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Hello everyone,
I have a question about the recommended pattern for using Milvus as an online store in a multi-team environment.
The feature_store.yaml configuration for Milvus defines global configs, like:
embedding_dim
index_type
metric_type
Our use case involves multiple teams (e.g., marketing, sales) sharing the feature store, but their vector embeddings have different requirements. For example:
The Marketing team might use a 768-dimension vector with COSINE similarity.
The Sales team might use a 128-dimension vector with L2 similarity.
Since these configurations seem to be set at the online_store level, it implies we can only support one type of vector across the entire store. This would prevent us from sharing one feature server across teams with different vector models.
What is the best way to support multiple, diverse vector feature views (with different dimensions, metrics, etc.) in a single Milvus online store?
Is the intended pattern to create a separate online_store definition for each unique vector configuration, or am I misunderstanding how these parameters should be used?
Thanks for any insights!
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Hello everyone,
I have a question about the recommended pattern for using Milvus as an online store in a multi-team environment.
The feature_store.yaml configuration for Milvus defines global configs, like:
embedding_dim
index_type
metric_type
Our use case involves multiple teams (e.g., marketing, sales) sharing the feature store, but their vector embeddings have different requirements. For example:
The Marketing team might use a 768-dimension vector with COSINE similarity.
The Sales team might use a 128-dimension vector with L2 similarity.
Since these configurations seem to be set at the online_store level, it implies we can only support one type of vector across the entire store. This would prevent us from sharing one feature server across teams with different vector models.
What is the best way to support multiple, diverse vector feature views (with different dimensions, metrics, etc.) in a single Milvus online store?
Is the intended pattern to create a separate online_store definition for each unique vector configuration, or am I misunderstanding how these parameters should be used?
Thanks for any insights!
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.