create and update emnedding generater file

relevance-scoring/embeddings/embedding_generator.py

@@ -0,0 +1,32 @@
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the pre-trained model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def generate_embedding(text):
+    """
+    Generate embedding for a given text using the pre-trained model.
+    """
+    return model.encode(text)
+
+def calculate_similarity(embedding1, embedding2):
+    """
+    Calculate cosine similarity between two embeddings.
+    """
+    return cosine_similarity([embedding1], [embedding2])[0][0]
+
+if __name__ == "__main__":
+    # Example texts
+    text1 = "a UI with red lines on the side showing a spacing issue"
+    text2 = "an issue with layout spacing in the UI"
+
+    # Generate embeddings
+    emb1 = generate_embedding(text1)
+    emb2 = generate_embedding(text2)
+
+    # Calculate similarity
+    similarity = calculate_similarity(emb1, emb2)
+
+    # Print the similarity score
+    print(f"Similarity: {similarity:.2f}")