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  • Python
    • EmbeddingModels

__call__()

This operation in SpladeEmbeddingFunction takes a list of text strings and directly encodes them into vector embeddings.

Unlike encode_documents() or encode_queries(), which enable you to prepend doc_instruction or query_instruction and utilize k_tokens_document or k_tokens_query for result pruning, the call() method directly returns embeddings without offering the option to prepend instructions or prune results.

Request syntax

# Instance created
splade_ef = SpladeEmbeddingFunction()

# __call__ method will be called
splade_ef(
    texts: List[str]
) -> csr_array

PARAMETERS:

  • texts (List[str])

    A list of string values, where each string represents text that will be passed to the embedding model for encoding. The model will generate an embedding vector for each string in the list.

RETURN TYPE:

csr_array

RETURNS:

Compressed sparse row matrices representing the document embeddings.

Exceptions:

  • ImportError

    This exception will be raised when the transformers library is not installed.

Examples

from pymilvus import model

splade_ef = model.sparse.SpladeEmbeddingFunction(
    model_name="naver/splade-cocondenser-selfdistil", 
    device="cpu"
)

docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]

splade_ef(docs)

# <3x30522 sparse array of type '<class 'numpy.float32'>'
#   with 298 stored elements in Compressed Sparse Row format>