from sentence_transformers import SentenceTransformer, SimilarityFunction


model = SentenceTransformer(
    "sentence-transformers/all-MiniLM-L6-v2",
    similarity_fn_name=SimilarityFunction.COSINE,
)

reference_texts = [
    "Reset an account password.",
    "Schedule a database backup.",
    "Bake sourdough bread.",
]
queries = [
    "Change my account password.",
    "Schedule a backup for the database.",
]

reference_embeddings = model.encode(reference_texts)
query_embeddings = model.encode(queries)
scores = model.similarity(query_embeddings, reference_embeddings)

print("similarity function:", model.similarity_fn_name)
print("score matrix:")
for query, row in zip(queries, scores):
    print(query)
    print("  " + "  ".join(f"{float(score):.4f}" for score in row))

print("top matches:")
for query, row in zip(queries, scores):
    best_index = int(row.argmax())
    best_score = float(row[best_index])
    print(f"{best_score:.4f} | {query} -> {reference_texts[best_index]}")