| name | milvus |
| description | Milvus — cloud-native vector database for billion-scale similarity search. GPU-accelerated indexing, hybrid search, multi-vector, streaming, and time travel. Distributed deployment with Kubernetes. |
| tags | ["milvus","vector-database","similarity-search","scale","embeddings","infrastructure","zorai"] |
Overview
Milvus is a cloud-native vector database for billion-scale similarity search. Supports GPU-accelerated indexing (IVF, HNSW, DiskANN), hybrid search (dense + sparse), multi-vector, streaming ingestion, time travel, and distributed deployment with Kubernetes.
Installation
docker compose -f https://github.com/milvus-io/milvus/releases/latest/download/milvus-standalone-docker-compose.yml up -d
Python Client
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
connections.connect(host="localhost", port=19530)
schema = CollectionSchema([
FieldSchema("id", DataType.INT64, is_primary=True),
FieldSchema("embedding", DataType.FLOAT_VECTOR, dim=384),
FieldSchema("text", DataType.VARCHAR, max_length=1000),
])
collection = Collection("documents", schema)
collection.create_index("embedding", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128}})
collection.load()
References
