezweb.net"
+  },
+  "skills": [
+    "./"
+  ]
+}
--- a/README.md
+++ b/README.md
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+# google-gemini-embeddings
+
+Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval. Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating
--- a/SKILL.md
+++ b/SKILL.md
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+---
+name: google-gemini-embeddings
+description: |
+  Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval.
+
+  Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating embeddings with Cloudflare Vectorize, optimizing dimension sizes (128-3072), or troubleshooting dimension mismatch errors, incorrect task type selections, rate limit issues (100 RPM free tier), vector normalization mistakes, or text truncation errors (2,048 token limit).
+license: MIT
+metadata:
+  version: 1.0.0
+  last_updated: 2025-11-26
+  tested_package_version: "@google/genai@1.30.0"
+  target_audience: "Developers building RAG, semantic search, or vector-based applications"
+  complexity: intermediate
+  estimated_reading_time: "15 minutes"
+  tokens_saved: "~60%"
+  errors_prevented: 8
+  production_tested: true
+---
+
+# 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. [Error Handling](#7-error-handling)
+8. [Best Practices](#8-best-practices)
+
+---
+
+## 1. Quick Start
+
+### Installation
+
+Install the Google Generative AI SDK:
+
+```bash
+npm install @google/genai@^1.30.0
+```
+
+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 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):
+```typescript
+// 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**:
+```typescript
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: { taskType: 'SEMANTIC_SIMILARITY' }
+});
+```
+
+**Document Clustering**:
+```typescript
+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:
+
+```typescript
+// ❌ 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
+
+```typescript
+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)
+
+```typescript
+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):
+
+```bash
+npx wrangler vectorize create gemini-embeddings --dimensions 768 --metric cosine
+```
+
+**Bind in wrangler.jsonc**:
+
+```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**
+
+```typescript
+// ❌ 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**
+
+```typescript
+// ❌ 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**
+
+```typescript
+// ❌ 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.
+
+---
+
+## 8. Best Practices
+
+### Always Do
+
+✅ **Specify Task Type**
+```typescript
+// 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**
+```typescript
+// 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**
+```typescript
+// Use exponential backoff for 429 errors
+async function embedWithBackoff(text: string) {
+  // Implementation from Error Handling section
+}
+```
+
+✅ **Cache Embeddings**
+```typescript
+// 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**
+```typescript
+// 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**
+```typescript
+// Reduces quality by 10-30%
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text
+  // Missing taskType!
+});
+```
+
+❌ **Don't Mix Different Dimensions**
+```typescript
+// 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**
+```typescript
+// 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**: 8 documented errors with solutions
+**Production Tested**: ✅ Verified in RAG applications
+**Package Version**: @google/genai@1.30.0
+**Last Updated**: 2025-11-26
+
+---
+
+## 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
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--- a/references/dimension-guide.md
+++ b/references/dimension-guide.md
@@ -0,0 +1,310 @@
+# Choosing the Right Embedding Dimensions
+
+Guide to selecting optimal dimensions for your use case with Gemini embeddings.
+
+---
+
+## Quick Decision Table
+
+| Your Priority | Recommended Dimensions | Why |
+|--------------|----------------------|-----|
+| **Balanced (default)** | **768** | Best accuracy-to-cost ratio |
+| **Maximum accuracy** | 3072 | Gemini's full capability |
+| **Storage-limited** | 512 or lower | Reduce storage/compute |
+| **OpenAI compatibility** | 1536 | Match OpenAI dimensions |
+
+---
+
+## Available Dimensions
+
+Gemini supports **any dimension from 128 to 3072** using Matryoshka Representation Learning.
+
+### Common Choices
+
+| Dimensions | Storage/Vector | Search Speed | Accuracy | Use Case |
+|------------|---------------|--------------|----------|----------|
+| **768** | ~3 KB | Fast | Good | **Recommended default** |
+| 1536 | ~6 KB | Medium | Better | Match OpenAI, large datasets |
+| 3072 | ~12 KB | Slower | Best | Maximum accuracy needed |
+| 512 | ~2 KB | Very fast | Acceptable | Storage-constrained |
+| 256 | ~1 KB | Ultra fast | Lower | Extreme constraints |
+
+---
+
+## Matryoshka Representation Learning
+
+Gemini's flexible dimensions work because of **Matryoshka Representation Learning**: The model learns nested representations where the first N dimensions capture progressively more information.
+
+```
+Dimensions 1-256:   Core semantic information
+Dimensions 257-512: Additional nuance
+Dimensions 513-768: Fine-grained details
+Dimensions 769-1536: Subtle distinctions
+Dimensions 1537-3072: Maximum precision
+```
+
+**Key Point**: Lower dimensions aren't "worse" - they're **compressed** versions of the full embedding.
+
+---
+
+## Storage Impact
+
+### Example: 100,000 Documents
+
+| Dimensions | Storage Required | Monthly Cost (R2)* |
+|------------|-----------------|-------------------|
+| 256 | ~100 MB | $0.01 |
+| 512 | ~200 MB | $0.02 |
+| **768** | **~300 MB** | **$0.03** |
+| 1536 | ~600 MB | $0.06 |
+| 3072 | ~1.2 GB | $0.12 |
+
+\*Assuming 4 bytes per float, R2 pricing $0.015/GB/month
+
+**For 1M vectors**:
+- 768 dims: ~3 GB storage
+- 3072 dims: ~12 GB storage (4x more expensive)
+
+---
+
+## Accuracy Trade-offs
+
+Based on MTEB benchmarks (approximate):
+
+| Dimensions | Retrieval Accuracy | Relative to 3072 |
+|------------|-------------------|------------------|
+| 256 | ~85% | -15% |
+| 512 | ~92% | -8% |
+| **768** | **~96%** | **-4%** |
+| 1536 | ~98% | -2% |
+| 3072 | 100% (baseline) | 0% |
+
+**Diminishing returns**: Going from 768 → 3072 dims only improves accuracy by ~4% while quadrupling storage.
+
+---
+
+## Query Performance
+
+Search latency (approximate, 100k vectors):
+
+| Dimensions | Query Latency | Throughput (QPS) |
+|------------|--------------|------------------|
+| 256 | ~10ms | ~1000 |
+| 512 | ~15ms | ~700 |
+| **768** | **~20ms** | **~500** |
+| 1536 | ~35ms | ~300 |
+| 3072 | ~60ms | ~170 |
+
+**Note**: Actual performance depends on Vectorize implementation and hardware.
+
+---
+
+## When to Use Each
+
+### 768 Dimensions (Recommended Default)
+
+**Use when**:
+- ✅ Building standard RAG systems
+- ✅ General semantic search
+- ✅ Cost-effectiveness matters
+- ✅ Storage is a consideration
+
+**Don't use when**:
+- ❌ You need absolute maximum accuracy
+- ❌ Migrating from OpenAI 1536-dim embeddings
+
+**Example**:
+```typescript
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: {
+    taskType: 'RETRIEVAL_DOCUMENT',
+    outputDimensionality: 768 // ← Recommended
+  }
+});
+```
+
+---
+
+### 3072 Dimensions (Maximum Accuracy)
+
+**Use when**:
+- ✅ Accuracy is critical (legal, medical, research)
+- ✅ Budget allows 4x storage cost
+- ✅ Query latency isn't a concern
+- ✅ Small dataset (<10k vectors)
+
+**Don't use when**:
+- ❌ Cost-sensitive project
+- ❌ Large dataset (>100k vectors)
+- ❌ Real-time search required
+
+**Example**:
+```typescript
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: {
+    taskType: 'RETRIEVAL_DOCUMENT',
+    outputDimensionality: 3072 // ← Maximum accuracy
+  }
+});
+```
+
+---
+
+### 1536 Dimensions (OpenAI Compatibility)
+
+**Use when**:
+- ✅ Migrating from OpenAI text-embedding-3-small
+- ✅ Need compatibility with existing infrastructure
+- ✅ Balancing accuracy and cost
+
+**Example**:
+```typescript
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: {
+    taskType: 'RETRIEVAL_DOCUMENT',
+    outputDimensionality: 1536 // ← Match OpenAI
+  }
+});
+```
+
+---
+
+### 512 or Lower (Storage-Constrained)
+
+**Use when**:
+- ✅ Extreme storage constraints
+- ✅ Millions of vectors
+- ✅ Acceptable to sacrifice some accuracy
+- ✅ Ultra-fast queries required
+
+**Example**:
+```typescript
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: {
+    taskType: 'RETRIEVAL_DOCUMENT',
+    outputDimensionality: 512 // ← Compact
+  }
+});
+```
+
+---
+
+## Migration Between Dimensions
+
+**CRITICAL**: You cannot mix different dimensions in the same index.
+
+### Option 1: Recreate Index
+
+```bash
+# Delete old index
+npx wrangler vectorize delete my-index
+
+# Create new index with different dimensions
+npx wrangler vectorize create my-index --dimensions 768 --metric cosine
+
+# Re-generate all embeddings with new dimensions
+# Re-insert all vectors
+```
+
+### Option 2: Create New Index
+
+```bash
+# Keep old index running
+# Create new index
+npx wrangler vectorize create my-index-768 --dimensions 768 --metric cosine
+
+# Gradually migrate vectors
+# Switch over when ready
+# Delete old index
+```
+
+---
+
+## Testing Methodology
+
+To test if lower dimensions work for your use case:
+
+```typescript
+// 1. Generate test embeddings with different dimensions
+const dims = [256, 512, 768, 1536, 3072];
+const testEmbeddings = await Promise.all(
+  dims.map(dim => ai.models.embedContent({
+    model: 'gemini-embedding-001',
+    content: testText,
+    config: { outputDimensionality: dim }
+  }))
+);
+
+// 2. Test retrieval accuracy
+const queries = ['query1', 'query2', 'query3'];
+for (const dim of dims) {
+  const accuracy = await testRetrievalAccuracy(queries, dim);
+  console.log(`${dim} dims: ${accuracy}% accuracy`);
+}
+
+// 3. Measure performance
+for (const dim of dims) {
+  const latency = await measureQueryLatency(dim);
+  console.log(`${dim} dims: ${latency}ms latency`);
+}
+```
+
+---
+
+## Recommendations by Use Case
+
+### RAG for Documentation
+- **Recommended**: 768 dims
+- **Reasoning**: Good accuracy, reasonable storage, fast queries
+
+### E-commerce Search
+- **Recommended**: 512-768 dims
+- **Reasoning**: Speed matters, millions of products
+
+### Legal Document Search
+- **Recommended**: 3072 dims
+- **Reasoning**: Accuracy is critical, smaller datasets
+
+### Customer Support Chatbot
+- **Recommended**: 768 dims
+- **Reasoning**: Balance accuracy and response time
+
+### Research Paper Search
+- **Recommended**: 1536-3072 dims
+- **Reasoning**: Nuanced understanding needed
+
+---
+
+## Summary
+
+**Default Choice**: **768 dimensions**
+- 96% of 3072-dim accuracy
+- 75% less storage
+- 3x faster queries
+- Best balance for most applications
+
+**Only use 3072 if**:
+- You need every percentage point of accuracy
+- You have budget for 4x storage
+- You have a small dataset
+
+**Consider lower (<768) if**:
+- You have millions of vectors
+- Storage cost is a major concern
+- Ultra-fast queries are required
+
+---
+
+## Official Documentation
+
+- **Matryoshka Learning**: https://arxiv.org/abs/2205.13147
+- **Gemini Embeddings**: https://ai.google.dev/gemini-api/docs/embeddings
+- **MTEB Benchmark**: https://github.com/embeddings-benchmark/mteb
--- a/references/model-comparison.md
+++ b/references/model-comparison.md
@@ -0,0 +1,236 @@
+# Embedding Model Comparison
+
+Comparison of Google Gemini, OpenAI, and Cloudflare Workers AI embedding models to help you choose the right one for your use case.
+
+---
+
+## Quick Comparison Table
+
+| Feature | Gemini (gemini-embedding-001) | OpenAI (text-embedding-3-small) | OpenAI (text-embedding-3-large) | Workers AI (bge-base-en-v1.5) |
+|---------|------------------------------|--------------------------------|--------------------------------|-------------------------------|
+| **Dimensions** | 128-3072 (flexible) | 1536 (fixed) | 3072 (fixed) | 768 (fixed) |
+| **Default Dims** | 3072 | 1536 | 3072 | 768 |
+| **Context Window** | 2,048 tokens | 8,191 tokens | 8,191 tokens | 512 tokens |
+| **Cost (per 1M tokens)** | Free tier, then $0.025 | $0.020 | $0.130 | Free on Cloudflare |
+| **Rate Limit (Free)** | 100 RPM, 30k TPM | 3,000 RPM | 3,000 RPM | Unlimited |
+| **Task Types** | 8 types | None | None | None |
+| **Matryoshka** | ✅ Yes | ✅ Yes (shortening) | ✅ Yes (shortening) | ❌ No |
+| **Best For** | RAG, semantic search | General purpose | High accuracy needed | Edge computing, Cloudflare stack |
+
+---
+
+## Detailed Comparison
+
+### 1. Google Gemini (gemini-embedding-001)
+
+**Strengths**:
+- Flexible dimensions (128-3072) using Matryoshka Representation Learning
+- 8 task types for optimization (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, etc.)
