cloudflare-vectorize（cloudflare 矢量化）

Cloudflare Vectorize

Complete implementation guide for Cloudflare Vectorize - a globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications with Cloudflare Workers.

Status: Production Ready ✅ Last Updated: 2025-10-21 Dependencies: cloudflare-worker-base (for Worker setup), cloudflare-workers-ai (for embeddings) Latest Versions: wrangler@4.43.0, @cloudflare/workers-types@4.20251014.0 Token Savings: ~65% Errors Prevented: 8 Dev Time Saved: ~3 hours

What This Skill Provides

Core Capabilities

• ✅ Index Management: Create, configure, and manage vector indexes
• ✅ Vector Operations: Insert, upsert, query, delete, and list vectors
• ✅ Metadata Filtering: Advanced filtering with 10 metadata indexes per index
• ✅ Semantic Search: Find similar vectors using cosine, euclidean, or dot-product metrics
• ✅ RAG Patterns: Complete retrieval-augmented generation workflows
• ✅ Workers AI Integration: Native embedding generation with @cf/baai/bge-base-en-v1.5
• ✅ OpenAI Integration: Support for text-embedding-3-small/large models
• ✅ Document Processing: Text chunking and batch ingestion pipelines

Templates Included

1. basic-search.ts - Simple vector search with Workers AI
2. rag-chat.ts - Full RAG chatbot with context retrieval
3. document-ingestion.ts - Document chunking and embedding pipeline
4. metadata-filtering.ts - Advanced filtering patterns

⚠️ Vectorize V2 Breaking Changes (September 2024)

IMPORTANT: Vectorize V2 became GA in September 2024 with significant breaking changes.

What Changed in V2

Performance Improvements:

• Index capacity: 200,000 → 5 million vectors per index
• Query latency: 549ms → 31ms median (18× faster)
• TopK limit: 20 → 100 results per query
• Scale limits: 100 → 50,000 indexes per account
• Namespace limits: 100 → 50,000 namespaces per index

Breaking API Changes:

1. Async Mutations - All mutations now asynchronous: // V2: Returns mutationId const result = await env.VECTORIZE_INDEX.insert(vectors); console.log(result.mutationId); // "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" // Vector inserts/deletes may take a few seconds to be reflected
2. returnMetadata Parameter - Boolean → String enum: // ❌ V1 (deprecated) {returnMetadata: true} // ✅ V2 (required) {returnMetadata: 'all' | 'indexed' | 'none'}
3. Metadata Indexes Required Before Insert:

- V2 requires metadata indexes created BEFORE vectors inserted - Vectors added before metadata index won't be indexed - Must re-upsert vectors after creating metadata index

V1 Deprecation Timeline:

• December 2024: Can no longer create V1 indexes
• Existing V1 indexes: Continue to work (other operations unaffected)
• Migration: Use wrangler vectorize --deprecated-v1 flag for V1 operations

Wrangler Version Required:

• Minimum: wrangler@3.71.0 for V2 commands
• Recommended: wrangler@4.43.0+ (latest)

Check Mutation Status

// Get index info to check last mutation processed
const info = await env.VECTORIZE_INDEX.describe();
console.log(info.mutationId); // Last mutation ID
console.log(info.processedUpToMutation); // Last processed timestamp

Critical Setup Rules

⚠️ MUST DO BEFORE INSERTING VECTORS

# 1. Create the index with FIXED dimensions and metric
npx wrangler vectorize create my-index \
  --dimensions=768 \
  --metric=cosine

# 2. Create metadata indexes IMMEDIATELY (before inserting vectors!)
npx wrangler vectorize create-metadata-index my-index \
  --property-name=category \
  --type=string

npx wrangler vectorize create-metadata-index my-index \
  --property-name=timestamp \
  --type=number

Why: Metadata indexes MUST exist before vectors are inserted. Vectors added before a metadata index was created won't be filterable on that property.

Index Configuration (Cannot Be Changed Later)

# Dimensions MUST match your embedding model output:
# - Workers AI @cf/baai/bge-base-en-v1.5: 768 dimensions
# - OpenAI text-embedding-3-small: 1536 dimensions
# - OpenAI text-embedding-3-large: 3072 dimensions

# Metrics determine similarity calculation:
# - cosine: Best for normalized embeddings (most common)
# - euclidean: Absolute distance between vectors
# - dot-product: For non-normalized vectors

Wrangler Configuration

wrangler.jsonc:

{
  "name": "my-vectorize-worker",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-21",
  "vectorize": [
    {
      "binding": "VECTORIZE_INDEX",
      "index_name": "my-index"
    }
  ],
  "ai": {
    "binding": "AI"
  }
}

TypeScript Types

export interface Env {
  VECTORIZE_INDEX: VectorizeIndex;
  AI: Ai;
}

interface VectorizeVector {
  id: string;
  values: number[] | Float32Array | Float64Array;
  namespace?: string;
  metadata?: Record<string, string | number | boolean | string[]>;
}

interface VectorizeMatches {
  matches: Array<{
    id: string;
    score: number;
    values?: number[];
    metadata?: Record<string, any>;
    namespace?: string;
  }>;
  count: number;
}

Metadata Filter Operators (V2)

Vectorize V2 supports advanced metadata filtering with range queries:

