open-source vector databases:
Monitor memory usage:
Ensure your vector indexes fit within available memory. If you use PostgreSQL with the pgvector extension you can ensure this by setting the appropriate maintenance_work_mem.
Vector data can grow large, and exceeding available memory during indexing can drastically increase build times.
Understand your indexing
algorithms:
Use specialized vector indexes like HNSW (Hierarchical Navigable Small Worlds) or IVFFlat (Inverted File with Flat Compression) for fast approximate nearest neighbor (ANN) search. HNSW is ideal for most use cases. It features high query performance and its indexing structure adapts to dataset evolution because it is based on graphs, while IVFFlat is better for memory efficiency and lower build times.
Incorporate vector
quantization: Utilize scalar quantization to reduce 4-byte floats to 2-byte floats, and binary quantization to reduce the dimensions to a single bit. This dramatically cuts storage costs, especially for large datasets with high-dimensional vectors.
Monitor vector database performance:
Implement monitoring and logging tools to track the performance of your vector database, particularly during high-load periods. This can help in identifying bottlenecks and optimizing query strategies in real-time.
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