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from typing import Dict, List, Any
# from sentence_transformers import SentenceTransformer
class EndpointHandler():
def __init__(self, path="NV-Embed-v2"):
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
self.embedding_model = SentenceTransformer(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# embeddings = self.embedding_model.encode(data)
# return embeddings
# pseudo
# self.model(input) |