AgentDB Performance Optimization

To accelerate similarity search in AgentDB, enable automatic HNSW indexing and adjust parameters like hnswM, hnswEfConstruction, and hnswEfSearch. Tuning these values optimizes approximate nearest neighbor search for lower latency.

The best way to improve batch ingestion throughput in AgentDB is by implementing caching strategies and batch operations. Tuning the cacheSize parameter further enhances performance for bulk inserts and repeated queries.

Yes, you can optimize AgentDB for large-scale deployments with millions of vectors. It combines quantization for memory reduction, automatic HNSW indexing for fast search, and batch operations to handle scaling efficiently.

Available quantization types for AgentDB performance optimization include binary, scalar, product, and none. Choosing the right quantizationType allows you to balance memory reduction against accuracy loss based on your use case.

To find the right performance settings, run comprehensive AgentDB benchmarks to evaluate different configurations. Testing various HNSW parameters and quantization types reveals the optimal setup for your specific workload.