Vector Search Performance / Conclusion
Vector Search Performance
Excellent work! You learned how to:
- Manage index size to ensure low-latency retrieval
- Leverage Search Nodes to improve performance for vector search workloads
- Optimize vector search performance
Learning Objectives
Manage index size to ensure low-latency retrieval: Explain the importance of ensuring vector search indexes fit into available memory (RAM) for low-latency retrieval and implement strategies to manage index size to meet memory constraints in production environments.
Leverage Search Nodes to improve performance for vector search workloads: Select the optimal deployment approach for your vector search workload based on your performance requirements. Learn to compare a search node architecture versus a coupled architecture where your operational and search workloads are co-located on the same nodes as your core database nodes.
Optimize vector search performance: Apply quantization and partial indexing with views to reduce index memory requirements and keep vector search performant as your data grows.
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Resources
Use the following resources to learn more about Vector Search Performance:
Docs: How to Perform Automatic Quantization with Voyage AI Embeddings
Docs: Build a Multi-Tenant Architecture for MongoDB Vector Search
To learn about the next steps, use these resources: