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Cloudflare Vectorize is a globally distributed vector database for building AI-powered applications. Use the API to create indexes, insert vectors, and perform similarity searches.

​
Overview

Access the Vectorize API: 
import Cloudflare from 'cloudflare';

const client = new Cloudflare({
  apiToken: process.env.CLOUDFLARE_API_TOKEN,
});

// Access Vectorize resources
const vectorize = client.vectorize;

​
Indexes

Manage vector indexes for similarity search.

​
Create an index

Create a new vector index. 
const index = await client.vectorize.indexes.create({
  account_id: '023e105f4ecef8ad9ca31a8372d0c353',
  name: 'my-index',
  config: {
    dimensions: 768,
    metric: 'cosine',
  },
});
​
account_id
string
required
Your Cloudflare account ID
​
name
string
required
Name for the index
​
config
object
required
Index configuration object
​
config.dimensions
number
required
Vector dimensions (e.g., 768 for many embedding models)
​
config.metric
string
required
Distance metric: ‘cosine’, ‘euclidean’, or ‘dot-product’
​
description
string
Optional description for the index
​
name
string
The index name
​
config
object
The index configuration
​
created_on
string
ISO 8601 timestamp when the index was created

​
List indexes

Retrieve all vector indexes in your account. 
for await (const index of client.vectorize.indexes.list({
  account_id: '023e105f4ecef8ad9ca31a8372d0c353',
})) {
  console.log(index);
}
​
account_id
string
required
Your Cloudflare account ID

​
Get an index

Retrieve details about a specific index. 
const index = await client.vectorize.indexes.get(
  'my-index',
  { account_id: '023e105f4ecef8ad9ca31a8372d0c353' }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID

​
Delete an index

Delete a vector index and all its data. 
await client.vectorize.indexes.delete(
  'my-index',
  { account_id: '023e105f4ecef8ad9ca31a8372d0c353' }
);
​
index_name
string
required
The index name to delete
​
account_id
string
required
Your Cloudflare account ID

​
Vectors

Manage vectors within an index.

​
Insert vectors

Insert vectors into an index. 
const response = await client.vectorize.indexes.insert(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    vectors: [
      {
        id: 'doc-1',
        values: [0.1, 0.2, 0.3, ...], // 768 dimensions
        metadata: { title: 'Document 1' },
      },
      {
        id: 'doc-2',
        values: [0.4, 0.5, 0.6, ...],
        metadata: { title: 'Document 2' },
      },
    ],
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
vectors
array
required
Array of vector objects to insert (up to 1000 vectors per request)
​
vectors.id
string
required
Unique identifier for the vector
​
vectors.values
array
required
Vector values (must match index dimensions)
​
vectors.metadata
object
Optional metadata to store with the vector (up to 10KB)
​
count
number
Number of vectors successfully inserted

​
Upsert vectors

Insert or update vectors. 
const response = await client.vectorize.indexes.upsert(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    vectors: [
      {
        id: 'doc-1',
        values: [0.1, 0.2, 0.3, ...],
        metadata: { title: 'Updated Document 1' },
      },
    ],
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
vectors
array
required
Array of vector objects to upsert

​
Query vectors

Perform a similarity search. 
const results = await client.vectorize.indexes.query(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    vector: [0.1, 0.2, 0.3, ...], // 768 dimensions
    top_k: 5,
    return_values: true,
    return_metadata: true,
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
vector
array
required
Query vector (must match index dimensions)
​
top_k
number
Number of results to return (default: 5, max: 100)
​
return_values
boolean
Whether to return vector values (default: false)
​
return_metadata
boolean
Whether to return metadata (default: true)
​
filter
object
Metadata filters for the search
​
matches
array
Array of matching vectors
​
matches.id
string
The vector ID
​
matches.score
number
Similarity score (interpretation depends on metric)
​
matches.metadata
object
The vector metadata (if return_metadata=true)
​
matches.values
array
The vector values (if return_values=true)

​
Get vectors by ID

Retrieve specific vectors by their IDs. 
const vectors = await client.vectorize.indexes.getByIds(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    ids: ['doc-1', 'doc-2'],
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
ids
array
required
Array of vector IDs to retrieve (up to 100 IDs)

​
Delete vectors by ID

Delete specific vectors from an index. 
await client.vectorize.indexes.deleteByIds(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    ids: ['doc-1', 'doc-2'],
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
ids
array
required
Array of vector IDs to delete (up to 1000 IDs)

​
List vectors

List all vectors in an index. 
const vectors = await client.vectorize.indexes.listVectors(
  'my-index',
  {
    account_id: '023e105f4ecef8ad9ca31a8372d0c353',
    limit: 100,
  }
);
​
index_name
string
required
The index name
​
account_id
string
required
Your Cloudflare account ID
​
limit
number
Maximum number of vectors to return (default: 100, max: 1000)
​
cursor
string
Pagination cursor for fetching the next page

​
Index information

Get statistics about an index. 
const info = await client.vectorize.indexes.info(
  'my-index',
  { account_id: '023e105f4ecef8ad9ca31a8372d0c353' }
);
​
vector_count
number
Total number of vectors in the index
​
dimensions
number
Vector dimensions
​
metric
string
Distance metric used

​
Using Vectorize in Workers

Bind a Vectorize index to your Worker: 
const version = await client.workers.beta.workers.versions.create(
  workerId,
  {
    account_id: accountId,
    main_module: 'worker.mjs',
    compatibility_date: '2024-03-01',
    bindings: [
      {
        type: 'vectorize',
        name: 'VECTORIZE',
        index_name: 'my-index',
      },
    ],
    modules: [...],
  }
);
Then query from your Worker: 
export default {
  async fetch(request, env) {
    // Insert vectors
    await env.VECTORIZE.insert([
      {
        id: 'doc-1',
        values: embeddings,
        metadata: { title: 'Document 1' },
      },
    ]);
    
    // Query for similar vectors
    const results = await env.VECTORIZE.query(
      queryEmbedding,
      { topK: 5 }
    );
    
    return Response.json(results);
  },
};

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