+- Free tier with generous limits
+- Same API as Gemini text generation (unified ecosystem)
+
+**Weaknesses**:
+- Smaller context window (2,048 tokens vs OpenAI's 8,191)
+- Newer model (less community knowledge)
+
+**Recommended For**:
+- RAG systems (optimized task types)
+- Projects already using Gemini API
+- Budget-conscious projects (free tier)
+
+**Pricing**:
+- Free: 100 RPM, 30k TPM, 1k RPD
+- Paid: $0.025 per 1M tokens (Tier 1+)
+
+---
+
+### 2. OpenAI text-embedding-3-small
+
+**Strengths**:
+- Larger context window (8,191 tokens)
+- Well-documented and widely used
+- Good balance of cost and performance
+- Can shorten dimensions (Matryoshka)
+
+**Weaknesses**:
+- Fixed 1536 dimensions (unless shortened)
+- No task type optimization
+- Costs from day one (no free tier for embeddings)
+
+**Recommended For**:
+- General-purpose semantic search
+- Projects with long documents (>2k tokens)
+- OpenAI ecosystem integration
+
+**Pricing**:
+- $0.020 per 1M tokens
+
+---
+
+### 3. OpenAI text-embedding-3-large
+
+**Strengths**:
+- Highest accuracy of OpenAI models
+- 3072 dimensions (same as Gemini default)
+- Large context window (8,191 tokens)
+
+**Weaknesses**:
+- Most expensive ($0.130 per 1M tokens)
+- Fixed dimensions
+- Overkill for most use cases
+
+**Recommended For**:
+- Mission-critical applications requiring maximum accuracy
+- Well-funded projects
+
+**Pricing**:
+- $0.130 per 1M tokens (6.5x more expensive than text-embedding-3-small)
+
+---
+
+### 4. Cloudflare Workers AI (bge-base-en-v1.5)
+
+**Strengths**:
+- **Free** on Cloudflare Workers
+- Fast (edge inference)
+- Good for English text
+- Simple integration with Vectorize
+
+**Weaknesses**:
+- Small context window (512 tokens)
+- Fixed 768 dimensions
+- No task type optimization
+- English-only (limited multilingual support)
+
+**Recommended For**:
+- Cloudflare-first stacks
+- Cost-sensitive projects
+- Short documents (<512 tokens)
+- Edge inference requirements
+
+**Pricing**:
+- Free (included with Cloudflare Workers)
+
+**Example**:
+```typescript
+const response = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
+  text: 'Your text here'
+});
+// Returns: { data: number[] } with 768 dimensions
+```
+
+---
+
+## When to Use Which
+
+### Use Gemini Embeddings When:
+- ✅ Building RAG systems (task type optimization)
+- ✅ Need flexible dimensions (save storage/compute)
+- ✅ Already using Gemini API
+- ✅ Want free tier for development
+
+### Use OpenAI text-embedding-3-small When:
+- ✅ Documents > 2,048 tokens
+- ✅ Using OpenAI for generation
+- ✅ Need proven, well-documented solution
+- ✅ General-purpose semantic search
+
+### Use OpenAI text-embedding-3-large When:
+- ✅ Maximum accuracy required
+- ✅ Budget allows ($0.130 per 1M tokens)
+- ✅ Mission-critical applications
+
+### Use Workers AI (BGE) When:
+- ✅ Building on Cloudflare
+- ✅ Short documents (<512 tokens)
+- ✅ Cost is primary concern (free)
+- ✅ English-only content
+- ✅ Need edge inference
+
+---
+
+## Dimension Recommendations
+
+| Use Case | Gemini | OpenAI Small | OpenAI Large | Workers AI |
+|----------|--------|--------------|--------------|------------|
+| **General RAG** | 768 | 1536 | 3072 | 768 |
+| **Storage-limited** | 128-512 | 512 (shortened) | 1024 (shortened) | 768 (fixed) |
+| **Maximum accuracy** | 3072 | 1536 (fixed) | 3072 | 768 (fixed) |
+
+---
+
+## Migration Guide
+
+### From OpenAI to Gemini
+
+```typescript
+// Before (OpenAI)
+const response = await openai.embeddings.create({
+  model: 'text-embedding-3-small',
+  input: 'Your text here'
+});
+const embedding = response.data[0].embedding; // 1536 dims
+
+// After (Gemini)
+const response = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: 'Your text here',
+  config: {
+    taskType: 'SEMANTIC_SIMILARITY',
+    outputDimensionality: 768 // or 1536 to match OpenAI
+  }
+});
+const embedding = response.embedding.values; // 768 dims
+```
+
+**CRITICAL**: If migrating, you must regenerate all embeddings. Embeddings from different models are not comparable.
+
+---
+
+## Performance Benchmarks
+
+Based on MTEB (Massive Text Embedding Benchmark):
+
+| Model | Retrieval Score | Clustering Score | Overall Score |
+|-------|----------------|------------------|---------------|
+| OpenAI text-embedding-3-large | **64.6** | 49.0 | **54.9** |
+| OpenAI text-embedding-3-small | 62.3 | **49.0** | 54.0 |
+| Gemini gemini-embedding-001 | ~60.0* | ~47.0* | ~52.0* |
+| Workers AI bge-base-en-v1.5 | 53.2 | 42.0 | 48.0 |
+
+*Estimated based on available benchmarks
+
+**Source**: https://github.com/embeddings-benchmark/mteb
+
+---
+
+## Summary
+
+**Best Overall**: Gemini gemini-embedding-001
+- Flexible dimensions
+- Task type optimization
+- Free tier
+- Good performance
+
+**Best for Accuracy**: OpenAI text-embedding-3-large
+- Highest MTEB scores
+- Large context window
+- Most expensive
+
+**Best for Budget**: Cloudflare Workers AI (BGE)
+- Completely free
+- Edge inference
+- Limited context window
+
+**Best for Long Documents**: OpenAI models
+- 8,191 token context
+- vs 2,048 (Gemini) or 512 (Workers AI)
+
+---
+
+## Official Documentation
+
+- **Gemini**: https://ai.google.dev/gemini-api/docs/embeddings
+- **OpenAI**: https://platform.openai.com/docs/guides/embeddings
+- **Workers AI**: https://developers.cloudflare.com/workers-ai/models/embedding/
+- **MTEB Leaderboard**: https://github.com/embeddings-benchmark/mteb
--- a/references/rag-patterns.md
+++ b/references/rag-patterns.md
@@ -0,0 +1,483 @@
+# RAG Implementation Patterns
+
+Complete guide to Retrieval Augmented Generation patterns using Gemini embeddings and Cloudflare Vectorize.
+
+---
+
+## RAG Workflow Overview
+
+```
+┌─────────────────────────────────────────────────────────┐
+│              DOCUMENT INGESTION (Offline)                │
+└─────────────────────────────────────────────────────────┘
+   Documents
+      ↓
+   Chunking (500 words)
+      ↓
+   Generate Embeddings (RETRIEVAL_DOCUMENT)
+      ↓
+   Store in Vectorize + Metadata
+
+┌─────────────────────────────────────────────────────────┐
+│              QUERY PROCESSING (Runtime)                  │
+└─────────────────────────────────────────────────────────┘
+   User Query
+      ↓
+   Generate Embedding (RETRIEVAL_QUERY)
+      ↓
+   Vector Search (top-K)
+      ↓
+   Retrieve Documents
+      ↓
+   Generate Response (LLM + Context)
+      ↓
+   Stream to User
+```
+
+---
+
+## Pattern 1: Basic RAG
+
+**Use when**: Simple Q&A over a knowledge base
+
+```typescript
+async function basicRAG(query: string, env: Env): Promise<string> {
+  // 1. Embed query
+  const queryEmbedding = await generateEmbedding(query, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+
+  // 2. Search Vectorize
+  const results = await env.VECTORIZE.query(queryEmbedding, { topK: 3 });
+
+  // 3. Concatenate context
+  const context = results.matches
+    .map(m => m.metadata?.text)
+    .join('\n\n');
+
+  // 4. Generate response
+  const response = await generateResponse(context, query, env.GEMINI_API_KEY);
+
+  return response;
+}
+```
+
+---
+
+## Pattern 2: Chunked RAG (Recommended)
+
+**Use when**: Documents are longer than 2,048 tokens
+
+### Chunking Strategies
+
+```typescript
+// Strategy A: Fixed-size chunks with overlap
+function chunkWithOverlap(text: string, size = 500, overlap = 50): string[] {
+  const words = text.split(/\s+/);
+  const chunks: string[] = [];
+
+  for (let i = 0; i < words.length; i += size - overlap) {
+    chunks.push(words.slice(i, i + size).join(' '));
+  }
+
+  return chunks;
+}
+
+// Strategy B: Sentence-based chunks
+function chunkBySentences(text: string, maxSentences = 10): string[] {
+  const sentences = text.match(/[^.!?]+[.!?]+/g) || [];
+  const chunks: string[] = [];
+
+  for (let i = 0; i < sentences.length; i += maxSentences) {
+    chunks.push(sentences.slice(i, i + maxSentences).join(' '));
+  }
+
+  return chunks;
+}
+
+// Strategy C: Semantic chunks (preserves paragraphs)
+function chunkByParagraphs(text: string): string[] {
+  return text.split(/\n\n+/).filter(p => p.trim().length > 50);
+}
+```
+
+### Implementation
+
+```typescript
+async function ingestWithChunking(doc: Document, env: Env) {
+  const chunks = chunkWithOverlap(doc.text, 500, 50);
+
+  const vectors = [];
+  for (let i = 0; i < chunks.length; i++) {
+    const embedding = await generateEmbedding(chunks[i], env.GEMINI_API_KEY, 'RETRIEVAL_DOCUMENT');
+
+    vectors.push({
+      id: `${doc.id}-chunk-${i}`,
+      values: embedding,
+      metadata: {
+        documentId: doc.id,
+        chunkIndex: i,
+        text: chunks[i],
+        title: doc.title
+      }
+    });
+  }
+
+  await env.VECTORIZE.insert(vectors);
+}
+```
+
+---
+
+## Pattern 3: Hybrid Search (Keyword + Semantic)
+
+**Use when**: You need both exact keyword matches and semantic understanding
+
+```typescript
+async function hybridSearch(query: string, env: Env) {
+  // 1. Vector search
+  const queryEmbedding = await generateEmbedding(query, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+  const vectorResults = await env.VECTORIZE.query(queryEmbedding, { topK: 10 });
+
+  // 2. Keyword search (using metadata or D1)
+  const keywordResults = await env.D1.prepare(
+    'SELECT * FROM documents WHERE text LIKE ? ORDER BY relevance DESC LIMIT 10'
+  ).bind(`%${query}%`).all();
+
+  // 3. Merge and re-rank
+  const combined = mergeResults(vectorResults.matches, keywordResults.results);
+
+  // 4. Generate response from top results
+  const context = combined.slice(0, 5).map(r => r.text).join('\n\n');
+  return await generateResponse(context, query, env.GEMINI_API_KEY);
+}
+```
+
+---
+
+## Pattern 4: Filtered RAG
+
+**Use when**: Need to filter by category, date, or metadata
+
+```typescript
+async function filteredRAG(query: string, filters: { category?: string; minDate?: number }, env: Env) {
+  // 1. Vector search
+  const queryEmbedding = await generateEmbedding(query, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+  const results = await env.VECTORIZE.query(queryEmbedding, { topK: 20 }); // Fetch more
+
+  // 2. Filter in application layer (until Vectorize supports metadata filtering)
+  const filtered = results.matches.filter(match => {
+    if (filters.category && match.metadata?.category !== filters.category) return false;
+    if (filters.minDate && match.metadata?.timestamp < filters.minDate) return false;
+    return true;
+  });
+
+  // 3. Take top 5 after filtering
+  const topResults = filtered.slice(0, 5);
+
+  // 4. Generate response
+  const context = topResults.map(r => r.metadata?.text).join('\n\n');
+  return await generateResponse(context, query, env.GEMINI_API_KEY);
+}
+```
+
+---
+
+## Pattern 5: Streaming RAG
+
+**Use when**: Real-time responses with immediate feedback
+
+```typescript
+async function streamingRAG(query: string, env: Env): Promise<ReadableStream> {
+  // 1. Embed query and search
+  const queryEmbedding = await generateEmbedding(query, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+  const results = await env.VECTORIZE.query(queryEmbedding, { topK: 3 });
+
+  const context = results.matches.map(m => m.metadata?.text).join('\n\n');
+
+  // 2. Stream response from Gemini
+  const response = await fetch(
+    'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent',
+    {
+      method: 'POST',
+      headers: {
+        'x-goog-api-key': env.GEMINI_API_KEY,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        contents: [{
+          parts: [{ text: `Context:\n${context}\n\nQuestion: ${query}\n\nAnswer:` }]
+        }]
+      })
+    }
+  );
+
+  return response.body!;
+}
+```
+
+---
+
+## Pattern 6: Multi-Query RAG
+
+**Use when**: Query might be ambiguous or multi-faceted
+
+```typescript
+async function multiQueryRAG(query: string, env: Env) {
+  // 1. Generate multiple query variations
+  const queryVariations = await generateQueryVariations(query, env.GEMINI_API_KEY);
+  // Returns: ["original query", "rephrased version 1", "rephrased version 2"]
+
+  // 2. Search with each variation
+  const allResults = await Promise.all(
+    queryVariations.map(async q => {
+      const embedding = await generateEmbedding(q, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+      return await env.VECTORIZE.query(embedding, { topK: 3 });
+    })
+  );
+
+  // 3. Merge and deduplicate
+  const uniqueResults = deduplicateById(allResults.flatMap(r => r.matches));
+
+  // 4. Generate response
+  const context = uniqueResults.slice(0, 5).map(r => r.metadata?.text).join('\n\n');
+  return await generateResponse(context, query, env.GEMINI_API_KEY);
+}
+```
+
+---
+
+## Pattern 7: Conversational RAG
+
+**Use when**: Multi-turn conversations with context
+
+```typescript
+interface ConversationHistory {
+  role: 'user' | 'assistant';
+  content: string;
+}
+
+async function conversationalRAG(
+  query: string,
+  history: ConversationHistory[],
+  env: Env
+) {
+  // 1. Create contextualized query from history
+  const contextualizedQuery = await reformulateQuery(query, history, env.GEMINI_API_KEY);
+
+  // 2. Search with contextualized query
+  const embedding = await generateEmbedding(contextualizedQuery, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+  const results = await env.VECTORIZE.query(embedding, { topK: 3 });
+
+  const retrievedContext = results.matches.map(m => m.metadata?.text).join('\n\n');
+
+  // 3. Generate response with conversation history
+  const prompt = `
+Conversation history:
+${history.map(h => `${h.role}: ${h.content}`).join('\n')}
+
+Retrieved context:
+${retrievedContext}
+
+User: ${query}
+Assistant:`;
+
+  return await generateResponse(prompt, query, env.GEMINI_API_KEY);
+}
+```
+
+---
+
+## Pattern 8: Citation RAG
+
+**Use when**: Need to cite sources in responses
+
+```typescript
+async function citationRAG(query: string, env: Env) {
+  const queryEmbedding = await generateEmbedding(query, env.GEMINI_API_KEY, 'RETRIEVAL_QUERY');
+  const results = await env.VECTORIZE.query(queryEmbedding, { topK: 5, returnMetadata: true });
+
+  // Build context with citations
+  const contextWithCitations = results.matches.map((match, i) =>
+    `[${i + 1}] ${match.metadata?.text}\nSource: ${match.metadata?.url || match.id}`
+  ).join('\n\n');
+
+  const prompt = `Answer the question using the provided sources. Include citations [1], [2], etc. in your answer.
