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from typing import Dict, List, Any
# from optimum.onnxruntime import ORTModelForSequenceClassification
# from transformers import pipeline, AutoTokenizer
from FlagEmbedding import BGEM3FlagModel
import time
class EndpointHandler():
def __init__(self, path="."):
# load the optimized model
# モデルの準備
self.model = BGEM3FlagModel(path, use_fp16=True)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
- "label": A string representing what the label/class is. There can be multiple labels.
- "score": A score between 0 and 1 describing how confident the model is for this label/class.
"""
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
# encodeメソッドの実行前に時間を記録
start_time = time.time()
result = self.model.encode(inputs, return_dense=False, return_sparse=True, max_length=1024)
# encodeメソッドの実行後に時間を記録
end_time = time.time()
# print(result)
# dense_vectors = result["dense_vecs"]
# 経過時間を計算
elapsed_time = end_time - start_time
print(f"Encoding took {elapsed_time:.4f} seconds")
sparse_vectors = result["lexical_weights"]
# defaultdict(<class 'int'>, {'6': 0.09546, '192661': 0.3323})
# pass inputs with all kwargs in data
# if parameters is not None:
# prediction = self.pipeline(inputs, **parameters)
# else:
# prediction = self.pipeline(inputs)
# postprocess the prediction
# レスポンスをの型をkey=str, value=floatのdictにする。なお、numpy.float16はjsonに変換できないので、floatに変換する。
sparse_vectors = {str(k): float(v) for k, v in sparse_vectors.items()}
# レスポンスの型をnumpy.ndarrayから、通常のarrayに変更する
# dense_vectors = dense_vectors.tolist()
return [
[
{ "outputs": sparse_vectors}
]
]
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