Revert "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"

This reverts commit 79e008e5730d9501b9a0c1fd955bfd6e8e5b0776.

handler.py

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