Vectorize API

This page covers the Vectorize API available within Cloudflare Workers, including usage examples.

let vectorsToInsert = [
  { id: "123", values: [32.4, 6.5, 11.2, 10.3, 87.9] },
  { id: "456", values: [2.5, 7.8, 9.1, 76.9, 8.5] },
];
let inserted = await env.YOUR_INDEX.insert(vectorsToInsert);

Inserts vectors into the index. Vectorize inserts are asynchronous and the insert operation returns a mutation identifier unique for that operation. It typically takes a few seconds for inserted vectors to be available for querying in a Vectorize index.

If vectors with the same vector ID already exist in the index, only the vectors with new IDs will be inserted.

If you need to update existing vectors, use the upsert operation.

let vectorsToUpsert = [
  { id: "123", values: [32.4, 6.5, 11.2, 10.3, 87.9] },
  { id: "456", values: [2.5, 7.8, 9.1, 76.9, 8.5] },
  { id: "768", values: [29.1, 5.7, 12.9, 15.4, 1.1] },
];
let upserted = await env.YOUR_INDEX.upsert(vectorsToUpsert);

Upserts vectors into an index. Vectorize upserts are asynchronous and the upsert operation returns a mutation identifier unique for that operation. It typically takes a few seconds for upserted vectors to be available for querying in a Vectorize index.

An upsert operation will insert vectors into the index if vectors with the same ID do not exist, and overwrite vectors with the same ID.

Upserting does not merge or combine the values or metadata of an existing vector with the upserted vector: the upserted vector replaces the existing vector in full.

let queryVector = [32.4, 6.55, 11.2, 10.3, 87.9];
let matches = await env.YOUR_INDEX.query(queryVector);

Query an index with the provided vector, returning the score(s) of the closest vectors based on the configured distance metric.

• Configure the number of returned matches by setting topK (default: 5)
• Return vector values by setting returnValues: true (default: false)
• Return vector metadata by setting returnMetadata: 'indexed' or returnMetadata: 'all' (default: ‘none’)

let matches = await env.YOUR_INDEX.query(queryVector, {
  topK: 5,
  returnValues: true,
  returnMetadata: "all",
});

The topK can be configured to specify the number of matches returned by the query operation. Vectorize now supports an upper limit of 100 for the topK value. However, for a query operation with returnValues set to true or returnMetadata set to all, topK would be limited to a maximum value of 20.

The returnMetadata field provides three ways to fetch vector metadata while querying:

1. none: Do not fetch metadata.
2. indexed: Fetched metadata only for the indexed metadata fields. There is no latency overhead with this option, but long text fields may be truncated.
3. all: Fetch all metadata associated with a vector. Queries may run slower with this option, and topK would be limited to 20.

let ids = ["11", "22", "33", "44"];
const vectors = await env.YOUR_INDEX.getByIds(ids);

Retrieves the specified vectors by their ID, including values and metadata.

let idsToDelete = ["11", "22", "33", "44"];
const deleted = await env.YOUR_INDEX.deleteByIds(idsToDelete);

Deletes the vector IDs provided from the current index. Vectorize deletes are asynchronous and the delete operation returns a mutation identifier unique for that operation. It typically takes a few seconds for vectors to be removed from the Vectorize index.

const details = await env.YOUR_INDEX.describe();

Retrieves the configuration of a given index directly, including its configured dimensions and distance metric.

Enable metadata filtering on the specified property. Limited to 10 properties.

Run the following wrangler vectorize command:

wrangler vectorize create-metadata-index <index-name> --property-name='some-prop' --type='string'

Allow Vectorize to delete the specified metadata index.

Run the following wrangler vectorize command:

wrangler vectorize delete-metadata-index <index-name> --property-name='some-prop'

List metadata properties on which metadata filtering is enabled.

Run the following wrangler vectorize command:

wrangler vectorize list-metadata-index <index-name>

Get additional details about the index.

Run the following wrangler vectorize command:

wrangler vectorize info <name>

A vector represents the vector embedding output from a machine learning model.

• id - a unique string identifying the vector in the index. This should map back to the ID of the document, object or database identifier that the vector values were generated from.
• namespace - an optional partition key within a index. Operations are performed per-namespace, so this can be used to create isolated segments within a larger index.
• values - an array of number, Float32Array, or Float64Array as the vector embedding itself. This must be a dense array, and the length of this array must match the dimensions configured on the index.
• metadata - an optional set of key-value pairs that can be used to store additional metadata alongside a vector.

let vectorExample = {
  id: "12345",
  values: [32.4, 6.55, 11.2, 10.3, 87.9],
  metadata: {
    key: "value",
    hello: "world",
    url: "r2://bucket/some/object.json",
  },
};

Bindings allow you to attach resources, including Vectorize indexes or R2 buckets, to your Worker.

Bindings are defined in either the wrangler.toml configuration associated with your Workers project, or via the Cloudflare dashboard for your project.

Vectorize indexes are bound by name. A binding for an index named production-doc-search would resemble the below:

[[vectorize]]
binding = "PROD_SEARCH" # the index will be available as env.PROD_SEARCH in your Worker
index_name = "production-doc-search"

Refer to the bindings documentation for more details.

New Workers projects created via npm create cloudflare@latest automatically include the relevant TypeScript types for Vectorize.

Older projects, or non-Workers projects looking to use Vectorize’s REST API ↗ in a TypeScript project, should ensure @cloudflare/workers-types version 4.20230922.0 or later is installed.