How does this skill help with dimension mismatches?

It provides strict configuration guides for Workers AI (768) and OpenAI (1536/3072) to ensure your index dimensions perfectly match your embedding model output.

Can I use this for metadata filtering?

Yes, the skill includes templates for creating metadata indexes before ingestion, ensuring your vectors are instantly filterable by properties like category or timestamp.

Does it support OpenAI embeddings?

Yes, it includes specific integration guides and configuration templates for OpenAI's text-embedding-3-small and text-embedding-3-large models.

What is the benefit of the included RAG pattern?

It provides a complete, optimized workflow covering embedding generation, vector similarity search, context augmentation, and final LLM response generation.

Is it compatible with Wrangler?

Absolutely, it includes comprehensive Wrangler CLI references for managing vectorize indexes, metadata, and environment bindings.

Cloudflare Vectorize for RAG & AI

Name: Cloudflare Vectorize for RAG & AI
Author: secondsky

bysecondsky

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Database Management

Implements high-performance semantic search and RAG workflows using Cloudflare's globally distributed vector database.

About

This skill provides a production-ready implementation framework for Cloudflare Vectorize, allowing developers to build sophisticated semantic search and Retrieval-Augmented Generation (RAG) systems. It streamlines the complex process of managing vector indexes, handling dimension configurations for Workers AI or OpenAI, and implementing advanced metadata filtering. By providing standardized patterns for document chunking and similarity search, this skill reduces development time and prevents common errors like metadata indexing lag and dimension mismatches in serverless environments.

Key Features

Native Workers AI & OpenAI Embedding Integration
21 GitHub stars
Smart Document Chunking & Ingestion Pipelines
Production-Ready RAG Chatbot Templates
Advanced Metadata Filtering & Namespace Support
Automated Index & Metadata Management

Use Cases

Creating personalized recommendation systems based on vector similarity
Implementing Retrieval-Augmented Generation (RAG) for AI chatbots
Building semantic search engines for large documentation sets

About

Key Features

Native Workers AI & OpenAI Embedding Integration
21 GitHub stars
Smart Document Chunking & Ingestion Pipelines
Production-Ready RAG Chatbot Templates
Advanced Metadata Filtering & Namespace Support
Automated Index & Metadata Management

Use Cases

Creating personalized recommendation systems based on vector similarity
Implementing Retrieval-Augmented Generation (RAG) for AI chatbots
Building semantic search engines for large documentation sets