from sentence_transformers import SentenceTransformer
import time

t0 = time.time()
model = SentenceTransformer("BAAI/bge-large-zh-v1.5", max_seq_length=512)
vec_dim = model.get_sentence_embedding_dimension()
t1 = time.time()
print(f"Model loaded in {t1-t0:.1f}s, vec_dim={vec_dim}")

vec = model.encode(["肠道菌群和抑郁有什么关系？"])
print(f"Sample vec shape: {vec.shape}")
print(f"Sample vec[0][:5]: {vec[0][:5]}")