カスタムハンドラではなく、デフォルトでスパースに対応する

handler.py

@@ -1,61 +0,0 @@
-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}
-            ]
-        ]