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    • Rerankers
      • BGERerankFunction

BGERerankFunction

BGERerankFunction is a class in milvus_model that takes a query and document as input and directly returns a similarity score instead of embeddings. This functionality uses the underlying BGE reranking model.

pymilvus.model.reranker.BGERerankFunction

Constructor

Constructs a BGERerankFunction for common use cases.

BGERerankFunction(
    model_name: str = "BAAI/bge-reranker-v2-m3",
    use_fp16: bool = True,
    batch_size: int = 32,
    normalize: bool = True,
    device: Optional[str] = None,
)

PARAMETERS:

  • model_name (string) -

    The name of the model to use. You can specify any of the available BGE reranker model names, for example, BAAI/bge-reranker-base, BAAI/bge-reranker-large, etc. If you leave this parameter unspecified, BAAI/bge-reranker-v2-m3 will be used. For a list of available models, refer to Model List.

  • use_fp16 (bool) -

    Whether to utilize 16-bit floating-point precision (fp16). The value is false when device is cpu.

  • batch_size (int) -

    The batch size used for the computation.

  • normalize (bool)

    Whether to normalize the reranking scores.

  • device (string) -

    Optional. The device to use for running the model. If not specified, the model will be run on the CPU. You can specify cpu for the CPU and cuda:n for the nth GPU device.

Examples

from pymilvus.model.reranker import BGERerankFunction

# Define the rerank function
bge_rf = BGERerankFunction(
    model_name="BAAI/bge-reranker-v2-m3",  # Specify the model name. Defaults to `BAAI/bge-reranker-v2-m3`.
    device="cpu" # Specify the device to use, e.g., 'cpu' or 'cuda:0'
)