from doris_vector_search import DorisVectorClient, AuthOptions

auth = AuthOptions(
    host="192.168.1.242",
    query_port=9030,
    user="thebears",
    password="marybear",
)

client = DorisVectorClient(database="nuggets", auth_options=auth)

tbl = client.open_table("video_embeddings")

query = [0.1] * 1152  # Example 128-dimensional vector

# SELECT id FROM sift_1M ORDER BY l2_distance_approximate(embedding, query) LIMIT 10;
result = tbl.search(query, metric_type="inner_product").limit(10).select(["id"]).to_pandas()

print(result)