# BGE m3

**Summary:** BGE m3 is a versatile multilingual embedding model that supports over 100 languages with an extended 8192 token context window, offering both dense and sparse retrieval capabilities. This model excels in cross-language information retrieval, multilingual document analysis, and global content discovery, making it essential for international applications, multilingual search systems, and global knowledge management platforms where language diversity and long-context understanding are crucial.

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## Central parameters

**Description:** Multilingual embedding model by Beijing Academy of Artificial Intelligence with extended input length support and 1024-dimensional output vectors.

**Model identifier:** `BAAI/bge-m3`

## IONOS AI Model Hub Lifecycle and Alternatives

|  **IONOS Launch** | **End of Life** |                                                                      **Alternative**                                                                     | **Successor** |
| :---------------: | :-------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------: |
| *January 1, 2025* |       N/A       | [<mark style="color:blue;">**BGE Large V1.5**</mark>](https://docs.ionos.com/sections-test/guides/ai/ai-model-hub/models/embedding-models/bge-large-1-5) |               |

## Origin

|                               **Provider**                              | **Country** |                                                  **License**                                                  | **Flavor** |     **Release**    |
| :---------------------------------------------------------------------: | :---------: | :-----------------------------------------------------------------------------------------------------------: | :--------: | :----------------: |
| [<mark style="color:blue;">**BAAI**</mark>](https://www.baai.ac.cn/en/) |  Community  | [<mark style="color:blue;">**License**</mark>](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE) |      -     | *January 28, 2024* |

## Technology

| **Input Length** | **Parameters** | **Tensor Type** | **Multilingual** |                                   **Further details**                                   |
| :--------------: | :------------: | :-------------: | :--------------: | :-------------------------------------------------------------------------------------: |
|      *8192*      |     *568M*     |     *float*     |       *Yes*      | [<mark style="color:blue;">**Hugging Face**</mark>](https://huggingface.co/BAAI/bge-m3) |

## Modalities

|     **Text**     |   **Image**   |   **Audio**   |
| :--------------: | :-----------: | :-----------: |
| Input and output | Not supported | Not supported |

## Endpoints

| **Chat Completions** | **Embeddings** | **Image generation** |
| :------------------: | :------------: | :------------------: |
|     Not supported    |  v1/embeddings |     Not supported    |

## Features

| **Streaming** | **Reasoning** | **Tool calling** |
| :-----------: | :-----------: | :--------------: |
| Not supported | Not supported |   Not supported  |

## Usage example

### Embeddings

The following example demonstrates how to generate text embeddings using **BGE m3**.

**API Endpoint:** `POST https://openai.inference.de-txl.ionos.com/v1/embeddings`

**Request:**

```json
{
  "model": "BAAI/bge-m3",
  "input": "The food was delicious and the waiter was friendly."
}
```

**Response:**

```json
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        0.0028842222
      ],
      "index": 0
    }
  ],
  "model": "BAAI/bge-m3",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}
```

## Rate limits

Rate limits ensure fair usage and reliable access to the AI Model Hub. In addition to the [<mark style="color:blue;">contract-wide rate limits</mark>](https://docs.ionos.com/sections-test/guides/ai/ai-model-hub/how-tos/rate-limits), no model-specific limits apply.