SpladeEmbeddingFunction
SpladeEmbeddingFunction is a class in pymilvus that handles encoding text into embeddings using SPLADE models to support embedding retrieval in Milvus.
pymilvus.model.sparse.SpladeEmbeddingFunction
Constructor
Constructs a SpladeEmbeddingFunction for common use cases.
SpladeEmbeddingFunction(
model_name: str = "naver/splade-cocondenser-ensembledistil",
batch_size: int = 32,
query_instruction: str = "",
doc_instruction: str = "",
device: Optional[str] = "cpu",
k_tokens_query: Optional[int] = None,
k_tokens_document: Optional[int] = None,
**kwargs,
)
PARAMETERS:
-
model_name (string) -
The name of the SPLADE model to use for encoding. Valid options are naver/splade-cocondenser-ensembledistil (default), naver/splade_v2_max, naver/splade_v2_distil, and naver/splade-cocondenser-selfdistil. For more information, refer to Play with models.
-
batch_size (int) -
The batch size used for the computation.
-
query_instruction (string) -
The query to use for encoding.
-
doc_instruction (string) -
The document to use for encoding.
-
device (string) -
The device to use, with cpu for the CPU and cuda:n for the nth GPU device.
-
k_tokens_query (int) -
The number of top tokens to use for query encodings. If not specified, it will use all non-zero tokens.
-
k_tokens_document (int) -
The number of top tokens to use for document encodings. If not specified, it will use all non-zero tokens.
-
**kwargs
Allows additional keyword arguments to be passed to the model initialization. For more information, refer to AutoModelForMaskedLM.
Examples
from pymilvus import model
splade_ef = model.sparse.SpladeEmbeddingFunction(
model_name="naver/splade-cocondenser-selfdistil",
device="cpu"
)