vectorize

Cloudflare Vectorize

Store and query high-dimensional vector embeddings at the edge for RAG (Retrieval Augmented Generation), semantic search, and similarity matching.

FIRST: Create Index

# Create with preset (auto-configures dimensions and metric)
wrangler vectorize create my-index --preset @cf/baai/bge-base-en-v1.5

# Or create with explicit dimensions
wrangler vectorize create my-index --dimensions 768 --metric cosine

# List indexes
wrangler vectorize list

Add to wrangler.jsonc:

{
  "vectorize": [
    { "binding": "SEARCH_INDEX", "index_name": "my-index" }
  ]
}

When to Use

Use Case	Description
RAG Pipelines	Store document embeddings for context retrieval with LLMs
Semantic Search	Find similar content by meaning, not keywords
Recommendation Systems	Match users/items based on embedding similarity
Duplicate Detection	Find near-duplicate content using vector distance
Content Classification	Group similar items by vector clustering

Quick Reference

Operation	API
Insert vectors	await env.INDEX.insert([{ id, values, metadata }])
Query similar vectors	await env.INDEX.query(vector, { topK: 5 })
Upsert (insert or update)	await env.INDEX.upsert([{ id, values, metadata }])
Delete by IDs	await env.INDEX.deleteByIds(["id1", "id2"])
Get by IDs	await env.INDEX.getByIds(["id1", "id2"])

Basic RAG Example

interface Env {
  SEARCH_INDEX: Vectorize;
  AI: Ai;
}

export default {
  async fetch(req: Request, env: Env): Promise<Response> {
    const url = new URL(req.url);

    // Index documents with embeddings
    if (url.pathname === "/index" && req.method === "POST") {
      const { text, id } = await req.json<{ text: string; id: string }>();
      
      // Generate embedding using Workers AI
      const { data } = await env.AI.run("@cf/baai/bge-base-en-v1.5", {
        text: [text],
      });
      
      const embedding = data[0];
      
      // Insert into Vectorize
      await env.SEARCH_INDEX.insert([{
        id,
        values: embedding,
        metadata: { text }
      }]);
      
      return Response.json({ success: true, id });
    }

    // Search similar documents
    if (url.pathname === "/search" && req.method === "POST") {
      const { query } = await req.json<{ query: string }>();
      
      // Generate query embedding
      const { data } = await env.AI.run("@cf/baai/bge-base-en-v1.5", {
        text: [query],
      });
      
      const queryEmbedding = data[0];
      
      // Find similar vectors
      const results = await env.SEARCH_INDEX.query(queryEmbedding, {
        topK: 5,
        returnMetadata: true
      });
      
      return Response.json({
        matches: results.matches.map(match => ({
          id: match.id,
          score: match.score,
          text: match.metadata?.text
        }))
      });
    }

    return new Response("Not found", { status: 404 });
  }
};