cloudflare-vectorize

Manage Cloudflare Vectorize V2 workflows including async mutations and metadata indexes.

961|99|Updated Oct 20, 2025

Search Tags:#semantic search #embeddings #RAG #AI #cloudflare #vectorize

Ready to run

npx skills add https://github.com/jezweb/claude-skills --skill cloudflare-vectorize

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cloudflare-vectorize
Source: https://github.com/jezweb/claude-skills/tree/main/skills/cloudflare-vectorize
Command: npx skills add https://github.com/jezweb/claude-skills --skill cloudflare-vectorize

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires wrangler, @cloudflare/workers-types, and includes references (resource) components.

What problem does it solve?

This Skill eliminates the complexity of setting up vector databases for AI applications, providing ready-to-use templates that save development time and prevent common errors.

Core Features & Use Cases

Semantic Search: Find similar documents, products, or content using vector similarity.
RAG Chatbots: Create intelligent assistants that retrieve relevant context before answering questions.
Use Case: Imagine you need to build a customer support chatbot that searches your documentation. Use this Skill to implement the complete RAG pipeline with working code.

Quick Start

Create a vector index for documentation search with 768 dimensions using cosine similarity metric.

Frequently Asked Questions about cloudflare-vectorize

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build semantic search into my application?▼

Semantic search uses vector embeddings to find similar content by meaning rather than keywords. This Skill provides ready-to-use templates for Cloudflare Vectorize that handle embedding generation, indexing, and similarity queries, letting you implement search in minutes.

Can I use Cloudflare Vectorize for RAG chatbots?▼

Yes. This Skill includes complete RAG pipeline templates that retrieve relevant context from your vectorized data before generating responses, enabling you to build intelligent assistants like customer support bots backed by your documentation.

What are the dimension and metadata constraints in Cloudflare Vectorize?▼

Vectorize enforces fixed embedding dimensions per model, supports up to 10 metadata indexes per vector index, and requires pre-created metadata indexes before inserts. This Skill handles these constraints and diagnoses dimension mismatches and returnMetadata errors.

How do I migrate from Cloudflare Vectorize V1 to V2?▼

V2 introduces async mutations tracked with mutationId and stricter batch upsert semantics. This Skill documents the migration path, shows how to manage async operations, and explains namespace filtering and timing considerations.

What's the best way to manage batch vector upserts at scale?▼

Batch upsert operations in Vectorize use semantic versioning for consistency and support namespace filtering for multi-tenant setups. This Skill provides patterns for scalable vector management and explains batch semantics to prevent conflicts.