__call()__

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

The __call__() method of CohereEmbeddingFunction shares the same functionality as encode_documents() and encode_queries().

Request syntax

# Instance created

cohere_ef = CohereEmbeddingFunction()

# __call__ method will be called
cohere_ef(
    texts: List[str]
) -> List[np.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:

List[np.array]

RETURNS:

A list where each element is a NumPy array.

Exceptions:

• ValueError

  This exception will be raised when you specify multiple embedding types or use the int8 or uint8 data type for CohereEmbeddingFunction initialization.

Examples

from pymilvus.model.dense import CohereEmbeddingFunction

cohere_ef = CohereEmbeddingFunction(
    model_name="embed-english-light-v3.0",
    api_key="YOUR_COHERE_API_KEY",
    input_type="search_document",
    embedding_types=["float"]
)

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.",
]

cohere_ef(docs)

# [array([ 3.43322754e-02,  1.16252899e-03, -5.25207520e-02,  1.32846832e-03,
#         -6.80541992e-02,  6.10961914e-02, -7.06176758e-02,  1.48925781e-01,
#          1.54174805e-01,  1.98516846e-02,  2.43835449e-02,  3.55224609e-02,
#          1.82952881e-02,  7.57446289e-02, -2.40783691e-02,  4.40063477e-02,
# ...
#          0.06008911, -0.05160522, -0.02758789, -0.06750488,  0.03050232,
#          0.01448822,  0.0236969 ,  0.09527588, -0.01791382, -0.04812622,
#          0.06359863, -0.01971436, -0.02253723,  0.00354195,  0.00222015,
#          0.00184727,  0.03408813, -0.00777817,  0.04919434,  0.01519775,
#         -0.02862549,  0.04760742, -0.07891846,  0.0124054 ], dtype=float32)]