bge-large-en-v1.5

Text Embeddings • BAAI • Hosted

BAAI general embedding (Large) model that transforms any given text into a 1024-dimensional vector

Model Info
More information	link ↗
Maximum Input Tokens	512
Output Dimensions	1,024
Batch	Yes
Unit Pricing	$0.20 per M input tokens

Usage

export interface Env {
  AI: Ai;
}

export default {
  async fetch(request, env): Promise<Response> {

    // 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 env.AI.run(
      "@cf/baai/bge-large-en-v1.5",
      {
        text: stories,
      }
    );

    return Response.json(embeddings);
  },
} satisfies ExportedHandler<Env>;

import os
import requests


ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")

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

response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-large-en-v1.5",
  headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
  json={"text": stories}
)

print(response.json())

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/baai/bge-large-en-v1.5  \
  -X POST  \
  -H "Authorization: Bearer $CLOUDFLARE_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"] }'

Parameters

Synchronous — Send a request and receive a complete response

Input
Output

▶text

one ofrequired

pooling

stringdefault: meanenum: mean, clsThe pooling method used in the embedding process. `cls` pooling will generate more accurate embeddings on larger inputs - however, embeddings created with cls pooling are not compatible with embeddings generated with mean pooling. The default pooling method is `mean` in order for this to not be a breaking change, but we highly suggest using the new `cls` pooling for better accuracy.

▶shape[]

array

▶data[]

arrayEmbeddings of the requested text values

pooling

stringenum: mean, clsThe pooling method used in the embedding process.

Batch — Send multiple requests in a single API call

Input
Output

▶requests[]

arrayrequiredBatch of the embeddings requests to run using async-queue

▶shape[]

array

▶data[]

arrayEmbeddings of the requested text values

pooling

stringenum: mean, clsThe pooling method used in the embedding process.

API Schemas (Raw)

Synchronous Input

Synchronous Output

Batch Input

Batch Output