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BGEM3EmbeddingFunction is a class in pymilvus that handles encoding text into embeddings using the BGE M3 model to support embedding retrieval in Milvus.



Constructs a BGEM3EmbeddingFunction for common use cases.

    model_name: str = "BAAI/bge-m3",
    batch_size: int = 16,
    device: str = "",
    normalize_embeddings: bool = True,
    use_fp16: bool = True,
    return_dense: bool = True,
    return_sparse: bool = True,
    return_colbert_vecs: bool = False,


  • model_name (string) -

    The name of the model to use for encoding. The value defaults to BAAI/bge-m3.

  • batch_size (int) -

    The batch size used for the computation.

  • device (string) -

    The device to use, with cpu for the CPU and cuda:n for the nth GPU device.

  • normalize_embeddings (bool) -

    Whether to normalize embedding vectors to unit length.

  • use_fp16 (bool) -

    Whether to utilize 16-bit floating-point precision (fp16). Specify False when device is cpu.

  • return_dense (bool) -

    Whether to return dense embedding vectors.

  • return_sparse (bool) -

    Whether to return sparse embedding vectors.

  • return_colbert_vecs (bool) -

    Whether to return ColBERT-style contextualized embedding vectors.

  • ****kwargs**

    Allows additional keyword arguments to be passed to the model initialization. For more information, refer to bge_m3.


from pymilvus import model

bge_m3_ef = model.hybrid.BGEM3EmbeddingFunction(
    model_name='BAAI/bge-m3', # Specify t`he model name
    device='cpu', # Specify the device to use, e.g., 'cpu' or 'cuda:0'
    use_fp16=False # Whether to use fp16. `False` for `device='cpu'`.


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