AgentDB Performance Optimization

Reduce AgentDB vector search memory usage by applying quantization strategies like binary, scalar, or product quantization. This shrinks memory footprint by 4 to 32 times while preserving usable accuracy for large-scale embedding storage.

Speed up similarity search over millions of vectors by configuring HNSW indexing parameters in your AgentDB adapter. HNSW transitions retrieval from slow linear scans to near-logarithmic search, achieving microsecond to millisecond latency.

Improve vector database throughput by using batch insert and retrieval workflows alongside in-memory caching. Configuring the AgentDB adapter with an appropriate cacheSize enables high-throughput batch operations for large numbers of embeddings.

Configuring HNSW indexing in AgentDB requires setting specific HNSW parameters and a supported quantizationType within your adapter. These settings optimize the balance between search latency, throughput, and storage size for your workload.

Use product quantization over scalar quantization when you need maximum memory reduction, up to 32 times, for massive embedding datasets. Scalar quantization offers a balance with higher accuracy retention, while binary provides the highest compression.

Vector retrieval slows down as pattern count grows because the database relies on linear scans. Moving to HNSW indexing and enabling caching resolves this by enabling near-logarithmic search paths and retaining frequent queries in memory.