scores = model.similarity(query_embedding, document_embeddings)[0]
best_index = int(scores.argmax())
prompt_delta = float(abs(query_embedding - named_query_embedding).max())

assert query_embedding.shape[1] == document_embeddings.shape[1]
assert best_index == 1
assert prompt_delta < 1e-6

print(f"prompt keys: {', '.join(sorted(model.prompts))}")
print(f"query shape: {query_embedding.shape}")
print(f"document shape: {document_embeddings.shape}")
print(f"named prompt delta: {prompt_delta:.6f}")
print(f"top match: doc-{best_index + 1}")
print(f"score: {scores[best_index]:.3f}")
print(f"text: {documents[best_index]}")