Text 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).
- Task type: text-embeddings
- TypeScript class: AiTextEmbeddings
Available models
List of available Text Embeddings models:
| Model ID | Description |
|---|---|
@cf/baai/bge-small-en-v1.5 | BAAI general embedding (bge) models transform any given text into a compact vector Max input tokens: 512 Output dimensions: 384 More information |
@cf/baai/bge-base-en-v1.5 | BAAI general embedding (bge) models transform any given text into a compact vector Max input tokens: 512 Output dimensions: 768 More information |
@cf/baai/bge-large-en-v1.5 | BAAI general embedding (bge) models transform any given text into a compact vector Max input tokens: 512 Output dimensions: 1024 More information |
An English word is approximately 1-3 tokens, depending on word length and representation within the model.
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 requestsAPI_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
Single string:
{ "shape":[1,768], "data": [ [0.03190500661730766, 0.006071353796869516, 0.025971125811338425,...] ]
}
Batch of two strings:
{ "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
Input
{ "type": "object", "properties": { "text": { "oneOf": [ { "type": "string" }, { "type": "array", "items": { "type": "string" }, "maxItems": 100 } ] } }, "required": [ "text" ]
}
TypeScript class: AiTextEmbeddingsInput
Output
{ "type": "object", "contentType": "application/json", "properties": { "shape": { "type": "array", "items": { "type": "number" } }, "data": { "type": "array", "items": { "type": "array", "items": { "type": "number" } } } }
}
TypeScript class: AiTextEmbeddingsOutput