Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed
and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high
recall—ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for
scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW’s accuracy,
trading off recall for memory efficiency. BT (A) isn’t a 23ai vector index; it’s a generic term unrelated
here. IFS (B) seems a typo for IVF; no such index exists. HNSW’s graph structure outperforms IVF in
small-to-medium datasets or where precision matters, as Oracle’s documentation and benchmarks
highlight, making it a go-to for balanced performance.
Reference:Oracle Database 23ai AI Vector Search Guide, Section on HNSW Indexing.