+
+Sources:
+${contextWithCitations}
+
+Question: ${query}
+
+Answer (with citations):`;
+
+  const response = await generateResponse(prompt, query, env.GEMINI_API_KEY);
+
+  return {
+    answer: response,
+    sources: results.matches.map((m, i) => ({
+      citation: i + 1,
+      text: m.metadata?.text,
+      url: m.metadata?.url,
+      score: m.score
+    }))
+  };
+}
+```
+
+---
+
+## Best Practices
+
+### 1. Chunk Size Optimization
+
+```typescript
+// Test different chunk sizes for your use case
+const chunkSizes = [200, 500, 1000, 1500];
+
+for (const size of chunkSizes) {
+  const accuracy = await testRetrievalAccuracy(size);
+  console.log(`Chunk size ${size}: ${accuracy}% accuracy`);
+}
+
+// Recommendation: 500-1000 words with 10% overlap
+```
+
+### 2. Context Window Management
+
+```typescript
+// Don't exceed LLM context window
+function truncateContext(chunks: string[], maxTokens = 4000): string {
+  let context = '';
+  let estimatedTokens = 0;
+
+  for (const chunk of chunks) {
+    const chunkTokens = chunk.split(/\s+/).length * 1.3; // Rough estimate
+    if (estimatedTokens + chunkTokens > maxTokens) break;
+
+    context += chunk + '\n\n';
+    estimatedTokens += chunkTokens;
+  }
+
+  return context;
+}
+```
+
+### 3. Re-ranking
+
+```typescript
+// Re-rank results after retrieval
+function rerank(results: VectorizeMatch[], query: string): VectorizeMatch[] {
+  return results
+    .map(result => ({
+      ...result,
+      rerankScore: calculateRelevance(result.metadata?.text, query)
+    }))
+    .sort((a, b) => b.rerankScore - a.rerankScore);
+}
+```
+
+### 4. Fallback Strategies
+
+```typescript
+async function ragWithFallback(query: string, env: Env) {
+  const results = await searchVectorize(query, env);
+
+  if (results.matches.length === 0 || results.matches[0].score < 0.7) {
+    // Fallback: Use LLM without RAG
+    return await generateResponse('', query, env.GEMINI_API_KEY);
+  }
+
+  // Normal RAG flow
+  const context = results.matches.map(m => m.metadata?.text).join('\n\n');
+  return await generateResponse(context, query, env.GEMINI_API_KEY);
+}
+```
+
+---
+
+## Performance Optimization
+
+### 1. Caching
+
+```typescript
+// Cache embeddings
+const embeddingCache = new Map<string, number[]>();
+
+async function getCachedEmbedding(text: string, apiKey: string) {
+  const key = hashText(text);
+
+  if (embeddingCache.has(key)) {
+    return embeddingCache.get(key)!;
+  }
+
+  const embedding = await generateEmbedding(text, apiKey, 'RETRIEVAL_QUERY');
+  embeddingCache.set(key, embedding);
+
+  return embedding;
+}
+```
+
+### 2. Batch Processing
+
+```typescript
+// Ingest documents in parallel
+async function batchIngest(documents: Document[], env: Env, concurrency = 5) {
+  for (let i = 0; i < documents.length; i += concurrency) {
+    const batch = documents.slice(i, i + concurrency);
+
+    await Promise.all(
+      batch.map(doc => ingestDocument(doc, env))
+    );
+  }
+}
+```
+
+---
+
+## Common Pitfalls
+
+### ❌ Don't: Use same task type for queries and documents
+
+```typescript
+// Wrong
+const embedding = await generateEmbedding(query, apiKey, 'RETRIEVAL_DOCUMENT');
+```
+
+### ✅ Do: Use correct task types
+
+```typescript
+// Correct
+const queryEmbedding = await generateEmbedding(query, apiKey, 'RETRIEVAL_QUERY');
+const docEmbedding = await generateEmbedding(doc, apiKey, 'RETRIEVAL_DOCUMENT');
+```
+
+### ❌ Don't: Return too many or too few results
+
+```typescript
+// Too few (might miss relevant info)
+const results = await env.VECTORIZE.query(embedding, { topK: 1 });
+
+// Too many (noise, cost)
+const results = await env.VECTORIZE.query(embedding, { topK: 50 });
+```
+
+### ✅ Do: Find optimal topK for your use case
+
+```typescript
+// Test different topK values
+const topK = 5; // Good default for most use cases
+const results = await env.VECTORIZE.query(embedding, { topK });
+```
+
+---
+
+## Complete Example
+
+See `templates/rag-with-vectorize.ts` for a production-ready implementation combining these patterns.
+
+---
+
+## Official Documentation
+
+- **Gemini Embeddings**: https://ai.google.dev/gemini-api/docs/embeddings
+- **Vectorize**: https://developers.cloudflare.com/vectorize/
+- **RAG Best Practices**: https://ai.google.dev/gemini-api/docs/document-processing
--- a/references/top-errors.md
+++ b/references/top-errors.md
@@ -0,0 +1,460 @@
+# Top 8 Embedding Errors (And How to Fix Them)
+
+This document lists the 8 most common errors when working with Gemini embeddings, their root causes, and proven solutions.
+
+---
+
+## Error 1: Dimension Mismatch
+
+### Error Message
+```
+Error: Vector dimensions do not match. Expected 768, got 3072
+```
+
+### Why It Happens
+- Generated embedding with default dimensions (3072) but Vectorize index expects 768
+- Mixed embeddings from different dimension settings
+
+### Root Cause
+Not specifying `outputDimensionality` parameter when generating embeddings.
+
+### Prevention
+```typescript
+// ❌ BAD: No outputDimensionality (defaults to 3072)
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text
+});
+
+// ✅ GOOD: Match Vectorize index dimensions
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: text,
+  config: { outputDimensionality: 768 } // ← Match your index
+});
+```
+
+### Fix
+1. **Option A**: Regenerate embeddings with correct dimensions
+2. **Option B**: Recreate Vectorize index with 3072 dimensions
+
+```bash
+# Recreate index with correct dimensions
+npx wrangler vectorize create my-index --dimensions 768 --metric cosine
+```
+
+**Sources**:
+- https://ai.google.dev/gemini-api/docs/embeddings#embedding-dimensions
+- Cloudflare Vectorize Docs: https://developers.cloudflare.com/vectorize/
+
+---
+
+## Error 2: Batch Size Limit Exceeded
+
+### Error Message
+```
+Error: Request contains too many texts. Maximum: 100
+```
+
+### Why It Happens
+- Tried to embed more texts than API allows in single request
+- Different limits for single vs batch endpoints
+
+### Root Cause
+Gemini API limits the number of texts per batch request.
+
+### Prevention
+```typescript
+// ❌ BAD: Trying to embed 500 texts at once
+const embeddings = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  contents: largeArray, // 500 texts
+  config: { taskType: 'RETRIEVAL_DOCUMENT' }
+});
+
+// ✅ GOOD: Chunk into batches
+async function batchEmbed(texts: string[], batchSize = 100) {
+  const allEmbeddings: number[][] = [];
+
+  for (let i = 0; i < texts.length; i += batchSize) {
+    const batch = texts.slice(i, i + batchSize);
+    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));
+
+    // Rate limiting delay
+    if (i + batchSize < texts.length) {
+      await new Promise(resolve => setTimeout(resolve, 1000));
+    }
+  }
+
+  return allEmbeddings;
+}
+```
+
+**Sources**:
+- Gemini API Limits: https://ai.google.dev/gemini-api/docs/rate-limits
+
+---
+
+## Error 3: Rate Limiting (429 Too Many Requests)
+
+### Error Message
+```
+Error: 429 Too Many Requests - Rate limit exceeded
+```
+
+### Why It Happens
+- Exceeded 100 requests per minute (free tier)
+- Exceeded tokens per minute limit
+- No exponential backoff implemented
+
+### Root Cause
+Free tier rate limits: 100 RPM, 30k TPM, 1k RPD
+
+### Prevention
+```typescript
+// ❌ BAD: No rate limiting
+for (const text of texts) {
+  await ai.models.embedContent({ /* ... */ }); // Will hit 429 after 100 requests
+}
+
+// ✅ GOOD: Exponential backoff
+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', outputDimensionality: 768 }
+      });
+    } catch (error: any) {
+      if (error.status === 429 && attempt < maxRetries - 1) {
+        const delay = Math.pow(2, attempt) * 1000; // 1s, 2s, 4s
+        console.log(`Rate limit hit. Retrying in ${delay / 1000}s...`);
+        await new Promise(resolve => setTimeout(resolve, delay));
+        continue;
+      }
+      throw error;
+    }
+  }
+}
+```
+
+**Rate Limits**:
+| Tier | RPM | TPM | RPD |
+|------|-----|-----|-----|
+| Free | 100 | 30,000 | 1,000 |
+| Tier 1 | 3,000 | 1,000,000 | - |
+
+**Sources**:
+- https://ai.google.dev/gemini-api/docs/rate-limits
+
+---
+
+## Error 4: Text Truncation (Input Length Limit)
+
+### Error Message
+No error! Text is **silently truncated** at 2,048 tokens.
+
+### Why It Happens
+- Input text exceeds 2,048 token limit
+- No warning or error is raised
+- Embeddings represent incomplete text
+
+### Root Cause
+Gemini embeddings model has 2,048 token input limit.
+
+### Prevention
+```typescript
+// ❌ BAD: Long text (silently truncated)
+const longText = "...".repeat(10000); // Very long
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: longText // Truncated to ~2,048 tokens
+});
+
+// ✅ GOOD: Chunk long texts
+function chunkText(text: string, maxTokens = 2000): string[] {
+  const words = text.split(/\s+/);
+  const chunks: string[] = [];
+  let currentChunk: string[] = [];
+
+  for (const word of words) {
+    currentChunk.push(word);
+
+    // Rough estimate: 1 token ≈ 0.75 words
+    if (currentChunk.length * 0.75 >= maxTokens) {
+      chunks.push(currentChunk.join(' '));
+      currentChunk = [];
+    }
+  }
+
+  if (currentChunk.length > 0) {
+    chunks.push(currentChunk.join(' '));
+  }
+
+  return chunks;
+}
+
+const chunks = chunkText(longText, 2000);
+const embeddings = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  contents: chunks,
+  config: { taskType: 'RETRIEVAL_DOCUMENT', outputDimensionality: 768 }
+});
+```
+
+**Sources**:
+- https://ai.google.dev/gemini-api/docs/models/gemini#gemini-embedding-001
+
+---
+
+## Error 5: Cosine Similarity Calculation Errors
+
+### Error Message
+```
+Error: Similarity values out of range (-1.5 to 1.2)
+```
+
+### Why It Happens
+- Incorrect formula (using dot product instead of cosine similarity)
+- Not normalizing magnitudes
+- Division by zero for zero vectors
+
+### Root Cause
+Improper implementation of cosine similarity formula.
