from sentence_transformers import SentenceTransformer


model = SentenceTransformer("support-mnrl-model")
query = "How can I replace an API token?"
documents = [
    "Revoke the old API token and create a replacement token.",
    "Open billing settings and update the primary contact.",
    "Open deleted projects and restore the selected project.",
]

query_embedding = model.encode([query])
document_embeddings = model.encode(documents)
scores = model.similarity(query_embedding, document_embeddings)[0]
best_index = scores.argmax().item()

print(f"embedding width: {model.get_embedding_dimension()}")
print(f"top document: {documents[best_index]}")
print(f"top score: {float(scores[best_index]):.4f}")

if best_index != 0:
    raise SystemExit("The matching API-token answer did not rank first.")