import sentence_transformers as st


org = "sentence-transformers"
model_name = "all-MiniLM-L6-v2"
model_id = f"{org}/{model_name}"
model = st.SentenceTransformer(model_id)

sentences = [
    "Reset a user password",
    "Create an S3 bucket",
]

embeddings = model.encode(sentences)
get_dim = model.get_embedding_dimension
reported_dim = get_dim()
actual_dim = embeddings.shape[1]
match = actual_dim == reported_dim

print(f"model={model_name}")
print(f"reported_dim={reported_dim}")
print(f"shape={embeddings.shape}")
print(f"match={match}")

if not match:
    raise SystemExit(
        f"mismatch: shape={actual_dim}, dim={reported_dim}"
    )