"""Milvus Lite: embedded Milvus from a local file — no server to start.
Creates a collection, inserts a few vectors, runs a similarity search, and
prints the closest row. Fully local and deterministic: no API key, no network.
Run:
docker build -t aas-milvus .
docker run --rm aas-milvus
"""
from pymilvus import MilvusClient
def main() -> None:
client = MilvusClient("/tmp/milvus-demo.db") # Milvus Lite — embedded file
if client.has_collection("docs"):
client.drop_collection("docs")
client.create_collection("docs", dimension=4)
client.insert(
"docs",
[
{"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "text": "Cats are independent pets that purr."},
{"id": 2, "vector": [0.9, 0.8, 0.7, 0.6], "text": "A rocket is propelled by ejecting exhaust."},
],
)
hits = client.search("docs", data=[[0.12, 0.21, 0.29, 0.41]], limit=1, output_fields=["text"])
print("nearest:", hits[0][0]["entity"]["text"])
if __name__ == "__main__":
main()
