back_end/models/embedding_model.py

# back_end/models/embedding_model.py
from sentence_transformers import SentenceTransformer

# Load the pre-trained embedding model
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)

def generate_embedding(text: str):
    """Generate a 768-dimensional embedding for the input text."""
    embedding = model.encode(text).tolist()  # Convert NumPy array to list
    return embedding