bge-base-en-v1.5

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bge-base-en-v1.5

Text Embeddings • baai

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

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

Model Info
More information	link ↗
Maximum Input Tokens	512
Output Dimensions	768
Batch	Yes
Unit Pricing	$0.067 per M input tokens

Usage

Workers - TypeScript

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>;

Python

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

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

* indicates a required field

Input

• 0 object
  • text * one of
    • 0 string min 1

      The text to embed

    • 1 array

      Batch of text values to embed

      • items string min 1

        The text to embed

  • pooling string default mean

    The 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.

• 1 object
  • requests * array

    Batch of the embeddings requests to run using async-queue

    • items object
      • text * one of
        • 0 string min 1

          The text to embed

        • 1 array

          Batch of text values to embed

          • items string min 1

            The text to embed

      • pooling string default mean

        The 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.

Output

• 0 object
  • shape array
    • items number

  • data array

    Embeddings of the requested text values

    • items array

      Floating point embedding representation shaped by the embedding model

      • items number

  • pooling string

    The pooling method used in the embedding process.

• Async response object
  • request_id string

    The async request id that can be used to obtain the results.

API Schemas

The following schemas are based on JSON Schema

Input

{
    "type": "object",
    "oneOf": [
        {
            "properties": {
                "text": {
                    "oneOf": [
                        {
                            "type": "string",
                            "description": "The text to embed",
                            "minLength": 1
                        },
                        {
                            "type": "array",
                            "description": "Batch of text values to embed",
                            "items": {
                                "type": "string",
                                "description": "The text to embed",
                                "minLength": 1
                            },
                            "maxItems": 100
                        }
                    ]
                },
                "pooling": {
                    "type": "string",
                    "enum": [
                        "mean",
                        "cls"
                    ],
                    "default": "mean",
                    "description": "The 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."
                }
            },
            "required": [
                "text"
            ]
        },
        {
            "properties": {
                "requests": {
                    "type": "array",
                    "description": "Batch of the embeddings requests to run using async-queue",
                    "items": {
                        "properties": {
                            "text": {
                                "oneOf": [
                                    {
                                        "type": "string",
                                        "description": "The text to embed",
                                        "minLength": 1
                                    },
                                    {
                                        "type": "array",
                                        "description": "Batch of text values to embed",
                                        "items": {
                                            "type": "string",
                                            "description": "The text to embed",
                                            "minLength": 1
                                        },
                                        "maxItems": 100
                                    }
                                ]
                            },
                            "pooling": {
                                "type": "string",
                                "enum": [
                                    "mean",
                                    "cls"
                                ],
                                "default": "mean",
                                "description": "The 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."
                            }
                        },
                        "required": [
                            "text"
                        ]
                    }
                }
            },
            "required": [
                "requests"
            ]
        }
    ]
}

Output

{
    "oneOf": [
        {
            "type": "object",
            "contentType": "application/json",
            "properties": {
                "shape": {
                    "type": "array",
                    "items": {
                        "type": "number"
                    }
                },
                "data": {
                    "type": "array",
                    "description": "Embeddings of the requested text values",
                    "items": {
                        "type": "array",
                        "description": "Floating point embedding representation shaped by the embedding model",
                        "items": {
                            "type": "number"
                        }
                    }
                },
                "pooling": {
                    "type": "string",
                    "enum": [
                        "mean",
                        "cls"
                    ],
                    "description": "The pooling method used in the embedding process."
                }
            }
        },
        {
            "type": "object",
            "contentType": "application/json",
            "title": "Async response",
            "properties": {
                "request_id": {
                    "type": "string",
                    "description": "The async request id that can be used to obtain the results."
                }
            }
        }
    ]
}