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:
-
ImportError
This exception will be raised when the model2vec module is not installed.
Examples
from pymilvus import model
gemini_ef = model.dense.GeminiEmbeddingFunction(
model_name="gemini-embedding-exp-03-07",
api_key="YOUR_API_KEY",
)
queries = ["When was artificial intelligence founded",
"Where was Alan Turing born?"]
query_embeddings = gemini_ef.encode_queries(queries)
# Print embeddings
print("Embeddings:", query_embeddings)
# Print dimension and shape of embeddings
print("Dim:", gemini_ef.dim, query_embeddings[0].shape)
# Embeddings: [array([-0.02066572, 0.02459551, 0.00707774, ..., 0.00259341,
# -0.01797572, -0.00626168], shape=(3072,)), array([ 0.00674969, 0.03023903, 0.01230692, ..., 0.00160009,
# -0.01710967, 0.00972728], shape=(3072,))]
# Dim 3072 (3072,)