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Vectorize
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Vectorize
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  1. Products
  2. Vectorize
  3. Get started
  4. Introduction to Vectorize

Get started

Vectorize is Cloudflare’s vector database. Vector databases allow you to use machine learning (ML) models to perform semantic search, recommendation, classification and anomaly detection tasks, as well as provide context to LLMs (Large Language Models).

This guide will instruct you through:

  • Creating your first Vectorize index.
  • Connecting a Cloudflare Worker to your index.
  • Inserting and performing a similarity search by querying your index.

​​ Prerequisites

To continue:

  1. Sign up for a Cloudflare account if you have not already.
  2. Install npm.
  3. Install Node.js. Use a Node version manager like Volta or nvm to avoid permission issues and change Node.js versions. Wrangler requires a Node version of 16.17.0 or later.

​​ 1. Create a Worker

You will create a new project that will contain a Worker, which will act as the client application for your Vectorize index.

Create a new project named vectorize-tutorial by running:

$ npm create cloudflare@latest

When setting up your vectorize-tutorial Worker, answering the questions as below:

  • Enter vectorize-tutorial as the directory for where you want to create your application.
  • Choose "Hello World Worker" for the type of application.
  • Select yes to using TypeScript.
  • Select yes to using Git.
  • Select no to deploying.

This will create a new vectorize-tutorial directory. Your new vectorize-tutorial directory will include:

  • A "Hello World" Worker at src/index.ts.
  • A wrangler.toml configuration file. wrangler.toml is how your vectorize-tutorial Worker will access your index.

​​ 2. Create an index

A vector database is distinct from a traditional SQL or NoSQL database. A vector database is designed to store vector embeddings, which are representations of data, but not the original data itself.

To create your first Vectorize index, change into the directory you just created for your Workers project:

$ cd vectorize-tutorial

To create an index, you will need to use the wrangler vectorize create command and provide a name for the index. A good index name is:

  • A combination of lowercase and/or numeric ASCII characters, shorter than 32 characters, starts with a letter, and uses dashes (-) instead of spaces.
  • Descriptive of the use-case and environment. For example, “production-doc-search” or “dev-recommendation-engine”.
  • Only used for describing the index, and is not directly referenced in code.

In addition, you will need to define both the dimensions of the vectors you will store in the index, as well as the distance metric used to determine similar vectors when creating the index. A metric can be Euclidean, cosine, or dot product. This configuration cannot be changed later, as a vector database is configured for a fixed vector configuration.

Run the following wrangler vectorize command:

$ npx wrangler vectorize create tutorial-index --dimensions=3 --metric=cosine


✅ Successfully created index 'tutorial-index'


[[vectorize]]
binding = "VECTORIZE_INDEX" # available in your Worker on env.VECTORIZE_INDEX
index_name = "tutorial-index"

The command above will create a new vector database, and output the binding configuration needed in the next step.

​​ 3. Bind your Worker to your index

You must create a binding for your Worker to connect to your Vectorize index. Bindings allow your Workers to access resources, like Vectorize or R2, from Cloudflare Workers. You create bindings by updating your wrangler.toml file.

To bind your index to your Worker, add the following to the end of your wrangler.toml file:

wrangler.toml
[[vectorize]]

binding = "VECTORIZE_INDEX" # available in your Worker on env.VECTORIZE_INDEX

index_name = "tutorial-index"

Specifically:

  • The value (string) you set for <BINDING_NAME> will be used to reference this database in your Worker. In this tutorial, name your binding VECTORIZE_INDEX.
  • The binding must be a valid JavaScript variable name. For example, binding = "MY_INDEX" or binding = "PROD_SEARCH_INDEX" would both be valid names for the binding.
  • Your binding is available in your Worker at env.<BINDING_NAME> and the Vectorize client API is exposed on this binding for use within your Workers application.

​​ 4. Insert vectors

Before you can query a vector database, you need to insert vectors for it to query against. These vectors would be generated from data (such as text or images) you pass to a machine learning model. However, this tutorial will define static vectors to illustrate how vector search works on its own.

