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encode_queries()

This operation takes in a list of query strings and encodes each query into a vector embedding.

Request syntax

encode_queries(
queries: List[str],
) -> List[np.array]

PARAMETERS:

  • queries (List[str])

    A list of string values, where each string represents a query 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=COHERE_API_KEY,
input_type="search_document",
embedding_types=["float"]
)

queries = ["When was artificial intelligence founded",
"Where was Alan Turing born?"]

query_embeddings = cohere_ef.encode_queries(queries)

print("Embeddings:", query_embeddings)
print("Dim", cohere_ef.dim, query_embeddings[0].shape)

# Embeddings: [array([-1.33361816e-02, 9.79423523e-04, -7.28759766e-02, -1.93786621e-02,
# -9.71679688e-02, 4.34875488e-02, -9.81445312e-02, 1.16882324e-01,
# 5.89904785e-02, -4.19921875e-02, 4.95910645e-02, 5.83496094e-02,
# 3.47595215e-02, -5.87463379e-03, -7.30514526e-03, 2.92816162e-02,
# ...
# 0.00749969, -0.01192474, 0.02719116, 0.03347778, 0.07696533,
# 0.01409149, 0.00964355, -0.01681519, -0.0073204 , 0.00043154,
# -0.04577637, 0.03591919, -0.02807617, -0.04812622], dtype=float32)]
# Dim 384 (384,)