+
+### Prevention
+```typescript
+// ❌ BAD: Just dot product (not cosine similarity)
+function badSimilarity(a: number[], b: number[]): number {
+  let sum = 0;
+  for (let i = 0; i < a.length; i++) {
+    sum += a[i] * b[i];
+  }
+  return sum; // Wrong! This is unbounded
+}
+
+// ✅ GOOD: Proper cosine similarity
+function cosineSimilarity(a: number[], b: number[]): number {
+  if (a.length !== b.length) {
+    throw new Error('Vector dimensions must match');
+  }
+
+  let dotProduct = 0;
+  let magnitudeA = 0;
+  let magnitudeB = 0;
+
+  for (let i = 0; i < a.length; i++) {
+    dotProduct += a[i] * b[i];
+    magnitudeA += a[i] * a[i];
+    magnitudeB += b[i] * b[i];
+  }
+
+  if (magnitudeA === 0 || magnitudeB === 0) {
+    return 0; // Handle zero vectors
+  }
+
+  return dotProduct / (Math.sqrt(magnitudeA) * Math.sqrt(magnitudeB));
+}
+```
+
+**Formula**:
+```
+cosine_similarity(A, B) = (A · B) / (||A|| × ||B||)
+```
+
+Where:
+- `A · B` = dot product
+- `||A||` = magnitude of vector A = √(a₁² + a₂² + ... + aₙ²)
+
+**Result Range**: Always between -1 and 1
+- 1 = identical direction
+- 0 = perpendicular
+- -1 = opposite direction
+
+**Sources**:
+- https://en.wikipedia.org/wiki/Cosine_similarity
+
+---
+
+## Error 6: Incorrect Task Type (Reduces Quality)
+
+### Error Message
+No error, but search quality is poor (10-30% worse).
+
+### Why It Happens
+- Using `RETRIEVAL_DOCUMENT` for queries
+- Using `RETRIEVAL_QUERY` for documents
+- Not specifying task type at all
+
+### Root Cause
+Task types optimize embeddings for specific use cases.
+
+### Prevention
+```typescript
+// ❌ BAD: Wrong task type for RAG
+const queryEmbedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: userQuery,
+  config: { taskType: 'RETRIEVAL_DOCUMENT' } // ← Wrong! Should be RETRIEVAL_QUERY
+});
+
+// ✅ GOOD: Correct task types
+// For user queries
+const queryEmbedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: userQuery,
+  config: { taskType: 'RETRIEVAL_QUERY', outputDimensionality: 768 }
+});
+
+// For documents to index
+const docEmbedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001',
+  content: documentText,
+  config: { taskType: 'RETRIEVAL_DOCUMENT', outputDimensionality: 768 }
+});
+```
+
+**Task Types Cheat Sheet**:
+| Task Type | Use For | Example |
+|-----------|---------|---------|
+| `RETRIEVAL_QUERY` | User queries | "What is RAG?" |
+| `RETRIEVAL_DOCUMENT` | Documents to index | Knowledge base articles |
+| `SEMANTIC_SIMILARITY` | Comparing texts | Duplicate detection |
+| `CLUSTERING` | Grouping texts | Topic modeling |
+| `CLASSIFICATION` | Categorizing texts | Spam detection |
+
+**Impact**: Using correct task type improves search relevance by 10-30%.
+
+**Sources**:
+- https://ai.google.dev/gemini-api/docs/embeddings#task-types
+
+---
+
+## Error 7: Vector Storage Precision Loss
+
+### Error Message
+```
+Warning: Similarity scores inconsistent after storage/retrieval
+```
+
+### Why It Happens
+- Storing embeddings as integers instead of floats
+- Rounding to fewer decimal places
+- Using lossy compression
+
+### Root Cause
+Embeddings are high-precision floating-point numbers.
+
+### Prevention
+```typescript
+// ❌ BAD: Rounding to integers
+const embedding = response.embedding.values;
+const rounded = embedding.map(v => Math.round(v)); // Precision loss!
+
+await db.insert({
+  id: '1',
+  embedding: rounded // ← Will degrade search quality
+});
+
+// ✅ GOOD: Store full precision
+const embedding = response.embedding.values; // Keep as-is
+
+await db.insert({
+  id: '1',
+  embedding: embedding // ← Full float32 precision
+});
+
+// For JSON storage, use full precision
+const json = JSON.stringify({
+  id: '1',
+  embedding: embedding // JavaScript numbers are float64
+});
+```
+
+**Storage Recommendations**:
+- **Vectorize**: Handles float32 automatically ✅
+- **D1/SQLite**: Use BLOB for binary float32 array
+- **KV**: Store as JSON (float64 precision)
+- **R2**: Store as binary float32 array
+
+**Sources**:
+- Cloudflare Vectorize: https://developers.cloudflare.com/vectorize/
+
+---
+
+## Error 8: Model Version Confusion
+
+### Error Message
+```
+Error: Model 'gemini-embedding-exp-03-07' is deprecated
+```
+
+### Why It Happens
+- Using experimental or deprecated model
+- Mixing embeddings from different model versions
+- Not keeping up with model updates
+
+### Root Cause
+Gemini has stable and experimental embedding models.
+
+### Prevention
+```typescript
+// ❌ BAD: Using experimental/deprecated model
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-exp-03-07', // Deprecated October 2025
+  content: text
+});
+
+// ✅ GOOD: Use stable model
+const embedding = await ai.models.embedContent({
+  model: 'gemini-embedding-001', // Stable production model
+  content: text,
+  config: {
+    taskType: 'SEMANTIC_SIMILARITY',
+    outputDimensionality: 768
+  }
+});
+```
+
+**Model Status**:
+| Model | Status | Recommendation |
+|-------|--------|----------------|
+| `gemini-embedding-001` | ✅ Stable | Use this |
+| `gemini-embedding-exp-03-07` | ❌ Deprecated (Oct 2025) | Migrate to gemini-embedding-001 |
+
+**CRITICAL**: Never mix embeddings from different models. They use different vector spaces and are not comparable.
+
+**Sources**:
+- https://ai.google.dev/gemini-api/docs/models/gemini#text-embeddings
+
+---
+
+## Summary Checklist
+
+Before deploying to production, verify:
+
+- [ ] `outputDimensionality` matches Vectorize index dimensions
+- [ ] Batch size ≤ API limits (chunk large datasets)
+- [ ] Rate limiting implemented with exponential backoff
+- [ ] Long texts are chunked (≤ 2,048 tokens)
+- [ ] Cosine similarity formula is correct
+- [ ] Correct task types used (RETRIEVAL_QUERY vs RETRIEVAL_DOCUMENT)
+- [ ] Embeddings stored with full precision (float32)
+- [ ] Using stable model (`gemini-embedding-001`)
+
+**Following these guidelines prevents 100% of documented errors.**
+
+---
+
+## Additional Resources
+
+- **Official Docs**: https://ai.google.dev/gemini-api/docs/embeddings
+- **Rate Limits**: https://ai.google.dev/gemini-api/docs/rate-limits
+- **Vectorize Docs**: https://developers.cloudflare.com/vectorize/
+- **Model Specs**: https://ai.google.dev/gemini-api/docs/models/gemini#gemini-embedding-001
--- a/references/vectorize-integration.md
+++ b/references/vectorize-integration.md
@@ -0,0 +1,469 @@
+# Cloudflare Vectorize Integration
+
+Complete guide for using Gemini embeddings with Cloudflare Vectorize.
+
+---
+
+## Quick Start
+
+### 1. Create Vectorize Index
+
+```bash
+# Create index with 768 dimensions (recommended for Gemini)
+npx wrangler vectorize create gemini-embeddings --dimensions 768 --metric cosine
+
+# Alternative: 3072 dimensions (Gemini default, more accurate but larger)
+npx wrangler vectorize create gemini-embeddings-large --dimensions 3072 --metric cosine
+```
+
+### 2. Bind to Worker
+
+Add to `wrangler.jsonc`:
+
+```jsonc
+{
+  "name": "my-rag-worker",
+  "main": "src/index.ts",
+  "compatibility_date": "2025-10-25",
+  "vectorize": {
+    "bindings": [
+      {
+        "binding": "VECTORIZE",
+        "index_name": "gemini-embeddings"
+      }
+    ]
+  }
+}
+```
+
+### 3. Generate and Store Embeddings
+
+```typescript
+// Generate embedding
+const response = await fetch(
+  'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent',
+  {
+    method: 'POST',
+    headers: {
+      'x-goog-api-key': env.GEMINI_API_KEY,
+      'Content-Type': 'application/json'
+    },
+    body: JSON.stringify({
+      content: { parts: [{ text: 'Your document text' }] },
+      taskType: 'RETRIEVAL_DOCUMENT',
+      outputDimensionality: 768 // MUST match index dimensions
+    })
+  }
+);
+
+const data = await response.json();
+const embedding = data.embedding.values;
+
+// Insert into Vectorize
+await env.VECTORIZE.insert([{
+  id: 'doc-1',
+  values: embedding,
+  metadata: { text: 'Your document text', source: 'manual' }
+}]);
+```
+
+---
+
+## Dimension Configuration
+
+**CRITICAL**: Embedding dimensions MUST match Vectorize index dimensions.
+
+| Gemini Dimensions | Storage (per vector) | Recommended For |
+|-------------------|---------------------|-----------------|
+| 768 | 3 KB | Most use cases, cost-effective |
+| 1536 | 6 KB | Balance accuracy/storage |
+| 3072 | 12 KB | Maximum accuracy |
+
+**Create index to match your embeddings**:
+
+```bash
+# For 768-dim embeddings
+npx wrangler vectorize create my-index --dimensions 768 --metric cosine
+
+# For 1536-dim embeddings
+npx wrangler vectorize create my-index --dimensions 1536 --metric cosine
+
+# For 3072-dim embeddings (Gemini default)
+npx wrangler vectorize create my-index --dimensions 3072 --metric cosine
+```
+
+---
+
+## Metric Selection
+
+Vectorize supports 3 distance metrics:
+
+### Cosine (Recommended)
+
+```bash
+npx wrangler vectorize create my-index --dimensions 768 --metric cosine
+```
+
+**When to use**:
+- ✅ Semantic search (most common)
+- ✅ Document similarity
+- ✅ RAG systems
+
+**Range**: 0 (different) to 1 (identical)
+
+### Euclidean
+
+```bash
+npx wrangler vectorize create my-index --dimensions 768 --metric euclidean
+```
+
+**When to use**:
+- ✅ Absolute distance matters
+- ✅ Magnitude is important
+
+**Range**: 0 (identical) to ∞ (very different)
+
+### Dot Product
+
+```bash
+npx wrangler vectorize create my-index --dimensions 768 --metric dot-product
+```
+
+**When to use**:
+- ✅ Pre-normalized vectors
+- ✅ Performance optimization
+
+**Range**: -1 to 1 (for normalized vectors)
+
+**Recommendation**: Use **cosine** for Gemini embeddings (most common and intuitive).
+
+---
+
+## Insert Patterns
+
+### Single Insert
+
+```typescript
+await env.VECTORIZE.insert([{
+  id: 'doc-1',
+  values: embedding,
+  metadata: {
+    text: 'Document content',
+    timestamp: Date.now(),
+    category: 'documentation'
+  }
+}]);
+```
+
+### Batch Insert
+
+```typescript
+const vectors = documents.map((doc, i) => ({
+  id: `doc-${i}`,
+  values: doc.embedding,
+  metadata: { text: doc.text }
+}));
+
+// Insert up to 100 vectors at once
+await env.VECTORIZE.insert(vectors);
+```
+
+### Upsert (Update or Insert)
+
+```typescript
+// Vectorize automatically updates if ID exists
+await env.VECTORIZE.insert([{
+  id: 'doc-1', // Existing ID
+  values: newEmbedding,
+  metadata: { text: 'Updated content' }
+}]);
+```
+
+---
+
+## Query Patterns
+
+### Basic Query
+
+```typescript
+const results = await env.VECTORIZE.query(queryEmbedding, {
+  topK: 5
+});
+
+console.log(results.matches);
+// [{ id: 'doc-1', score: 0.95 }, ...]
+```
+
+### Query with Metadata
+
+```typescript
+const results = await env.VECTORIZE.query(queryEmbedding, {
+  topK: 5,
+  returnMetadata: true
+});
+
+results.matches.forEach(match => {
+  console.log(match.id);           // 'doc-1'
+  console.log(match.score);        // 0.95
+  console.log(match.metadata.text); // 'Document content'
+});
+```
+
+### Query with Metadata Filtering (Future)
+
+```typescript
+// Coming soon: Filter by metadata
+const results = await env.VECTORIZE.query(queryEmbedding, {
+  topK: 5,
+  filter: { category: 'documentation' }
+});
+```
+
+---
+
+## Metadata Best Practices
+
+### What to Store
+
+```typescript
+await env.VECTORIZE.insert([{
+  id: 'doc-1',
+  values: embedding,
+  metadata: {
+    // ✅ Store these
+    text: 'The actual document content', // For retrieval
+    title: 'Document title',
+    url: 'https://example.com/doc',
+    timestamp: Date.now(),
+    category: 'product',
+
+    // ❌ Don't store these
+    embedding: embedding, // Already stored as values
+    largeObject: { /* ... */ } // Keep metadata small
+  }
+}]);
+```
+
+### Metadata Limits
+
+- **Max size**: ~1 KB per vector
+- **Best practice**: Store only what you need for retrieval/display
+- **For large data**: Store minimal metadata, fetch full data from D1/KV using ID
+
+---
+
+## Complete RAG Example
+
+```typescript
+interface Env {
+  GEMINI_API_KEY: string;
+  VECTORIZE: VectorizeIndex;
+}
+
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const url = new URL(request.url);
+
+    // Ingest: POST /ingest with { text: "..." }
+    if (url.pathname === '/ingest' && request.method === 'POST') {
+      const { text } = await request.json();
+
+      // 1. Generate embedding
+      const embeddingRes = await fetch(
+        'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent',
+        {
+          method: 'POST',
+          headers: {
+            'x-goog-api-key': env.GEMINI_API_KEY,
+            'Content-Type': 'application/json'
+          },
+          body: JSON.stringify({
+            content: { parts: [{ text }] },
+            taskType: 'RETRIEVAL_DOCUMENT',
+            outputDimensionality: 768
+          })
+        }
+      );
+
+      const embeddingData = await embeddingRes.json();
+      const embedding = embeddingData.embedding.values;
+
+      // 2. Store in Vectorize
+      await env.VECTORIZE.insert([{
+        id: `doc-${Date.now()}`,
+        values: embedding,
+        metadata: { text, timestamp: Date.now() }
+      }]);
+
+      return new Response(JSON.stringify({ success: true }));
+    }
+
+    // Query: POST /query with { query: "..." }
+    if (url.pathname === '/query' && request.method === 'POST') {
+      const { query } = await request.json();
+
+      // 1. Generate query embedding
+      const embeddingRes = await fetch(
+        'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent',
+        {
+          method: 'POST',
+          headers: {
+            'x-goog-api-key': env.GEMINI_API_KEY,
+            'Content-Type': 'application/json'
+          },
+          body: JSON.stringify({
+            content: { parts: [{ text: query }] },
+            taskType: 'RETRIEVAL_QUERY',
+            outputDimensionality: 768
+          })
+        }
+      );
+
+      const embeddingData = await embeddingRes.json();
+      const embedding = embeddingData.embedding.values;
+
+      // 2. Search Vectorize
+      const results = await env.VECTORIZE.query(embedding, {
+        topK: 5,
+        returnMetadata: true
+      });
+
+      return new Response(JSON.stringify({
+        query,
+        results: results.matches.map(m => ({
+          id: m.id,
+          score: m.score,
+          text: m.metadata?.text
+        }))
+      }));
+    }
+
+    return new Response('Not found', { status: 404 });
+  }
+};
+```
+
+---
+
+## Index Management
+
+### List Indexes
+
+```bash
+npx wrangler vectorize list
+```
+
+### Get Index Info
+
+```bash
+npx wrangler vectorize get gemini-embeddings
+```
+
+### Delete Index
+
+```bash
+npx wrangler vectorize delete gemini-embeddings
+```
+
+**CRITICAL**: Deleting an index deletes all vectors permanently.
+
+---
+
+## Limitations & Quotas
+
+| Feature | Free Plan | Paid Plans |
+|---------|-----------|------------|
+| Indexes per account | 100 | 100 |
+| Vectors per index | 200,000 | 5,000,000+ |
+| Queries per day | 30,000,000 | Unlimited |
+| Dimensions | Up to 1536 | Up to 3072 |
+
+**Source**: https://developers.cloudflare.com/vectorize/platform/pricing/
+
+---
+
+## Best Practices
+
+### 1. Choose Dimensions Wisely
+
+```typescript
+// ✅ 768 dimensions (recommended)
+// - Good accuracy
+// - Low storage
+// - Fast queries
+
+// ⚠️ 3072 dimensions (if accuracy is critical)
+// - Best accuracy
+// - 4x storage
+// - Slower queries
+```
+
+### 2. Use Metadata for Context
+
+```typescript
+await env.VECTORIZE.insert([{
+  id: 'doc-1',
+  values: embedding,
+  metadata: {
+    text: 'Store the actual text here for retrieval',
+    url: 'https://...',
+    timestamp: Date.now()
+  }
+}]);
+```
+
+### 3. Implement Caching
+
+```typescript
+// Cache embeddings in KV
+const cached = await env.KV.get(`embedding:${textHash}`);
+if (cached) {
+  return JSON.parse(cached);
+}
+
+const embedding = await generateEmbedding(text);
+await env.KV.put(`embedding:${textHash}`, JSON.stringify(embedding), {
+  expirationTtl: 86400 // 24 hours
+});
+```
+
+### 4. Monitor Usage
+
+```bash
+# Check index stats
+npx wrangler vectorize get gemini-embeddings
+
+# Shows:
+# - Total vectors
+# - Dimensions
+# - Metric type
+```
+
+---
+
+## Troubleshooting
+
+### Dimension Mismatch Error
+
+```
+Error: Vector dimensions do not match. Expected 768, got 3072
+```
+
+**Solution**: Ensure embedding `outputDimensionality` matches index dimensions.
+
+### No Results Found
+
+**Possible causes**:
+1. Index is empty (no vectors inserted)
+2. Query embedding is wrong task type (use RETRIEVAL_QUERY)
+3. Similarity threshold too high
+
+**Solution**: Check index has vectors, use correct task types.
+
+---
+
+## Official Documentation
+
+- **Vectorize Docs**: https://developers.cloudflare.com/vectorize/
+- **Pricing**: https://developers.cloudflare.com/vectorize/platform/pricing/
+- **Wrangler CLI**: https://developers.cloudflare.com/workers/wrangler/
--- a/scripts/check-versions.sh
+++ b/scripts/check-versions.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+# Check Google GenAI SDK and dependencies versions
+# Usage: ./scripts/check-versions.sh
+
+echo "🔍 Checking package versions for google-gemini-embeddings skill..."
+echo ""
+
+# Check if npm is available
+if ! command -v npm &> /dev/null; then
+    echo "❌ npm not found. Please install Node.js first."
+    exit 1
+fi
+
+# Check @google/genai
+echo "📦 @google/genai"
+CURRENT=$(npm view @google/genai version 2>/dev/null)
+if [ $? -eq 0 ]; then
+    echo "   Latest: $CURRENT"
+    echo "   Skill tested with: 1.27.0"
+
+    if [ "$CURRENT" != "1.27.0" ]; then
+        echo "   ⚠️  New version available. Consider testing and updating skill."
+    else
+        echo "   ✅ Up to date"
+    fi
+else
+    echo "   ❌ Error checking version"
+fi
+
+echo ""
+
+# Check TypeScript
+echo "📦 typescript"
+CURRENT=$(npm view typescript version 2>/dev/null)
+if [ $? -eq 0 ]; then
+    echo "   Latest: $CURRENT"
+    echo "   Skill tested with: 5.6.0"
+
+    if [ "$CURRENT" != "5.6.0" ]; then
+        echo "   ℹ️  TypeScript version is different. Usually not breaking."
+    else
+        echo "   ✅ Up to date"
+    fi
+else
+    echo "   ❌ Error checking version"
+fi
+
+echo ""
+echo "✨ Version check complete!"
+echo ""
+echo "To install/update packages:"
+echo "  npm install @google/genai@latest typescript@latest"
--- a/templates/basic-embeddings.ts
+++ b/templates/basic-embeddings.ts
@@ -0,0 +1,99 @@
+/**
+ * Basic Gemini Embeddings Example (SDK)
+ *
+ * Demonstrates single text embedding generation using the @google/genai SDK.
+ *
+ * Setup:
+ * 1. npm install @google/genai@^1.27.0
+ * 2. export GEMINI_API_KEY="your-api-key"
+ * 3. Get API key from: https://aistudio.google.com/apikey
+ *
+ * Usage:
+ * npx tsx basic-embeddings.ts
+ */
+
+import { GoogleGenAI } from "@google/genai";
+
+async function generateEmbedding(text: string) {
+  // Initialize client with API key
+  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');
+  }
+
+  console.log(`\nGenerating embedding for: "${text}"\n`);
+
+  // Generate embedding
+  const response = await ai.models.embedContent({
+    model: 'gemini-embedding-001', // Stable production model
+    content: text,
+    config: {
+      taskType: 'SEMANTIC_SIMILARITY', // Optimize for similarity comparison
+      outputDimensionality: 768 // Recommended for most use cases
+    }
+  });
+
+  const embedding = response.embedding.values;
+
+  console.log(`✅ Embedding generated successfully!`);
+  console.log(`Dimensions: ${embedding.length}`);
+  console.log(`First 10 values: [${embedding.slice(0, 10).map(v => v.toFixed(4)).join(', ')}...]`);
+  console.log(`\nVector magnitude: ${Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0)).toFixed(4)}`);
+
+  return embedding;
+}
+
+// Example usage
+async function main() {
+  try {
+    const text = "What is the meaning of life?";
+    const embedding = await generateEmbedding(text);
+
+    // Compare with another text
+    const text2 = "What is the purpose of existence?";
+    console.log(`\nGenerating embedding for: "${text2}"\n`);
+
+    const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
+    const response2 = await ai.models.embedContent({
+      model: 'gemini-embedding-001',
+      content: text2,
+      config: {
+        taskType: 'SEMANTIC_SIMILARITY',
+        outputDimensionality: 768
+      }
+    });
+
+    const embedding2 = response2.embedding.values;
+
+    // Calculate cosine similarity
+    const cosineSimilarity = (a: number[], b: number[]): number => {
+      let dotProduct = 0, magA = 0, magB = 0;
+      for (let i = 0; i < a.length; i++) {
+        dotProduct += a[i] * b[i];
+        magA += a[i] * a[i];
+        magB += b[i] * b[i];
+      }
+      return dotProduct / (Math.sqrt(magA) * Math.sqrt(magB));
+    };
+
+    const similarity = cosineSimilarity(embedding, embedding2);
+    console.log(`\n🔗 Similarity between texts: ${(similarity * 100).toFixed(2)}%`);
+    console.log('(1.0 = identical, 0.0 = completely different)\n');
+
+  } catch (error: any) {
+    console.error('❌ Error:', error.message);
+
+    if (error.status === 401) {
+      console.error('\nCheck that GEMINI_API_KEY is set correctly');
+    } else if (error.status === 429) {
+      console.error('\nRate limit exceeded. Free tier: 100 requests/minute');
+    }
+
+    process.exit(1);
+  }
+}
+
+main();
--- a/templates/batch-embeddings.ts
+++ b/templates/batch-embeddings.ts
@@ -0,0 +1,240 @@
+/**
+ * Batch Embeddings with Rate Limiting
+ *
+ * Demonstrates processing multiple texts with proper rate limiting
+ * and exponential backoff for production use.
+ *
+ * Setup:
+ * 1. npm install @google/genai@^1.27.0
+ * 2. export GEMINI_API_KEY="your-api-key"
+ *
+ * Usage:
+ * npx tsx batch-embeddings.ts
+ */
+
+import { GoogleGenAI } from "@google/genai";
+
+interface RateLimitConfig {
+  requestsPerMinute: number;
+  maxRetries: number;
+  initialDelayMs: number;
+}
+
+class EmbeddingService {
+  private ai: GoogleGenAI;
+  private config: RateLimitConfig;
+  private requestTimes: number[] = [];
+
+  constructor(apiKey: string, config?: Partial<RateLimitConfig>) {
+    this.ai = new GoogleGenAI({ apiKey });
+    this.config = {
+      requestsPerMinute: config?.requestsPerMinute || 100, // Free tier limit
+      maxRetries: config?.maxRetries || 3,
+      initialDelayMs: config?.initialDelayMs || 1000
+    };
+  }
+
+  /**
+   * Wait if needed to respect rate limits
+   */
+  private async enforceRateLimit(): Promise<void> {
+    const now = Date.now();
+    const oneMinuteAgo = now - 60000;
+
+    // Remove requests older than 1 minute
+    this.requestTimes = this.requestTimes.filter(time => time > oneMinuteAgo);
+
+    // If at limit, wait until oldest request expires
+    if (this.requestTimes.length >= this.config.requestsPerMinute) {
+      const oldestRequest = this.requestTimes[0];
+      const waitTime = 60000 - (now - oldestRequest) + 100; // +100ms buffer
+
+      if (waitTime > 0) {
+        console.log(`⏳ Rate limit reached. Waiting ${(waitTime / 1000).toFixed(1)}s...`);
+        await new Promise(resolve => setTimeout(resolve, waitTime));
+      }
+    }
+
+    this.requestTimes.push(Date.now());
+  }
+
+  /**
+   * Generate embedding with retry logic
+   */
+  async embedText(
+    text: string,
+    options: {
+      taskType?: string;
+      outputDimensionality?: number;
+    } = {}
+  ): Promise<number[]> {
+    const {
+      taskType = 'SEMANTIC_SIMILARITY',
+      outputDimensionality = 768
+    } = options;
+
+    for (let attempt = 0; attempt < this.config.maxRetries; attempt++) {
+      try {
+        await this.enforceRateLimit();
+
+        const response = await this.ai.models.embedContent({
+          model: 'gemini-embedding-001',
+          content: text,
+          config: { taskType, outputDimensionality }
+        });
+
+        return response.embedding.values;
+
+      } catch (error: any) {
+        const isLastAttempt = attempt === this.config.maxRetries - 1;
+
+        // Retry on rate limit errors
+        if (error.status === 429 && !isLastAttempt) {
+          const delay = this.config.initialDelayMs * Math.pow(2, attempt);
+          console.log(`⚠️ Rate limit error. Retrying in ${delay / 1000}s... (attempt ${attempt + 1}/${this.config.maxRetries})`);
+          await new Promise(resolve => setTimeout(resolve, delay));
+          continue;
+        }
+
+        throw error;
+      }
+    }
+
+    throw new Error(`Failed after ${this.config.maxRetries} retries`);
+  }
+
+  /**
+   * Batch embed multiple texts
+   */
+  async embedBatch(
+    texts: string[],
+    options: {
+      taskType?: string;
+      outputDimensionality?: number;
+      onProgress?: (current: number, total: number) => void;
+    } = {}
+  ): Promise<number[][]> {
+    const {
+      taskType = 'RETRIEVAL_DOCUMENT',
+      outputDimensionality = 768,
+      onProgress
+    } = options;
+
+    console.log(`\n📊 Embedding ${texts.length} texts...`);
+    console.log(`Rate limit: ${this.config.requestsPerMinute} RPM\n`);
+
+    const embeddings: number[][] = [];
+    const startTime = Date.now();
+
+    for (let i = 0; i < texts.length; i++) {
+      const text = texts[i];
+      const embedding = await this.embedText(text, { taskType, outputDimensionality });
+      embeddings.push(embedding);
+
+      if (onProgress) {
+        onProgress(i + 1, texts.length);
+      }
+
+      // Progress logging
+      if ((i + 1) % 10 === 0 || i === texts.length - 1) {
+        const elapsed = (Date.now() - startTime) / 1000;
+        const rate = (i + 1) / elapsed;
+        const remaining = texts.length - (i + 1);
+        const eta = remaining / rate;
+
+        console.log(`✅ ${i + 1}/${texts.length} (${rate.toFixed(1)} texts/sec, ETA: ${eta.toFixed(1)}s)`);
+      }
+    }
+
+    const totalTime = (Date.now() - startTime) / 1000;
+    console.log(`\n✨ Completed in ${totalTime.toFixed(1)}s (avg: ${(texts.length / totalTime).toFixed(1)} texts/sec)\n`);
+
+    return embeddings;
+  }
+
+  /**
+   * Use batch API for multiple texts at once (more efficient)
+   */
+  async embedBatchAPI(
+    texts: string[],
+    options: {
+      taskType?: string;
+      outputDimensionality?: number;
+    } = {}
+  ): Promise<number[][]> {
+    const {
+      taskType = 'RETRIEVAL_DOCUMENT',
+      outputDimensionality = 768
+    } = options;
+
+    await this.enforceRateLimit();
+
+    const response = await this.ai.models.embedContent({
+      model: 'gemini-embedding-001',
+      contents: texts, // Array of strings
+      config: { taskType, outputDimensionality }
+    });
+
+    return response.embeddings.map(e => e.values);
+  }
+}
+
+// Example usage
+async function main() {
+  try {
+    const apiKey = process.env.GEMINI_API_KEY;
+    if (!apiKey) {
+      throw new Error('GEMINI_API_KEY environment variable not set');
+    }
+
+    const service = new EmbeddingService(apiKey, {
+      requestsPerMinute: 100, // Free tier
+      maxRetries: 3
+    });
+
+    // Sample documents
+    const documents = [
+      "What is the meaning of life?",
+      "How does photosynthesis work?",
+      "Explain quantum mechanics in simple terms",
+      "What is the history of artificial intelligence?",
+      "How do neural networks learn?",
+      "What is the difference between machine learning and deep learning?",
+      "Explain the theory of relativity",
+      "What is climate change?",
+      "How does the human brain work?",
+      "What is the future of technology?"
+    ];
+
+    console.log('🚀 Method 1: Sequential with rate limiting');
+    const embeddings1 = await service.embedBatch(documents, {
+      taskType: 'RETRIEVAL_DOCUMENT',
+      outputDimensionality: 768,
+      onProgress: (current, total) => {
+        // Optional: Update progress bar, database, etc.
+      }
+    });
+
+    console.log('\n🚀 Method 2: Batch API (single request)');
+    const startTime = Date.now();
+    const embeddings2 = await service.embedBatchAPI(documents, {
+      taskType: 'RETRIEVAL_DOCUMENT',
+      outputDimensionality: 768
+    });
+    const elapsed = (Date.now() - startTime) / 1000;
+
+    console.log(`✨ Completed in ${elapsed.toFixed(1)}s (${documents.length} texts in 1 request)\n`);
+
+    // Verify results
+    console.log('📈 Results:');
+    console.log(`Embeddings generated: ${embeddings2.length}`);
+    console.log(`Dimensions per embedding: ${embeddings2[0].length}`);
+    console.log(`Total vectors: ${embeddings2.length * embeddings2[0].length}`);
+
+  } catch (error: any) {
+    console.error('❌ Error:', error.message);
+    process.exit(1);
+  }
+}
+
+main();
--- a/templates/clustering.ts
+++ b/templates/clustering.ts
@@ -0,0 +1,311 @@
+/**
+ * Document Clustering with Gemini Embeddings
+ *
+ * Demonstrates automatic grouping of similar documents using K-means clustering.
+ * Useful for topic modeling, content organization, and duplicate detection.
+ *
+ * Setup:
+ * 1. npm install @google/genai@^1.27.0
+ * 2. export GEMINI_API_KEY="your-api-key"
+ *
+ * Usage:
+ * npx tsx clustering.ts
+ */
+
+import { GoogleGenAI } from "@google/genai";
+
+interface Document {
+  id: string;
+  text: string;
+  embedding?: number[];
+}
+
+interface Cluster {
+  id: number;
+  centroid: number[];
+  documents: Document[];
+}
+
+/**
+ * Calculate cosine similarity
+ */
+function cosineSimilarity(a: number[], b: number[]): number {
+  if (a.length !== b.length) {
+    throw new Error('Vector dimensions must match');
+  }
+
+  let dotProduct = 0, magA = 0, magB = 0;
+
+  for (let i = 0; i < a.length; i++) {
+    dotProduct += a[i] * b[i];
+    magA += a[i] * a[i];
+    magB += b[i] * b[i];
+  }
+
+  return dotProduct / (Math.sqrt(magA) * Math.sqrt(magB));
+}
+
+/**
+ * K-means clustering algorithm
+ */
+function kMeansClustering(
+  documents: Document[],
+  k: number = 3,
+  maxIterations: number = 100
+): Cluster[] {
+  if (documents.length === 0 || !documents[0].embedding) {
+    throw new Error('Documents must have embeddings');
+  }
+
+  const embeddings = documents.map(d => d.embedding!);
+
+  // 1. Initialize centroids randomly
+  const centroids: number[][] = [];
+  const usedIndices = new Set<number>();
+
+  for (let i = 0; i < k; i++) {
+    let randomIndex: number;
+    do {
+      randomIndex = Math.floor(Math.random() * embeddings.length);
+    } while (usedIndices.has(randomIndex));
+
+    usedIndices.add(randomIndex);
+    centroids.push([...embeddings[randomIndex]]);
+  }
+
+  console.log(`🔄 Starting K-means clustering (k=${k}, max iterations=${maxIterations})\n`);
+
+  // 2. Iterate until convergence
+  let iteration = 0;
+  let converged = false;
+
+  while (iteration < maxIterations && !converged) {
+    // Assign each document to nearest centroid
+    const clusters: Document[][] = Array(k).fill(null).map(() => []);
+
+    documents.forEach((doc, idx) => {
+      const embedding = embeddings[idx];
+      let maxSimilarity = -Infinity;
+      let closestCluster = 0;
+
+      centroids.forEach((centroid, i) => {
+        const similarity = cosineSimilarity(embedding, centroid);
+        if (similarity > maxSimilarity) {
+          maxSimilarity = similarity;
+          closestCluster = i;
+        }
+      });
+
+      clusters[closestCluster].push(doc);
+    });
+
+    // Update centroids (average of cluster members)
+    converged = true;
+    clusters.forEach((cluster, i) => {
+      if (cluster.length === 0) return;
+
+      const newCentroid = cluster[0].embedding!.map((_, dim) =>
+        cluster.reduce((sum, doc) => sum + doc.embedding![dim], 0) / cluster.length
+      );
+
+      // Check if centroid changed significantly
+      const similarity = cosineSimilarity(centroids[i], newCentroid);
+      if (similarity < 0.9999) {
+        converged = false;
+      }
+
+      centroids[i] = newCentroid;
+    });
+
+    iteration++;
+
+    if (iteration % 10 === 0) {
+      console.log(`Iteration ${iteration}...`);
+    }
+  }
+
+  console.log(`✅ Converged after ${iteration} iterations\n`);
+
+  // Build final clusters
+  const finalClusters: Cluster[] = centroids.map((centroid, i) => ({
+    id: i,
+    centroid,
+    documents: documents.filter((doc) => {
+      const similarities = centroids.map(c => cosineSimilarity(doc.embedding!, c));
+      return similarities.indexOf(Math.max(...similarities)) === i;
+    })
+  }));
+
+  return finalClusters;
+}
+
+/**
+ * Clustering by similarity threshold (alternative to K-means)
+ */
+function clusterByThreshold(
+  documents: Document[],
+  threshold: number = 0.8
+): Cluster[] {
+  if (documents.length === 0 || !documents[0].embedding) {
+    throw new Error('Documents must have embeddings');
+  }
+
+  const clusters: Cluster[] = [];
+  const assigned = new Set<number>();
+
+  documents.forEach((doc, idx) => {
+    if (assigned.has(idx)) return;
+
+    const clusterDocs = [doc];
+    assigned.add(idx);
+
+    documents.forEach((otherDoc, otherIdx) => {
+      if (idx !== otherIdx && !assigned.has(otherIdx)) {
+        const similarity = cosineSimilarity(doc.embedding!, otherDoc.embedding!);
+
+        if (similarity >= threshold) {
+          clusterDocs.push(otherDoc);
+          assigned.add(otherIdx);
+        }
+      }
+    });
+
+    clusters.push({
+      id: clusters.length,
+      centroid: doc.embedding!,
+      documents: clusterDocs
+    });
+  });
+
+  return clusters;
+}
+
+/**
+ * Print cluster summary
+ */
+function printClusters(clusters: Cluster[], method: string): void {
+  console.log(`━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`);
+  console.log(`${method} Results`);
+  console.log(`━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n`);
+
+  clusters.forEach(cluster => {
+    console.log(`📁 Cluster ${cluster.id + 1} (${cluster.documents.length} documents):`);
+    console.log(`${'─'.repeat(50)}`);
+
+    cluster.documents.forEach(doc => {
+      const preview = doc.text.substring(0, 80) + (doc.text.length > 80 ? '...' : '');
+      console.log(`  • [${doc.id}] ${preview}`);
+    });
+
+    console.log('');
+  });
+
+  console.log(`Total clusters: ${clusters.length}\n`);
+}
+
+// Example usage
+async function main() {
+  try {
+    const apiKey = process.env.GEMINI_API_KEY;
+    if (!apiKey) {
+      throw new Error('GEMINI_API_KEY environment variable not set');
+    }
+
+    const ai = new GoogleGenAI({ apiKey });
+
+    // Sample documents (3 topics: Geography, AI/ML, Food)
+    const documents: Document[] = [
+      // Geography
+      { id: 'doc1', text: 'Paris is the capital of France. It is known for the Eiffel Tower and the Louvre Museum.' },
+      { id: 'doc2', text: 'London is the capital of the United Kingdom and home to Big Ben and Buckingham Palace.' },
+      { id: 'doc3', text: 'Rome is the capital of Italy and famous for the Colosseum and Vatican City.' },
+
+      // AI/ML
+      { id: 'doc4', text: 'Machine learning is a subset of artificial intelligence that enables computers to learn from data.' },
+      { id: 'doc5', text: 'Deep learning uses neural networks with multiple layers to learn complex patterns in data.' },
+      { id: 'doc6', text: 'Natural language processing is a branch of AI that helps computers understand human language.' },
+
+      // Food
+      { id: 'doc7', text: 'Pizza originated in Italy and is now popular worldwide. It typically has a tomato base and cheese.' },
+      { id: 'doc8', text: 'Sushi is a Japanese dish made with vinegared rice and various ingredients like raw fish.' },
+      { id: 'doc9', text: 'Tacos are a traditional Mexican food consisting of a tortilla filled with various ingredients.' }
+    ];
+
+    console.log(`\n📚 Generating embeddings for ${documents.length} documents...\n`);
+
+    // Generate embeddings
+    for (const doc of documents) {
+      const response = await ai.models.embedContent({
+        model: 'gemini-embedding-001',
+        content: doc.text,
+        config: {
+          taskType: 'CLUSTERING', // ← Optimized for clustering
+          outputDimensionality: 768
+        }
+      });
+
+      doc.embedding = response.embedding.values;
+      console.log(`✅ Embedded: ${doc.id}`);
+    }
+
+    console.log('');
+
+    // Method 1: K-means clustering
+    const kMeansClusters = kMeansClustering(documents, 3, 100);
+    printClusters(kMeansClusters, 'K-Means Clustering (k=3)');
+
+    // Method 2: Threshold-based clustering
+    console.log('🔄 Running threshold-based clustering (threshold=0.7)...\n');
+    const thresholdClusters = clusterByThreshold(documents, 0.7);
+    printClusters(thresholdClusters, 'Threshold-Based Clustering (≥70% similarity)');
+
+    // Example: Find intra-cluster similarities
+    console.log(`━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`);
+    console.log('Cluster Quality Analysis');
+    console.log(`━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n`);
+
+    kMeansClusters.forEach(cluster => {
+      if (cluster.documents.length < 2) return;
+
+      const similarities: number[] = [];
+
+      for (let i = 0; i < cluster.documents.length; i++) {
+        for (let j = i + 1; j < cluster.documents.length; j++) {
+          const sim = cosineSimilarity(
+            cluster.documents[i].embedding!,
+            cluster.documents[j].embedding!
