AgentDB Performance Optimization

Speed up vector search and reduce memory usage by configuring quantization, HNSW indexing, caching, and batch operations. These optimizations shrink the embedding footprint and achieve logarithmic-time similarity search with controlled accuracy trade-offs.

Tune HNSW parameters by adjusting M, efConstruction, and efSearch to achieve logarithmic-time similarity search. Properly sizing these values increases query throughput and recall rates while maintaining efficient indexing.

Quantization reduces vector search storage by configuring binary, scalar, or product quantization types. This shrinks the embedding footprint with controlled accuracy trade-offs, lowering memory requirements while maintaining acceptable search performance.

Use batch inserts and in-memory caching to improve vector database throughput. Batch operations amortize write costs and reduce database overhead, while caching minimizes query latency for frequently accessed vectors.

Benchmark your dataset before applying vector search optimizations to measure baseline performance. Running a benchmark validates the impact of quantization, HNSW, caching, and batch operations on your specific data.

Vector search queries slow down when scaling to millions of vectors due to insufficient indexing and memory overhead. Apply scaling recipes with appropriate HNSW parameters and quantization to maintain low-latency retrieval and efficient scaling.