import numpy as np
from sklearn.cluster import KMeans


def cluster_embeddings(
    embeddings: np.ndarray,
    n_clusters: int = 4,
    random_state: int = 42,
) -> list[int]:
    if len(embeddings) == 0:
        return []

    effective_clusters = min(n_clusters, len(embeddings))
    if effective_clusters == 1:
        return [0]

    kmeans = KMeans(
        n_clusters=effective_clusters,
        random_state=random_state,
        n_init=10,
    )
    return kmeans.fit_predict(embeddings).tolist()