Added embedding handler

handler.py

@@ -0,0 +1,31 @@
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Initialize the tokenizer and model with pre-trained weights
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModel.from_pretrained(path)
+
+    def __call__(self, data):
+        # Extract text input from the request data
+        inputs = data['inputs']
+
+        # Define a prompt to provide context
+        prompt = "Contextual understanding of the following text, from the perspective of Chassidic philosophy: "
+
+        # Combine prompt with the actual input
+        combined_input = prompt + inputs
+
+        # Prepare the text for the model
+        encoded_input = self.tokenizer(combined_input, return_tensors='pt', padding=True, truncation=True, max_length=512)
+
+        # Generate embeddings without updating gradients
+        with torch.no_grad():
+            outputs = self.model(**encoded_input)
+
+        # Extract embeddings from the last hidden layer
+        embeddings = outputs.last_hidden_state.squeeze().tolist()
+
+        # Return the embeddings as a list (serialized format)
+        return {'embeddings': embeddings}

requirements.txt

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+torch==1.11.0
+transformers==4.18.0
+numpy==1.22.3