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Milvus for managing vector search at scale
Organize large embedding collections with configurable indexes and search modes.
Key Takeaways
- Milvus stores vectors and scalar fields in collections.
- Define the collection schema, vector index and write pattern together.
- Benchmark two index settings on the same data.
- Deployment complexity grows with distributed storage and indexing.
How it works
Milvus stores vectors and scalar fields in collections. Index and consistency choices affect query behavior while records are being ingested or updated.
What you need
Define the collection schema, vector index and write pattern together. A workload with frequent updates makes different demands from an offline index built once.
What to try first
Benchmark two index settings on the same data. Include query tests immediately after writes and compare recall with a small exact reference.
Check what readers see during a bulk import and after a restart. Consistency and index availability matter if the service accepts traffic while ingesting.
Where to look for failures
Deployment complexity grows with distributed storage and indexing. Size each component with representative data and measure rebuild time.
Related Franklin pages
Start with a labeled query set and save each engine's candidates. Compare filtered search and ranking behavior before extrapolating throughput from a query without metadata constraints.
- Combine vector, keyword and other search signals over the same document collection. Infinity for hybrid search in one engine
- Build search applications that combine text, vectors and ranking functions. Vespa for custom ranking and retrieval
For the role of the index in nearest-neighbor search, see Franklin’s glossary. Vector indexes
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
Read the project documentation ↗