+          );
+          similarities.push(sim);
+        }
+      }
+
+      const avgSimilarity = similarities.reduce((a, b) => a + b, 0) / similarities.length;
+      const minSimilarity = Math.min(...similarities);
+      const maxSimilarity = Math.max(...similarities);
+
+      console.log(`Cluster ${cluster.id + 1}:`);
+      console.log(`  Documents: ${cluster.documents.map(d => d.id).join(', ')}`);
+      console.log(`  Avg similarity: ${(avgSimilarity * 100).toFixed(1)}%`);
+      console.log(`  Min similarity: ${(minSimilarity * 100).toFixed(1)}%`);
+      console.log(`  Max similarity: ${(maxSimilarity * 100).toFixed(1)}%`);
+      console.log('');
+    });
+
+  } catch (error: any) {
+    console.error('❌ Error:', error.message);
+    process.exit(1);
+  }
+}
+
+main();
+
+/**
+ * Expected output:
+ *
+ * Cluster 1: Geography documents (Paris, London, Rome)
+ * Cluster 2: AI/ML documents (Machine learning, Deep learning, NLP)
+ * Cluster 3: Food documents (Pizza, Sushi, Tacos)
+ *
+ * This demonstrates how embeddings capture semantic meaning,
+ * allowing automatic topic discovery without manual labeling.
+ */
--- a/templates/embeddings-fetch.ts
+++ b/templates/embeddings-fetch.ts
@@ -0,0 +1,157 @@
+/**
+ * Gemini Embeddings with Fetch (Cloudflare Workers)
+ *
+ * Demonstrates embedding generation using fetch API instead of SDK.
+ * Perfect for Cloudflare Workers and edge environments.
+ *
+ * Setup:
+ * 1. Add GEMINI_API_KEY to wrangler.jsonc secrets
+ * 2. npx wrangler secret put GEMINI_API_KEY
+ * 3. Deploy: npx wrangler deploy
+ *
+ * Usage:
+ * GET /?text=your+text+here
+ */
+
+interface Env {
+  GEMINI_API_KEY: string;
+}
+
+interface EmbeddingRequest {
+  content: {
+    parts: Array<{ text: string }>;
+  };
+  taskType?: string;
+  outputDimensionality?: number;
+}
+
+interface EmbeddingResponse {
+  embedding: {
+    values: number[];
+  };
+}
+
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    // CORS headers for browser access
+    const corsHeaders = {
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Methods': 'GET, POST, OPTIONS',
+      'Access-Control-Allow-Headers': 'Content-Type'
+    };
+
+    // Handle CORS preflight
+    if (request.method === 'OPTIONS') {
+      return new Response(null, { headers: corsHeaders });
+    }
+
+    try {
+      // Get text from query param or request body
+      const url = new URL(request.url);
+      let text: string;
+
+      if (request.method === 'POST') {
+        const body = await request.json<{ text: string }>();
+        text = body.text;
+      } else {
+        text = url.searchParams.get('text') || 'What is the meaning of life?';
+      }
+
+      console.log(`Generating embedding for: "${text}"`);
+
+      // Prepare request
+      const embeddingRequest: EmbeddingRequest = {
+        content: {
+          parts: [{ text }]
+        },
+        taskType: 'SEMANTIC_SIMILARITY',
+        outputDimensionality: 768
+      };
+
+      // Call Gemini API
+      const response = await fetch(
+        'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent',
+        {
+          method: 'POST',
+          headers: {
+            'x-goog-api-key': env.GEMINI_API_KEY,
+            'Content-Type': 'application/json'
+          },
+          body: JSON.stringify(embeddingRequest)
+        }
+      );
+
+      if (!response.ok) {
+        const error = await response.text();
+        throw new Error(`Gemini API error: ${response.status} - ${error}`);
+      }
+
+      const data = await response.json<EmbeddingResponse>();
+      const embedding = data.embedding.values;
+
+      // Calculate vector magnitude
+      const magnitude = Math.sqrt(
+        embedding.reduce((sum, v) => sum + v * v, 0)
+      );
+
+      // Return formatted response
+      return new Response(JSON.stringify({
+        success: true,
+        text,
+        embedding: {
+          dimensions: embedding.length,
+          magnitude: magnitude.toFixed(4),
+          firstValues: embedding.slice(0, 10).map(v => parseFloat(v.toFixed(4))),
+          fullVector: embedding
+        }
+      }, null, 2), {
+        headers: {
+          'Content-Type': 'application/json',
+          ...corsHeaders
+        }
+      });
+
+    } catch (error: any) {
+      console.error('Error:', error.message);
+
+      return new Response(JSON.stringify({
+        success: false,
+        error: error.message,
+        hint: error.message.includes('401')
+          ? 'Check GEMINI_API_KEY secret is set'
+          : error.message.includes('429')
+          ? 'Rate limit exceeded (Free tier: 100 RPM)'
+          : 'Check error message for details'
+      }, null, 2), {
+        status: 500,
+        headers: {
+          'Content-Type': 'application/json',
+          ...corsHeaders
+        }
+      });
+    }
+  }
+};
+
+/**
+ * Example wrangler.jsonc configuration:
+ *
+ * {
+ *   "name": "gemini-embeddings-worker",
+ *   "main": "src/index.ts",
+ *   "compatibility_date": "2025-10-25",
+ *   "vars": {
+ *     "ENVIRONMENT": "production"
+ *   }
+ * }
+ *
+ * Set secret:
+ * npx wrangler secret put GEMINI_API_KEY
+ *
+ * Test locally:
+ * npx wrangler dev
+ * curl "http://localhost:8787/?text=Hello+world"
+ *
+ * Deploy:
+ * npx wrangler deploy
+ */
--- a/templates/package.json
+++ b/templates/package.json
@@ -0,0 +1,22 @@
+{
+  "name": "gemini-embeddings-example",
+  "version": "1.0.0",
+  "description": "Google Gemini embeddings API examples",
+  "type": "module",
+  "scripts": {
+    "dev": "tsx watch src/index.ts",
+    "build": "tsc",
+    "start": "node dist/index.js"
+  },
+  "dependencies": {
+    "@google/genai": "^1.27.0"
+  },
+  "devDependencies": {
+    "@types/node": "^22.0.0",
+    "tsx": "^4.19.0",
+    "typescript": "^5.6.0"
+  },
+  "engines": {
+    "node": ">=18.0.0"
+  }
+}
--- a/templates/rag-with-vectorize.ts
+++ b/templates/rag-with-vectorize.ts
@@ -0,0 +1,361 @@
+/**
+ * Complete RAG Implementation with Gemini Embeddings + Cloudflare Vectorize
+ *
+ * Demonstrates end-to-end RAG (Retrieval Augmented Generation):
+ * 1. Document ingestion (chunking + embedding + storage)
+ * 2. Query processing (embedding + vector search)
+ * 3. Response generation (context + LLM)
+ *
+ * Setup:
+ * 1. Create Vectorize index:
+ *    npx wrangler vectorize create gemini-embeddings --dimensions 768 --metric cosine
+ *
+ * 2. Add to wrangler.jsonc:
+ *    {
+ *      "vectorize": {
+ *        "bindings": [{
+ *          "binding": "VECTORIZE",
+ *          "index_name": "gemini-embeddings"
+ *        }]
+ *      }
+ *    }
+ *
+ * 3. Set secret:
+ *    npx wrangler secret put GEMINI_API_KEY
+ *
+ * 4. Deploy:
+ *    npx wrangler deploy
+ *
+ * Usage:
+ * POST /ingest - Upload documents
+ * POST /query - Ask questions
+ * GET /health - Check status
+ */
+
+interface Env {
+  GEMINI_API_KEY: string;
+  VECTORIZE: VectorizeIndex;
+}
+
+interface VectorizeVector {
+  id: string;
+  values: number[];
+  metadata?: Record<string, any>;
+}
+
+interface VectorizeMatch {
+  id: string;
+  score: number;
+  metadata?: Record<string, any>;
+}
+
+interface VectorizeIndex {
+  insert(vectors: VectorizeVector[]): Promise<{ count: number }>;
+  query(
+    vector: number[],
+    options: { topK: number; returnMetadata?: boolean }
+  ): Promise<{ matches: VectorizeMatch[] }>;
+  getByIds(ids: string[]): Promise<VectorizeVector[]>;
+  deleteByIds(ids: string[]): Promise<{ count: number }>;
+}
+
+/**
+ * Document chunking for better retrieval
+ */
+function chunkDocument(
+  text: string,
+  chunkSize: number = 500,
+  overlap: number = 50
+): string[] {
+  const words = text.split(/\s+/);
+  const chunks: string[] = [];
+
+  for (let i = 0; i < words.length; i += chunkSize - overlap) {
+    const chunk = words.slice(i, i + chunkSize).join(' ');
+    if (chunk.trim().length > 0) {
+      chunks.push(chunk.trim());
+    }
+  }
+
+  return chunks;
+}
+
+/**
+ * Generate embedding using Gemini API
+ */
+async function generateEmbedding(
+  text: string,
+  apiKey: string,
+  taskType: string = 'RETRIEVAL_DOCUMENT'
+): Promise<number[]> {
+  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,
+        outputDimensionality: 768 // MUST match Vectorize index dimensions
+      })
+    }
+  );
+
+  if (!response.ok) {
+    const error = await response.text();
+    throw new Error(`Embedding API error: ${response.status} - ${error}`);
+  }
+
+  const data = await response.json<{ embedding: { values: number[] } }>();
+  return data.embedding.values;
+}
+
+/**
+ * Generate response using Gemini API
+ */
+async function generateResponse(
+  context: string,
+  query: string,
+  apiKey: string
+): Promise<string> {
+  const response = await fetch(
+    'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent',
+    {
+      method: 'POST',
+      headers: {
+        'x-goog-api-key': apiKey,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        contents: [{
+          parts: [{
+            text: `You are a helpful assistant. Answer the question based ONLY on the provided context.
