from sdk.python.minikv_client import MinikvClient

client = MinikvClient()

# 1) Upsert vectors
client.vector_upsert("doc-1", [0.1, 0.2, 0.3], {"label": "anomaly"})
client.vector_upsert("doc-2", [0.11, 0.19, 0.29], {"label": "normal"})

# 2) Query nearest neighbors
result = client.vector_query([0.1, 0.2, 0.3], top_k=2)
print(result)

# 3) Query time-series and convert for analysis
ts = client.timeseries_query({"metric": "cpu.usage"})

# Optional dataframe conversion helpers
from sdk.python.minikv_client import to_pandas_points
print(to_pandas_points(ts).head())

for event in client.watch_sse():
    print(event)