encode_documents()

This operation takes in documents and encodes them into vector embeddings.

Request syntax

encode_documents(
    documents: List[str], 
) -> csr_array

PARAMETERS:

• documents (List[str])

  A list of string values, where each string represents a document 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.",
]

docs_embeddings = splade_ef.encode_documents(docs)

# Print embeddings
print("Embeddings:", docs_embeddings)
# since the output embeddings are in a 2D csr_array format, we convert them to a list for easier manipulation.
print("Sparse dim:", splade_ef.dim, list(docs_embeddings)[0].shape)

# Embeddings:   (0, 2001)   0.6392706036567688
#   (0, 2034)   0.024093208834528923
#   (0, 2082)   0.3230178654193878
# ...
#   (2, 23602)  0.5671860575675964
#   (2, 26757)  0.5770265460014343
#   (2, 28639)  3.1990697383880615
# Sparse dim: 30522 (1, 30522)