@kuralle-agents/vectorize-store

Cloudflare Vectorize adapter for Kuralle RAG pipelines.

Install

npm install @kuralle-agents/vectorize-store

Peer: @kuralle-agents/rag.

What it does

CloudflareVectorizeStore implements VectorStoreCore from @kuralle-agents/rag, backed by Cloudflare Vectorize — designed for use inside Cloudflare Workers.

Key exports:

• CloudflareVectorizeStore — VectorStoreCore implementation for Cloudflare Vectorize.

Usage

Pair Vectorize with Workers AI embeddings — the embedding model runs inside Cloudflare's network (env.AI binding), so every query embedding skips the public-internet round trip a cloud embedding API costs, and no provider API key is needed. AiSdkEmbedder accepts the model from workers-ai-provider directly:

import { CloudflareVectorizeStore } from '@kuralle-agents/vectorize-store';
import { AiSdkEmbedder, VectorRetriever } from '@kuralle-agents/rag';
import { createWorkersAI } from 'workers-ai-provider';

interface Env {
  VECTORIZE: VectorizeIndex;
  AI: Ai;
}

export default {
  async fetch(request: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const vectorStore = new CloudflareVectorizeStore({ index: env.VECTORIZE });
    const embedder = new AiSdkEmbedder({
      model: workersai.textEmbeddingModel('@cf/baai/bge-m3'),
    });
    const retriever = new VectorRetriever({ store: vectorStore, embedder, indexName: 'docs', topK: 5 });
    // ...
  },
};

# wrangler.toml
[ai]
binding = "AI"

[[vectorize]]
binding = "VECTORIZE"
index_name = "docs"

The Vectorize index dimension must match the embedding model (@cf/baai/bge-m3 → 1024):

npx wrangler vectorize create docs --dimensions=1024 --metric=cosine

Any other AI SDK embedding provider (OpenAI, Google, Cohere) plugs into AiSdkEmbedder the same way when you need a specific model — at the cost of a cross-internet API call per embedding and a provider key. Whichever model you choose, configure RagPipeline's manifest (see @kuralle-agents/rag) so the index is locked to the model that built it — mixing embedding models in one index silently corrupts relevance.

Use createVectorRetrievalTool from @kuralle-agents/tools to attach this retriever as an agent tool.

Related

• @kuralle-agents/rag — VectorStoreCore interface, retrievers, embedders.
• @kuralle-agents/tools — createVectorRetrievalTool.
• @kuralle-agents/cf-agent — Cloudflare Durable Objects agent integration.