cloudflare-vectorize

Name: cloudflare-vectorize
Availability: InStock
Author: Jack

Store and query vector embeddings with Cloudflare Vectorize.

16|7|Updated Nov 20, 2025

Search Tags:#semantic search #RAG #metadata #vectorize #embedding #vector index

Ready to run

npx skills add https://github.com/jackspace/ClaudeSkillz --skill cloudflare-vectorize-jackspace

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Cloudflare Vectorize provides a ready-to-use guide for building semantic search, RAG, and AI-powered apps using vector indexes, embeddings, and metadata filters.

Core Features & Use Cases

Index management, vector insert/upsert/query/delete/list
Metadata filtering, topK search, namespaces
RAG patterns, Embeddings integration (Workers AI, OpenAI)
Document processing and templates

Quick Start

Create index with fixed dimensions and cosine metric
Create 10 metadata indexes and set up namespace filtering
Use embedding services to query and build context

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 with vector embeddings?▼

Semantic search uses vector embeddings to find contextually similar content by converting text into numerical representations and comparing their proximity. Cloudflare Vectorize stores these embeddings in a vector index, enabling fast similarity queries across documents without keyword matching.

Can I use Cloudflare Vectorize for retrieval augmented generation (RAG)?▼

Yes. Vectorize supports RAG workflows by storing document embeddings, retrieving top matching vectors for a query, and passing that context to a language model. Integration with Workers AI and OpenAI embeddings enables end-to-end RAG pipelines in Cloudflare Workers.

How do I filter vector search results by metadata?▼

Vectorize applies metadata filters during vector queries using dot notation and range queries, allowing you to narrow results by structured fields before topK retrieval. Namespace filtering further isolates results to specific data partitions.

What vector index operations does Cloudflare Vectorize support?▼

Vectorize supports insert, upsert, query, delete, and list operations on vector indexes. It enforces fixed dimensions and chosen distance metrics (like cosine) to ensure consistent index behavior and predictable search performance.

Can I use Vectorize with multiple data sources in Workers?▼

Yes. Vectorize integrates with multiple embedding sources—Workers AI and OpenAI—and supports document processing across multiple data sources, enabling unified semantic search across heterogeneous content within Cloudflare Workers.

What are the prerequisites for building a vector index with Vectorize?▼

You need to define a fixed embedding dimension, select a distance metric (cosine is common), prepare vector representations of your content, and structure metadata for filtering. Embeddings can be generated via Workers AI or OpenAI APIs.