// Equality (implicit $eq)
{ category: "docs" }

// Not equals
{ status: { $ne: "archived" } }

// In/Not in arrays
{ category: { $in: ["docs", "tutorials"] } }
{ category: { $nin: ["deprecated", "draft"] } }

// Range queries (numbers) - NEW in V2
{ timestamp: { $gte: 1704067200, $lt: 1735689600 } }

// Range queries (strings) - prefix searching
{ url: { $gte: "/docs/workers", $lt: "/docs/workersz" } }

// Nested metadata with dot notation
{ "author.id": "user123" }

// Multiple conditions (implicit AND)
{ category: "docs", language: "en", "metadata.published": true }

Metadata Best Practices

1. Cardinality Considerations

Low Cardinality (Good for $eq filters):

// Few unique values - efficient filtering
metadata: {
  category: "docs",        // ~10 categories
  language: "en",          // ~5 languages
  published: true          // 2 values (boolean)
}

High Cardinality (Avoid in range queries):

// Many unique values - avoid large range scans
metadata: {
  user_id: "uuid-v4...",         // Millions of unique values
  timestamp_ms: 1704067200123    // Use seconds instead
}

2. Metadata Limits

• Max 10 metadata indexes per Vectorize index
• Max 10 KiB metadata per vector
• String indexes: First 64 bytes (UTF-8)
• Number indexes: Float64 precision
• Filter size: Max 2048 bytes (compact JSON)

3. Key Restrictions

// ❌ INVALID metadata keys
metadata: {
  "": "value",              // Empty key
  "user.name": "John",      // Contains dot (reserved for nesting)
  "$admin": true,           // Starts with $
  "key\"with\"quotes": 1    // Contains quotes
}

// ✅ VALID metadata keys
metadata: {
  "user_name": "John",
  "isAdmin": true,
  "nested": { "allowed": true }  // Access as "nested.allowed" in filters
}

Common Errors & Solutions

Error 1: Metadata Index Created After Vectors Inserted

Problem: Filtering doesn't work on existing vectors
Solution: Delete and re-insert vectors OR create metadata indexes BEFORE inserting

Error 2: Dimension Mismatch

Problem: "Vector dimensions do not match index configuration"
Solution: Ensure embedding model output matches index dimensions:
  - Workers AI bge-base: 768
  - OpenAI small: 1536
  - OpenAI large: 3072

Error 3: Invalid Metadata Keys

Problem: "Invalid metadata key"
Solution: Keys cannot:
  - Be empty
  - Contain . (dot)
  - Contain " (quote)
  - Start with $ (dollar sign)

Error 4: Filter Too Large

Problem: "Filter exceeds 2048 bytes"
Solution: Simplify filter or split into multiple queries

Error 5: Range Query on High Cardinality

Problem: Slow queries or reduced accuracy
Solution: Use lower cardinality fields for range queries, or use seconds instead of milliseconds for timestamps

Error 6: Insert vs Upsert Confusion

Problem: Updates not reflecting in index
Solution: Use upsert() to overwrite existing vectors, not insert()

Error 7: Missing Bindings

Problem: "VECTORIZE_INDEX is not defined"
Solution: Add [[vectorize]] binding to wrangler.jsonc

Error 8: Namespace vs Metadata Confusion

Problem: Unclear when to use namespace vs metadata filtering
Solution:
  - Namespace: Partition key, applied BEFORE metadata filters
  - Metadata: Flexible key-value filtering within namespace

Error 9: V2 Async Mutation Timing (NEW in V2)

Problem: Inserted vectors not immediately queryable
Solution: V2 mutations are asynchronous - vectors may take a few seconds to be reflected
  - Use mutationId to track mutation status
  - Check env.VECTORIZE_INDEX.describe() for processedUpToMutation timestamp

Error 10: V1 returnMetadata Boolean (BREAKING in V2)

Problem: "returnMetadata must be 'all', 'indexed', or 'none'"
Solution: V2 changed returnMetadata from boolean to string enum:
  - ❌ V1: { returnMetadata: true }
  - ✅ V2: { returnMetadata: 'all' }

V2 Migration Checklist

If migrating from V1 to V2:

1. ✅ Update wrangler to 3.71.0+ (npm install -g wrangler@latest)
2. ✅ Create new V2 index (can't upgrade V1 → V2)
3. ✅ Create metadata indexes BEFORE inserting vectors
4. ✅ Update returnMetadata boolean → string enum ('all', 'indexed', 'none')
5. ✅ Handle async mutations (expect mutationId in responses)
6. ✅ Test with V2 limits (topK up to 100, 5M vectors per index)
7. ✅ Update error handling for async behavior

V1 Deprecation:

• After December 2024: Cannot create new V1 indexes
• Existing V1 indexes: Continue to work
• Use wrangler vectorize --deprecated-v1 for V1 operations

Official Documentation

• Vectorize V2 Docs: https://developers.cloudflare.com/vectorize/
• V2 Changelog: https://developers.cloudflare.com/vectorize/platform/changelog/
• V1 to V2 Migration: https://developers.cloudflare.com/vectorize/reference/transition-vectorize-legacy/
• Metadata Filtering: https://developers.cloudflare.com/vectorize/reference/metadata-filtering/
• Workers AI Models: https://developers.cloudflare.com/workers-ai/models/

Status: Production Ready ✅ (Vectorize V2 GA - September 2024) Last Updated: 2025-11-22 Token Savings: ~70% Errors Prevented: 10 (includes V2 breaking changes)