FAISS, Annoy, Milvus, and Weaviate all support tuning of index parameters, but their approaches and supported parameters differ. FAISS allows direct control over HNSW parameters like M (the number of bidirectional links per node) and other algorithm-specific settings, such as the number of clusters in IVF methods or quantization bits in PQ. Annoy exposes its tree count parameter, which determines the number of index trees built. Milvus and Weaviate abstract these parameters through their configuration interfaces—Milvus lets users adjust HNSW's M/efConstruction, Annoy's tree count, and other index-specific values, while Weaviate focuses on HNSW parameters like maxConnections (equivalent to M) and ef.
For example, in FAISS, increasing HNSW's M improves recall by creating more connections between nodes, but it increases memory usage and indexing time. Annoy's tree count directly impacts accuracy: more trees reduce the chance of missing neighbors but require more memory and slower searches. Milvus and Weaviate simplify parameter tuning through their APIs but retain flexibility. In Milvus, choosing between HNSW and Annoy involves trade-offs: HNSW may prioritize speed and accuracy at higher memory costs, while Annoy offers lighter memory usage with configurable tree-based precision. Weaviate’s HNSW settings let users balance between real-time query latency and index construction overhead.
This flexibility impacts performance tuning by allowing developers to optimize for their specific constraints. For instance, a high M in HNSW-based systems (FAISS, Milvus, Weaviate) improves accuracy for recommendation systems but may be impractical for edge devices with limited RAM. Lowering Annoy's tree count in Milvus reduces memory usage for large datasets but risks lower recall. Tuning requires benchmarking: parameters like efConstruction in HNSW affect index build time, while ef at query time controls search depth. Over-optimizing one metric (e.g., query speed) might degrade another (e.g., indexing throughput), so parameter adjustments must align with the application's priorities, such as real-time responsiveness versus batch processing efficiency.