Milvus

Managed Milvus, an open-source vector database for embeddings and similarity search, with standalone or clustered high-availability deployments.

Creating a Milvus instance

From the phase canvas, click + Service → Databases → Milvus, then Commit. Milvus is a vector database: it stores high-dimensional embeddings and finds the most similar vectors to a query, which makes it a common building block for AI features such as semantic search, recommendations, and retrieval-augmented generation (RAG).

Attach Milvus from the add-on Connections list or the app Add-ons tab. StackBlaze injects MILVUS_HOST, MILVUS_PORT, MILVUS_URI, and MILVUS_URL.

Connecting

Milvus speaks gRPC and listens on port 19530. Connect over the private network using the internal hostname of your service. The injected MILVUS_URI points at this address.

[service-name]:19530

Browse and query with CloudBeaver

Every database add-on ships with CloudBeaver. Open with DB Beaver on the add-on's Overview starts a scoped session where you can browse tables, run SQL, and edit data in the browser.

Standard vs high availability

Standard runs Milvus in standalone mode: a single process that holds the query, data, and index roles together. It is simple and well suited to development and smaller workloads.

High availability runs Milvus in cluster mode. The query, data, and index components run as separate, independently scalable services, each with replicas. This lets the database scale out across nodes and tolerate the loss of a single node without going down. You choose Standard or HA when creating the instance.

Connecting from Python

Install pymilvus and read the connection target from MILVUS_URI. The snippet below connects, creates a collection with a vector field, inserts nothing yet, and runs a similarity search.

import os
from pymilvus import MilvusClient, DataType

client = MilvusClient(uri="http://" + os.environ["MILVUS_URI"])

# Create a collection with a 768-dimensional vector field
schema = client.create_schema(auto_id=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("embedding", DataType.FLOAT_VECTOR, dim=768)

client.create_collection("documents", schema=schema)
client.create_index(
    "documents",
    index_params=client.prepare_index_params(
        field_name="embedding",
        index_type="IVF_FLAT",
        metric_type="L2",
    ),
)
client.load_collection("documents")

# Search for the 5 nearest vectors to a query embedding
query = [[0.1] * 768]
results = client.search(
    "documents",
    data=query,
    anns_field="embedding",
    limit=5,
)
print(results)

Backups

Milvus instances are backed up automatically. Backups are encrypted and follow the standard StackBlaze backup policy, including schedule and retention. For details on how backups run, how they are encrypted, and how to restore, see the /docs/databases/backups guide.