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 OpenAI module is not installed.
Examples
from pymilvus import model
openai_ef = model.dense.OpenAIEmbeddingFunction(
model_name='text-embedding-3-large', # Specify the model name
dimensions=512 # Set the embedding dimensionality according to MRL feature.
)
queries = ["When was artificial intelligence founded",
"Where was Alan Turing born?"]
query_embeddings = openai_ef.encode_queries(queries)
# Print embeddings
print("Embeddings:", query_embeddings)
# Print dimension and shape of embeddings
print("Dim:", openai_ef.dim, query_embeddings[0].shape)
# Embeddings: [array([ 0.00530251, -0.01907905, -0.01672608, -0.05030033, 0.01635982,
# -0.03169853, -0.0033602 , 0.09047844, 0.00030747, 0.11853652,
# -0.02870182, -0.01526102, 0.05505067, 0.00993909, -0.07165466,
# ...
# -9.78106782e-02, -2.22669560e-02, 1.21873049e-02, -4.83198799e-02,
# 5.32377362e-02, -1.90469325e-02, 5.62430918e-02, 1.02650477e-02,
# -6.21757433e-02, 7.88027793e-02, 4.91846527e-04, -1.51633881e-02])]
# Dim: 512 (512,)