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:
None
Examples
from pymilvus.model.dense import OnnxEmbeddingFunction
onnx_ef = OnnxEmbeddingFunction(
model_name="GPTCache/paraphrase-albert-onnx", # Defaults to `GPTCache/paraphrase-albert-onnx`
tokenizer_name="GPTCache/paraphrase-albert-small-v2" # Defaults to `GPTCache/paraphrase-albert-small-v2`
)
queries = ["When was artificial intelligence founded",
"Where was Alan Turing born?"]
query_embeddings = onnx_ef.encode_queries(queries)
print("Embeddings:", query_embeddings)
print("Dim", onnx_ef.dim, query_embeddings[0].shape)
# Embeddings: [array([-1.09502957e-02, -2.61731189e-02, -1.14003704e-02, 1.87525299e-02,
# 4.06063837e-02, 1.50731323e-02, -3.68221761e-03, 1.09151563e-03,
# 5.71931723e-02, -3.04123055e-02, -1.23123940e-02, -1.68146057e-02,
# -9.35562516e-03, -4.28719301e-02, 1.35385097e-02, -1.47082414e-02,
# ...
# 2.29728036e-02, 1.30193396e-02, -3.18266590e-02, -2.95146697e-03,
# 2.25738962e-02, 7.75775969e-02, -2.46181466e-02, 3.65723938e-02,
# 8.26405265e-02, -3.07154769e-02, 3.95052996e-03, -3.55286066e-02])]
# Dim 768 (768,)