Vectorize

Cloudflare Vectorize is a vector search index for building semantic search, retrieval workflows, and recommendation features.

Config

edgebase.config.ts

import { defineConfig } from '@edge-base/shared';

export default defineConfig({
  vectorize: {
    embeddings: {
      binding: 'EMBEDDINGS_INDEX',
      dimensions: 1536,
      metric: 'cosine',
    },
  },
});

Field	Required	Description
binding	No	Optional Wrangler binding override. Defaults to an EdgeBase-managed binding name
dimensions	Yes	Vector dimensionality and must match your embedding model output
metric	Yes	Distance metric: cosine, euclidean, or dot-product

Common Use Cases

• Semantic search: embed documents and search by meaning instead of keywords.
• Retrieval workflows: retrieve relevant context for semantic search and ranked lookup flows.
• Recommendations: find similar items based on user behavior or item attributes.

Local Development

Cloudflare does not provide a local Vectorize simulation. Wrangler supports remote binding connections for Vectorize during local development, but EdgeBase currently falls back to stub responses when a Vectorize binding is unavailable in local or Docker environments.

Next Steps

• Use the Vectorize Admin SDK for backend code.
• Review Vectorize limits & pricing before choosing dimensions, filters, or query shape.
• Use the Native Resources API reference if you need raw HTTP access.
• Use context.admin.vector() inside App Functions.