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  1. Products
  2. Workers AI
  3. Models
  4. Embedding

Embeddings

Feature extraction models transform raw data into numerical features that can be processed while preserving the information in the original dataset.

These models are ideal as part of building vector search applications or Retrieval Augmented Generation workflows with Large Language Models (LLM).

​​ Available Embedding Models

Workers AI includes the following built-in text embedding models:

Model IDMax Input Tokens 1Output Dimensions
@cf/baai/bge-small-en-v1.5512 tokens384
@cf/baai/bge-base-en-v1.5512 tokens768
@cf/baai/bge-large-en-v1.5512 tokens1024

1 An English word is approximately 1-3 tokens, depending on word length and representation within the model.

​​ Model details

​​ Examples


import { Ai } from '@cloudflare/ai'



export interface Env {
  AI: any;

}



export default {
  async fetch(request: Request, env: Env) {
    const ai = new Ai(env.AI);


    // Can be a string or array of strings]
    const stories = [
      'This is a story about an orange cloud',
      'This is a story about a llama',
      'This is a story about a hugging emoji'
    ]


    const embeddings = await ai.run('@cf/baai/bge-base-en-v1.5', {
        text: stories
      }
    );


    return Response.json(embeddings);
  },

};


async function run(model, input) {
	const response = await fetch(
	  `https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/${model}`,
	  {
		headers: { Authorization: "Bearer ${API_TOKEN}" },
		method: "POST",
		body: JSON.stringify(input),
	  }
	);
	const result = await response.json();
	return result;

}
  
// Can be a string or array of strings]

const stories = [

'This is a story about an orange cloud',

'This is a story about a llama',

'This is a story about a hugging emoji'

];



run('@cf/baai/bge-base-en-v1.5', { text: stories }).then((response) => {
  console.log(JSON.stringify(response));

});


import requests
API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}}/ai/run/"
headers = {"Authorization": "Bearer {API_TOKEN}"}



def run(model, input):
    response = requests.post(f"{API_BASE_URL}{model}", headers=headers, json=input)
    return response.json()


stories = [
  'This is a story about an orange cloud',
  'This is a story about a llama',
  'This is a story about a hugging emoji'

]
    
output = run("@cf/baai/bge-base-en-v1.5", { "text": stories })

print(output)


$ curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-base-en-v1.5 \
  -X POST \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{ "text": ["This is a story about an orange cloud", "This is a story about a llama", "This is a story about a hugging emoji"] }

Example Workers AI response


{
  "input": {
    "text":"Tell me a joke about Cloudflare"
  },
  "response": {
    "shape":[1,768],
    "data": [
      [0.03190500661730766, 0.006071353796869516, 0.025971125811338425,...]
    ]
  },
  "batchedInput": {
    "text": ["Tell me a joke about Cloudflare","The weather is sunny"]
  },
  "batchedResponse": {
    "shape":[2,768],
    "data":[
      [0.03190416097640991, 0.006062490865588188, 0.025968171656131744,...],
      [0.002439928939566016, -0.021352028474211693, 0.06229676678776741,...],
      [-0.02154572866857052,0.09098546206951141,0.006273532286286354,...]
    ]
  }

}

​​ API schema

The following schema is based on JSON Schema


{
    "task": "text-embeddings",
    "tsClass": "AiTextEmbeddings",
    "jsonSchema": {
        "input": {
            "type": "object",
            "properties": {
                "text": {
                    "oneOf": [
                        { "type": "string" },
                        {
                            "type": "array",
                            "items": {
                                "type": "string"
                            }
                        }
                    ]
                }
            },
            "required": ["text"]
        },
        "output": {
            "type": "object",
            "properties": {
                "shape": {
                    "type": "array",
                    "items": {
                        "type": "number"
                    }
                },
                "data": {
                    "type": "array",
                    "items": {
                        "type": "array",
                        "items": {
                            "type": "number"
                        }
                    }
                }
            }
        }
    }

}