Milvus is a vector database that stores, indexes, and manages massive embedding vectors
that are developed by deep neural networks and other machine learning (ML) models. It is developed
to empower embedding similarity search and AI applications. Milvus makes unstructured data search
more accessible and consistent across various environments.
watsonx.data on Red Hat® OpenShift®
watsonx.data Developer
edition
watsonx.data SaaS on
AWS
About this task
You can add Milvus as a service in IBM®
watsonx.data through web console by using the
following steps.
Procedure
- Log in to watsonx.data
console.
- From the navigation menu, select Infrastructure
Manager.
- To define and connect to a service, click Add component and select
Add service.
- In the Add service window, select Milvus
from the Type list.
| Field |
Description |
| Display name |
Enter the Milvus service name to be displayed on the screen. |
| Size |
Select the suitable size.
- Starter: Recommended for 1 million vectors, 64 index parameters, 1024 segment
size, and 384 dimensions.
- Small: Recommended for 10 million vectors, 64 index parameters, 1024 segment size,
and 384 dimensions.
- Medium: Recommended for 50 million vectors, 64 index parameters, 1024 segment
size, and 384 dimensions.
- Large: Recommended for 100 million vectors, 64 index parameters, 1024 segment
size, and 384 dimensions.
|
| Add storage bucket |
For Small, Medium, and Large sizes, you must associate an external
bucket. For Starter size, you can use an IBM-managed bucket or an external bucket. Note: To
associate an external bucket, you must have the bucket configured.
|
| Path |
For external buckets, specify the path where you want to store vectorized data files. |
Important: You must provide the endpoint for buckets used by Milvus
with the region for region-specific buckets like S3 and without trailing slashes. For
example:
https://s3.<REGION>.amazonaws.com
For more information about adding external buckets, see Adding a storage-catalog pair.
- Click Provision.