First, go to your vectorize-tutorial Worker and open the src/index.ts file. The index.ts file is where you configure your Worker’s interactions with your Vectorize index.

Clear the content of index.ts. Paste the following code snippet into your index.ts file. On the env parameter, replace <BINDING_NAME> with VECTORIZE_INDEX:

src/index.ts
export interface Env {
	// This makes your vector index methods available on env.VECTORIZE_INDEX.*
	// For example, env.VECTORIZE_INDEX.insert() or query()
	VECTORIZE_INDEX: VectorizeIndex;

}



// Sample vectors: 3 dimensions wide.

//

// Vectors from a machine-learning model are typically ~100 to 1536 dimensions

// wide (or wider still).

const sampleVectors: Array<VectorizeVector> = [
	{ id: '1', values: [32.4, 74.1, 3.2], metadata: { url: '/products/sku/13913913' } },
	{ id: '2', values: [15.1, 19.2, 15.8], metadata: { url: '/products/sku/10148191' } },
	{ id: '3', values: [0.16, 1.2, 3.8], metadata: { url: '/products/sku/97913813' } },
	{ id: '4', values: [75.1, 67.1, 29.9], metadata: { url: '/products/sku/418313' } },
	{ id: '5', values: [58.8, 6.7, 3.4], metadata: { url: '/products/sku/55519183' } },

];



export default {
	async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
		let path = new URL(request.url).pathname;
		if (path.startsWith("/favicon")) {
			return new Response('', { status: 404 });
		}


		// You only need to insert vectors into your index once
		if (path.startsWith("/insert")) {
			// Insert some sample vectors into your index
			// In a real application, these vectors would be the output of a machine learning (ML) model,
			// such as Workers AI, OpenAI, or Cohere.
			const inserted = await env.VECTORIZE_INDEX.insert(sampleVectors);


			// Return the number of IDs you successfully inserted
			return Response.json(inserted);
		}


		return Response.json({text: "nothing to do... yet"}, { status: 404 })
	}

};

In the code above, you:

  • Define a binding to your Vectorize index from your Workers code. This binding matches the binding value you set in wrangler.toml under [[vectorize]].
  • Specify a set of example vectors that you will query against in the next step.
  • Insert those vectors into the index and confirm it was successful.

In the next step, you will expand the Worker to query the index and the vectors you insert.

​​ 5. Query vectors

In this step, you will take a vector representing an incoming query and use it to search your index.

First, go to your vectorize-tutorial Worker and open the src/index.ts file. The index.ts file is where you configure your Worker’s interactions with your Vectorize index.

Clear the content of index.ts. Paste the following code snippet into your index.ts file. On the env parameter, replace <BINDING_NAME> with VECTORIZE_INDEX:

src/index.ts
export interface Env {
	// This makes our vector index methods available on env.VECTORIZE_INDEX.*
	// For example, env.VECTORIZE_INDEX.insert() or query()
	VECTORIZE_INDEX: VectorizeIndex;

}



// Sample vectors: 3 dimensions wide.

//

// Vectors from a machine-learning model are typically ~100 to 1536 dimensions

// wide (or wider still).

const sampleVectors: Array<VectorizeVector> = [
	{ id: '1', values: [32.4, 74.1, 3.2], metadata: { url: '/products/sku/13913913' } },
	{ id: '2', values: [15.1, 19.2, 15.8], metadata: { url: '/products/sku/10148191' } },
	{ id: '3', values: [0.16, 1.2, 3.8], metadata: { url: '/products/sku/97913813' } },
	{ id: '4', values: [75.1, 67.1, 29.9], metadata: { url: '/products/sku/418313' } },
	{ id: '5', values: [58.8, 6.7, 3.4], metadata: { url: '/products/sku/55519183' } },

];



export default {
	async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
		let path = new URL(request.url).pathname;
		if (path.startsWith("/favicon")) {
			return new Response('', { status: 404 });
		}


