bge-base-en-v1.5

Text Embeddings • BAAI

@cf/baai/bge-base-en-v1.5

• Cloudflare-hosted
• Batch

BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector

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-base-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-base-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-base-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"] }'

OpenAI compatible endpoints

Workers AI also supports OpenAI compatible API endpoints for /v1/chat/completions and /v1/embeddings. For more details, refer to Configurations.

Parameters

<details>

<summary>Synchronous — Send a request and receive a complete response</summary>

▶text

<code>one of</code>required

pooling

<code>string</code>default: 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[]

<code>array</code>

▶data[]

<code>array</code>Embeddings of the requested text values

pooling

<code>string</code>enum: mean, clsThe pooling method used in the embedding process.

</details>

<details>

<summary>Batch — Send multiple requests in a single API call</summary>

▶requests[]

<code>array</code>requiredBatch of the embeddings requests to run using async-queue

▶shape[]

<code>array</code>

▶data[]

<code>array</code>Embeddings of the requested text values

pooling

<code>string</code>enum: mean, clsThe pooling method used in the embedding process.

</details>

API Schemas (Raw)

SynchronousInput Open Download

SynchronousOutput Open Download

BatchInput Open Download

BatchOutput Open Download

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description: BAAI general embedding (Large) model that transforms any given text into a 1024-dimensional vector title: bge-large-en-v1.5 image: https://developers.cloudflare.com/og-docs.png

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