AgentDB Performance Optimization

Reduce vector database memory usage by applying quantization techniques like binary, scalar, or product quantization. These methods decrease memory footprint by 4-32x while maintaining controllable accuracy for nearest-neighbor searches.

Accelerate nearest-neighbor search on large vector datasets by configuring HNSW indexing parameters. Tunable HNSW indexing significantly speeds up query times compared to flat searches across millions of vectors.

AgentDB supports batch processing for bulk vector inserts and retrievals. Batch operations improve ingestion throughput significantly, making it suitable for scaling deployments with large-scale datasets.

In-memory caching patterns reduce repeated computation and IO overhead during vector retrieval. Data caching minimizes query latency for frequent searches by keeping active vectors readily accessible.

Use product quantization for higher memory reduction ratios, while scalar quantization offers a balance between memory savings and accuracy. Choosing the right quantization type depends on your specific memory constraints and accuracy requirements.

HNSW indexing accelerates search but requires additional memory for graph structures, challenging memory-constrained environments. Combine HNSW with quantization to mitigate memory overhead while retaining faster query speeds.