		// You only need to insert vectors into your index once
		if (path.startsWith("/insert")) {
			// Insert some sample vectors into your index
			// In a real application, these vectors would be the output of a machine learning (ML) model,
			// such as Workers AI, OpenAI, or Cohere.
			let inserted = await env.VECTORIZE_INDEX.insert(sampleVectors);


			// Return the number of IDs we successfully inserted
			return Response.json(inserted);
		}


		// return Response.json({text: "nothing to do... yet"}, { status: 404 })


		// In a real application, you would take a user query. For example, "what is a
		// vector database" - and transform it into a vector embedding first.
		//
		// In this example, you will construct a vector that should
		// match vector id #5
		const queryVector: Array<number> = [54.8, 5.5, 3.1];


		// Query your index and return the three (topK = 3) most similar vector
		// IDs with their similarity score.
		//
		// By default, vector values are not returned, as in many cases the
		// vector id and scores are sufficient to map the vector back to the
		// original content it represents.
		const matches = await env.VECTORIZE_INDEX.query(queryVector, { topK: 3, returnValues: true, returnMetadata: true });


		return Response.json({
			// This will return the closest vectors: you will notice that the vector
			// with id = 5 has the highest score (closest to 1.0) as the
			// distance between it and our query vector is the smallest.
			// You return the full set of matches so you can check the possible scores.
			matches: matches,
		});
	},

};

​​ 6. Deploy your Worker

Before deploying your Worker globally, log in with your Cloudflare account by running:

$ npx wrangler login

You will be directed to a web page asking you to log in to the Cloudflare dashboard. After you have logged in, you will be asked if Wrangler can make changes to your Cloudflare account. Scroll down and select Allow to continue.

From here, you can deploy your Worker to make your project accessible on the Internet. To deploy your Worker, run:

$ npx wrangler deploy

Once deployed, preview your Worker at https://vectorize-tutorial.<YOUR_SUBDOMAIN>.workers.dev.

​​ 7. Query your index

To insert vectors and then query them, use the URL for your deployed Worker, such as https://vectorize-tutorial.<YOUR_SUBDOMAIN>.workers.dev/. Open your browser and:

  1. Insert your vectors first by visiting /insert. This should return the below JSON:
// https://vectorize-tutorial.<YOUR_SUBDOMAIN>.workers.dev/insert

{ "count": 5, "ids": [1, 2, 3, 4, 5] }

Subsequent visits will return count:0 as you cannot insert() the same vector IDs.

  1. Query your index - expect your query vector of [54.8, 5.5, 3.1] to be closest to vector ID 5 by visiting the root path of / . This query will return the three (topK: 3) closest matches, as well as their vector values and metadata.

You will notice that id: 5 has a score of 0.999909486. Because you are using cosine as our distance metric, the closer the score to 1.0, the closer your vectors are.

// https://vectorize-tutorial.<YOUR_SUBDOMAIN>.workers.dev/

{
  "matches": {
    "count": 3,
    "matches": [
      {
        "id": "5",
        "score": 0.999909486,
        "values": [58.79999923706055, 6.699999809265137, 3.4000000953674316],
        "metadata": {
          "url": "/products/sku/55519183"
        }
      },
      {
        "id": "4",
        "score": 0.789848214,
        "values": [75.0999984741211, 67.0999984741211, 29.899999618530273],
        "metadata": {
          "url": "/products/sku/418313"
        }
      },
      {
        "id": "2",
        "score": 0.611976262,
        "values": [15.100000381469727, 19.200000762939453, 15.800000190734863],
        "metadata": {
          "url": "/products/sku/10148191"
        }
      }
    ]
  }

}

From here, experiment by passing a different queryVector and observe the results: the matches and the score should change based on the change in distance between the query vector and the vectors in our index.

In a real-world application, the queryVector would be the vector embedding representation of a query from a user or system, and our sampleVectors would be generated from real content. To build on this example, read the vector search tutorial that combines Workers AI and Vectorize to build an end-to-end application with Workers.