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| from sentence_transformers import SentenceTransformer | |
| model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu') | |
| def get_embedding(text: str): | |
| try: | |
| vec = model.encode(text) | |
| vec = vec.flatten() | |
| assert vec.shape[0] == 384, f"Expected embedding of size 384, got {vec.shape[0]}" | |
| return vec | |
| except Exception as e: | |
| print(f"Embedding Error: {e}") | |
| return None | |