bge-m3

b

bge-m3

Text Embeddings • baai

@cf/baai/bge-m3

Multi-Functionality, Multi-Linguality, and Multi-Granularity embeddings model.

Model Info
Batch	Yes
Unit Pricing	$0.012 per M input tokens

Usage

Workers - TypeScript

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-m3",
      {
        text: stories,
      }
    );

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

Python

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-m3",
  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-m3  \
  -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
  • query string min 1

    A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts

  • contexts array required

    List of provided contexts. Note that the index in this array is important, as the response will refer to it.

    • items object
      • text string min 1

        One of the provided context content

  • truncate_inputs boolean

    When provided with too long context should the model error out or truncate the context to fit?

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

      The text to embed

    • 1 array

      Batch of text values to embed

      • items string min 1

        The text to embed

  • truncate_inputs boolean

    When provided with too long context should the model error out or truncate the context to fit?

• 2 object
  • requests array required

    Batch of the embeddings requests to run using async-queue

    • items one of
      • 0 object
        • query string min 1

          A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts

        • contexts array required

          List of provided contexts. Note that the index in this array is important, as the response will refer to it.

          • items object
            • text string min 1

              One of the provided context content

        • truncate_inputs boolean

          When provided with too long context should the model error out or truncate the context to fit?

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

            The text to embed

          • 1 array

            Batch of text values to embed

            • items string min 1

              The text to embed

        • truncate_inputs boolean

          When provided with too long context should the model error out or truncate the context to fit?

Output

• 0 object
  • response array
    • items object
      • id integer

        Index of the context in the request

      • score number

        Score of the context under the index.

• 1 object
  • response array
    • items array
      • items number

  • shape array
    • items number

  • pooling string

    The pooling method used in the embedding process.

• 2 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.

• 3 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": [
        {
            "title": "BGE M3 Input Query and Contexts",
            "properties": {
                "query": {
                    "type": "string",
                    "minLength": 1,
                    "description": "A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts"
                },
                "contexts": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "text": {
                                "type": "string",
                                "minLength": 1,
                                "description": "One of the provided context content"
                            }
                        }
                    },
                    "description": "List of provided contexts. Note that the index in this array is important, as the response will refer to it."
                },
                "truncate_inputs": {
                    "type": "boolean",
                    "default": false,
                    "description": "When provided with too long context should the model error out or truncate the context to fit?"
                }
            },
            "required": [
                "contexts"
            ]
        },
        {
            "title": "BGE M3 Input Embedding",
            "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
                        }
                    ]
                },
                "truncate_inputs": {
                    "type": "boolean",
                    "default": false,
                    "description": "When provided with too long context should the model error out or truncate the context to fit?"
                }
            },
            "required": [
                "text"
            ]
        },
        {
            "properties": {
                "requests": {
                    "type": "array",
                    "description": "Batch of the embeddings requests to run using async-queue",
                    "items": {
                        "type": "object",
                        "oneOf": [
                            {
                                "title": "BGE M3 Input Query and Contexts",
                                "properties": {
                                    "query": {
                                        "type": "string",
                                        "minLength": 1,
                                        "description": "A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts"
                                    },
                                    "contexts": {
                                        "type": "array",
                                        "items": {
                                            "type": "object",
                                            "properties": {
                                                "text": {
                                                    "type": "string",
                                                    "minLength": 1,
                                                    "description": "One of the provided context content"
                                                }
                                            }
                                        },
                                        "description": "List of provided contexts. Note that the index in this array is important, as the response will refer to it."
                                    },
                                    "truncate_inputs": {
                                        "type": "boolean",
                                        "default": false,
                                        "description": "When provided with too long context should the model error out or truncate the context to fit?"
                                    }
                                },
                                "required": [
                                    "contexts"
                                ]
                            },
                            {
                                "title": "BGE M3 Input Embedding",
                                "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
                                            }
                                        ]
                                    },
                                    "truncate_inputs": {
                                        "type": "boolean",
                                        "default": false,
                                        "description": "When provided with too long context should the model error out or truncate the context to fit?"
                                    }
                                },
                                "required": [
                                    "text"
                                ]
                            }
                        ]
                    }
                }
            },
            "required": [
                "requests"
            ]
        }
    ]
}

Output

{
    "type": "object",
    "contentType": "application/json",
    "oneOf": [
        {
            "title": "BGE M3 Ouput Query",
            "properties": {
                "response": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "id": {
                                "type": "integer",
                                "description": "Index of the context in the request"
                            },
                            "score": {
                                "type": "number",
                                "description": "Score of the context under the index."
                            }
                        }
                    }
                }
            }
        },
        {
            "title": "BGE M3 Output Embedding for Contexts",
            "properties": {
                "response": {
                    "type": "array",
                    "items": {
                        "type": "array",
                        "items": {
                            "type": "number"
                        }
                    }
                },
                "shape": {
                    "type": "array",
                    "items": {
                        "type": "number"
                    }
                },
                "pooling": {
                    "type": "string",
                    "enum": [
                        "mean",
                        "cls"
                    ],
                    "description": "The pooling method used in the embedding process."
                }
            }
        },
        {
            "title": "BGE M3 Ouput Embedding",
            "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."
                }
            }
        }
    ]
}