from sentence_transformers import SentenceTransformer


model_id = "sentence-transformers/all-MiniLM-L6-v2"
sentences = [
    "Reset a user password",
    "Create a private S3 bucket",
    "Rotate an SSH key",
]

model = SentenceTransformer(model_id)
embeddings = model.encode(sentences, show_progress_bar=False)

print(f"model={model_id}")
print(f"input_count={len(sentences)}")
print(f"embedding_shape={embeddings.shape}")
print(f"embedding_dtype={embeddings.dtype}")
print(f"model_dimension={model.get_embedding_dimension()}")
print(f"first_vector_preview={[round(float(value), 4) for value in embeddings[0][:5]]}")