BGEM3EmbeddingFunction
BGEM3EmbeddingFunction is a class in pymilvus that handles encoding text into embeddings using the BGE M3 model to support embedding retrieval in Milvus.
pymilvus.model.hybrid.BGEM3EmbeddingFunction
Constructor
Constructs a BGEM3EmbeddingFunction for common use cases.
BGEM3EmbeddingFunction(
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,
**kwargs,
)
PARAMETERS:
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.
Examples
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'`.
)