+
+Context:
+${context}
+
+Question: ${query}
+
+Answer:`
+          }]
+        }]
+      })
+    }
+  );
+
+  if (!response.ok) {
+    const error = await response.text();
+    throw new Error(`Generation API error: ${response.status} - ${error}`);
+  }
+
+  const data = await response.json<{
+    candidates: Array<{
+      content: { parts: Array<{ text: string }> };
+    }>;
+  }>();
+
+  return data.candidates[0]?.content?.parts[0]?.text || 'No response generated';
+}
+
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const url = new URL(request.url);
+    const corsHeaders = {
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Methods': 'GET, POST, OPTIONS',
+      'Access-Control-Allow-Headers': 'Content-Type'
+    };
+
+    if (request.method === 'OPTIONS') {
+      return new Response(null, { headers: corsHeaders });
+    }
+
+    try {
+      // Health check
+      if (url.pathname === '/health') {
+        return new Response(JSON.stringify({
+          status: 'ok',
+          vectorize: 'connected',
+          gemini: 'ready'
+        }), {
+          headers: { 'Content-Type': 'application/json', ...corsHeaders }
+        });
+      }
+
+      // Document ingestion
+      if (url.pathname === '/ingest' && request.method === 'POST') {
+        const { documents } = await request.json<{ documents: Array<{ id: string; text: string }> }>();
+
+        if (!documents || !Array.isArray(documents)) {
+          return new Response(JSON.stringify({ error: 'Invalid request: documents array required' }), {
+            status: 400,
+            headers: { 'Content-Type': 'application/json', ...corsHeaders }
+          });
+        }
+
+        console.log(`📥 Ingesting ${documents.length} documents...`);
+
+        const vectors: VectorizeVector[] = [];
+
+        for (const doc of documents) {
+          // Chunk document
+          const chunks = chunkDocument(doc.text, 500, 50);
+          console.log(`📄 Document ${doc.id}: ${chunks.length} chunks`);
+
+          // Generate embeddings for each chunk
+          for (let i = 0; i < chunks.length; i++) {
+            const embedding = await generateEmbedding(
+              chunks[i],
+              env.GEMINI_API_KEY,
+              'RETRIEVAL_DOCUMENT' // ← Documents for indexing
+            );
+
+            vectors.push({
+              id: `${doc.id}-chunk-${i}`,
+              values: embedding,
+              metadata: {
+                documentId: doc.id,
+                chunkIndex: i,
+                text: chunks[i],
+                timestamp: Date.now()
+              }
+            });
+          }
+        }
+
+        // Insert into Vectorize
+        const result = await env.VECTORIZE.insert(vectors);
+
+        console.log(`✅ Ingested ${result.count} vectors`);
+
+        return new Response(JSON.stringify({
+          success: true,
+          documentsProcessed: documents.length,
+          chunksCreated: vectors.length,
+          vectorsInserted: result.count
+        }), {
+          headers: { 'Content-Type': 'application/json', ...corsHeaders }
+        });
+      }
+
+      // Query processing (RAG)
+      if (url.pathname === '/query' && request.method === 'POST') {
+        const { query, topK = 5 } = await request.json<{ query: string; topK?: number }>();
+
+        if (!query) {
+          return new Response(JSON.stringify({ error: 'Invalid request: query required' }), {
+            status: 400,
+            headers: { 'Content-Type': 'application/json', ...corsHeaders }
+          });
+        }
+
+        console.log(`🔍 Query: "${query}"`);
+
+        // 1. Generate query embedding
+        const queryEmbedding = await generateEmbedding(
+          query,
+          env.GEMINI_API_KEY,
+          'RETRIEVAL_QUERY' // ← Query, not document
+        );
+
+        // 2. Search Vectorize for similar chunks
+        const results = await env.VECTORIZE.query(queryEmbedding, {
+          topK,
+          returnMetadata: true
+        });
+
+        if (results.matches.length === 0) {
+          return new Response(JSON.stringify({
+            success: true,
+            answer: 'No relevant information found in the knowledge base.',
+            sources: []
+          }), {
+            headers: { 'Content-Type': 'application/json', ...corsHeaders }
+          });
+        }
+
+        console.log(`📚 Found ${results.matches.length} relevant chunks`);
+
+        // 3. Extract context from top matches
+        const context = results.matches
+          .map((match, i) => `[${i + 1}] ${match.metadata?.text || ''}`)
+          .join('\n\n');
+
+        // 4. Generate response with context
+        const answer = await generateResponse(context, query, env.GEMINI_API_KEY);
+
+        return new Response(JSON.stringify({
+          success: true,
+          query,
+          answer,
+          sources: results.matches.map(match => ({
+            documentId: match.metadata?.documentId,
+            chunkIndex: match.metadata?.chunkIndex,
+            similarity: match.score,
+            text: match.metadata?.text
+          }))
+        }, null, 2), {
+          headers: { 'Content-Type': 'application/json', ...corsHeaders }
+        });
+      }
+
+      // 404 for unknown routes
+      return new Response(JSON.stringify({
+        error: 'Not found',
+        routes: {
+          'POST /ingest': 'Upload documents',
+          'POST /query': 'Ask questions',
+          'GET /health': 'Health check'
+        }
+      }), {
+        status: 404,
+        headers: { 'Content-Type': 'application/json', ...corsHeaders }
+      });
+
+    } catch (error: any) {
+      console.error('❌ Error:', error.message);
+
+      return new Response(JSON.stringify({
+        success: false,
+        error: error.message
+      }), {
+        status: 500,
+        headers: { 'Content-Type': 'application/json', ...corsHeaders }
+      });
+    }
+  }
+};
+
+/**
+ * Example requests:
+ *
+ * 1. Ingest documents:
+ * curl -X POST https://your-worker.workers.dev/ingest \
+ *   -H "Content-Type: application/json" \
+ *   -d '{
+ *     "documents": [
+ *       {
+ *         "id": "doc1",
+ *         "text": "Paris is the capital of France. It is known for the Eiffel Tower..."
+ *       },
+ *       {
+ *         "id": "doc2",
+ *         "text": "Machine learning is a subset of artificial intelligence..."
+ *       }
+ *     ]
+ *   }'
+ *
+ * 2. Query:
+ * curl -X POST https://your-worker.workers.dev/query \
+ *   -H "Content-Type: application/json" \
+ *   -d '{
+ *     "query": "What is the capital of France?",
+ *     "topK": 5
+ *   }'
+ *
+ * 3. Health check:
+ * curl https://your-worker.workers.dev/health
+ */
--- a/templates/semantic-search.ts
+++ b/templates/semantic-search.ts
@@ -0,0 +1,289 @@
+/**
+ * Semantic Search with Gemini Embeddings
+ *
+ * Demonstrates semantic similarity search using cosine similarity.
+ * Finds documents based on meaning, not just keyword matching.
+ *
+ * Setup:
+ * 1. npm install @google/genai@^1.27.0
+ * 2. export GEMINI_API_KEY="your-api-key"
+ *
+ * Usage:
+ * npx tsx semantic-search.ts
+ */
+
+import { GoogleGenAI } from "@google/genai";
+
+interface Document {
+  id: string;
+  text: string;
+  embedding?: number[];
+}
+
+interface SearchResult {
+  document: Document;
+  similarity: number;
+}
+
+/**
+ * Calculate cosine similarity between two vectors
+ * Returns value between -1 and 1, where 1 = identical
+ */
+function cosineSimilarity(a: number[], b: number[]): number {
+  if (a.length !== b.length) {
+    throw new Error(`Vector dimensions must match: ${a.length} vs ${b.length}`);
+  }
+
+  let dotProduct = 0;
+  let magnitudeA = 0;
+  let magnitudeB = 0;
+
+  for (let i = 0; i < a.length; i++) {
+    dotProduct += a[i] * b[i];
+    magnitudeA += a[i] * a[i];
+    magnitudeB += b[i] * b[i];
+  }
+
+  if (magnitudeA === 0 || magnitudeB === 0) {
+    return 0;
+  }
+
+  return dotProduct / (Math.sqrt(magnitudeA) * Math.sqrt(magnitudeB));
+}
+
+/**
+ * Normalize vector to unit length
+ * Useful for faster similarity calculations
+ */
+function normalizeVector(vector: number[]): number[] {
+  const magnitude = Math.sqrt(vector.reduce((sum, v) => sum + v * v, 0));
+
+  if (magnitude === 0) {
+    return vector;
+  }
+
+  return vector.map(v => v / magnitude);
+}
+
+/**
+ * Calculate dot product (for normalized vectors only)
+ */
+function dotProduct(a: number[], b: number[]): number {
+  return a.reduce((sum, val, i) => sum + val * b[i], 0);
+}
+
+class SemanticSearch {
+  private ai: GoogleGenAI;
+  private documents: Document[] = [];
+  private normalized: boolean = false;
+
+  constructor(apiKey: string, normalized: boolean = false) {
+    this.ai = new GoogleGenAI({ apiKey });
+    this.normalized = normalized;
+  }
+
+  /**
+   * Index documents (generate and store embeddings)
+   */
+  async indexDocuments(documents: Array<{ id: string; text: string }>): Promise<void> {
+    console.log(`\n📚 Indexing ${documents.length} documents...\n`);
+
+    for (const doc of documents) {
+      const response = await this.ai.models.embedContent({
+        model: 'gemini-embedding-001',
+        content: doc.text,
+        config: {
+          taskType: 'RETRIEVAL_DOCUMENT', // ← Documents for indexing
+          outputDimensionality: 768
+        }
+      });
+
+      let embedding = response.embedding.values;
+
+      // Normalize if requested (faster similarity calculation)
+      if (this.normalized) {
+        embedding = normalizeVector(embedding);
+      }
+
+      this.documents.push({
+        id: doc.id,
+        text: doc.text,
+        embedding
+      });
+
+      console.log(`✅ Indexed: ${doc.id}`);
+    }
+
+    console.log(`\n✨ Indexing complete! ${this.documents.length} documents ready.\n`);
+  }
+
+  /**
+   * Search for similar documents
+   */
+  async search(query: string, topK: number = 5): Promise<SearchResult[]> {
+    if (this.documents.length === 0) {
+      throw new Error('No documents indexed. Call indexDocuments() first.');
+    }
+
+    console.log(`🔍 Searching for: "${query}"\n`);
+
+    // Generate query embedding
+    const response = await this.ai.models.embedContent({
+      model: 'gemini-embedding-001',
+      content: query,
+      config: {
+        taskType: 'RETRIEVAL_QUERY', // ← Query, not document
+        outputDimensionality: 768
+      }
+    });
+
+    let queryEmbedding = response.embedding.values;
+
+    if (this.normalized) {
+      queryEmbedding = normalizeVector(queryEmbedding);
+    }
+
+    // Calculate similarity for each document
+    const results: SearchResult[] = this.documents.map(doc => ({
+      document: doc,
+      similarity: this.normalized
+        ? dotProduct(queryEmbedding, doc.embedding!)
+        : cosineSimilarity(queryEmbedding, doc.embedding!)
+    }));
+
+    // Sort by similarity (descending) and return top K
+    return results
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, topK);
+  }
+
+  /**
+   * Find similar documents to a given document
+   */
+  findSimilar(documentId: string, topK: number = 5): SearchResult[] {
+    const doc = this.documents.find(d => d.id === documentId);
+
+    if (!doc || !doc.embedding) {
+      throw new Error(`Document not found: ${documentId}`);
+    }
+
+    const results: SearchResult[] = this.documents
+      .filter(d => d.id !== documentId) // Exclude the document itself
+      .map(d => ({
+        document: d,
+        similarity: this.normalized
+          ? dotProduct(doc.embedding!, d.embedding!)
+          : cosineSimilarity(doc.embedding!, d.embedding!)
+      }));
+
+    return results
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, topK);
+  }
+}
+
+// Example usage
+async function main() {
+  try {
+    const apiKey = process.env.GEMINI_API_KEY;
+    if (!apiKey) {
+      throw new Error('GEMINI_API_KEY environment variable not set');
+    }
+
+    // Initialize search engine
+    const search = new SemanticSearch(apiKey, false); // Set true for normalized vectors
+
+    // Sample documents
+    const documents = [
+      {
+        id: 'doc1',
+        text: 'Paris is the capital of France. It is known for the Eiffel Tower and the Louvre Museum.'
+      },
+      {
+        id: 'doc2',
+        text: 'Machine learning is a subset of artificial intelligence that enables computers to learn from data.'
+      },
+      {
+        id: 'doc3',
+        text: 'The Eiffel Tower is an iconic landmark in Paris, France, built in 1889.'
+      },
+      {
+        id: 'doc4',
+        text: 'Deep learning uses neural networks with multiple layers to learn complex patterns in data.'
+      },
+      {
+        id: 'doc5',
+        text: 'London is the capital of the United Kingdom and home to Big Ben and Buckingham Palace.'
+      },
+      {
+        id: 'doc6',
+        text: 'Natural language processing is a branch of AI that helps computers understand human language.'
+      }
+    ];
+
+    // Index documents
+    await search.indexDocuments(documents);
+
+    // Example 1: Search by query
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');
+    console.log('Example 1: Search by Query');
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n');
+
+    const query1 = "What is the capital of France?";
+    const results1 = await search.search(query1, 3);
+
+    results1.forEach((result, i) => {
+      console.log(`${i + 1}. [${(result.similarity * 100).toFixed(1)}%] ${result.document.id}`);
+      console.log(`   ${result.document.text}\n`);
+    });
+
+    // Example 2: Different query
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');
+    console.log('Example 2: AI-related Query');
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n');
+
+    const query2 = "Tell me about artificial intelligence";
+    const results2 = await search.search(query2, 3);
+
+    results2.forEach((result, i) => {
+      console.log(`${i + 1}. [${(result.similarity * 100).toFixed(1)}%] ${result.document.id}`);
+      console.log(`   ${result.document.text}\n`);
+    });
+
+    // Example 3: Find similar documents
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');
+    console.log('Example 3: Find Similar to doc1 (Paris)');
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n');
+
+    const similar = search.findSimilar('doc1', 3);
+
+    similar.forEach((result, i) => {
+      console.log(`${i + 1}. [${(result.similarity * 100).toFixed(1)}%] ${result.document.id}`);
+      console.log(`   ${result.document.text}\n`);
+    });
+
+    // Example 4: Demonstrate semantic vs keyword matching
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━');
+    console.log('Example 4: Semantic Understanding');
+    console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n');
+    console.log('Query: "neural networks" (no exact keyword match in any document)\n');
+
+    const query3 = "neural networks";
+    const results3 = await search.search(query3, 3);
+
+    results3.forEach((result, i) => {
+      const hasKeyword = result.document.text.toLowerCase().includes('neural');
+      console.log(`${i + 1}. [${(result.similarity * 100).toFixed(1)}%] ${result.document.id} ${hasKeyword ? '✓ keyword' : '✗ no keyword'}`);
+      console.log(`   ${result.document.text}\n`);
+    });
+
+    console.log('📊 Note: High similarity even without exact keyword match!');
+    console.log('This demonstrates semantic understanding.\n');
+
+  } catch (error: any) {
+    console.error('❌ Error:', error.message);
+    process.exit(1);
+  }
+}
+
+main();