This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. Vector Database Tuning: Index Parameters, Search Configuration, and Hybrid Search Vector databases are the backbone of modern RAG applications. But default configurations rarely give optimal results. Tuning your vector database for your specific data distribution and query patterns can improve recall by 20% or more while reducing latency. Here is the tuning guide. Understanding Index Types Vector databases support multiple index types, each with different trade-offs between search speed, memory usage, and recall accuracy. HNSW Hierarchical Navigable Small World is the default choice for most applications. It offers excellent search speed and recall. The trade-off is higher memory usage and slower index building. HNSW is ideal when your dataset fits in memory and query speed is critical. IVF Inverted File Index is more memory-efficient than HNSW.…