---
description: Multi-Functionality, Multi-Linguality, and Multi-Granularity embeddings model.
title: bge-m3
image: https://developers.cloudflare.com/og-docs.png
---

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# bge-m3

Text Embeddings • BAAI

Copy as Markdown|[View as Markdown](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/models/bge-m3/index.md)|[Agent setup](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/agent-setup/)

`@cf/baai/bge-m3`

* Cloudflare-hosted

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

| Model Info                                                                                                                                 |                           |
| ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------- |
| Context Window[ ↗](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/platform/glossary/) | 60,000 tokens             |
| Unit Pricing                                                                                                                               | $0.012 per M input tokens |

## Usage

```ts

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>;
```

```py

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())
```

```sh

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](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/configuration/open-ai-compatibility/).

## Parameters

Synchronous — Send a request and receive a complete response

query

`string`minLength: 1A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts

▶contexts\[\]

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

truncate\_inputs

`boolean`default: falseWhen provided with too long context should the model error out or truncate the context to fit?

request\_id

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

Batch — Send multiple requests in a single API call

▶requests\[\]

`array`requiredBatch of the embeddings requests to run using async-queue

request\_id

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

## API Schemas (Raw)

SynchronousInput

SynchronousOutput

BatchInput

BatchOutput

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```