In-Memory Neighbor Graph Vector Index

Hierarchical Navigable Small World (HNSW) is the only type of In-Memory Neighbor Graph vector index supported. HNSW graphs are very efficient indexes for vector approximate similarity search. HNSW graphs are structured using principles from small world networks along with layered hierarchical organization.

• About In-Memory Neighbor Graph Vector Index
  The default type of index created for an In-Memory Neighbor Graph vector index is Hierarchical Navigable Small World.
• Local Hierarchical Navigable Small World Indexes
  A local HNSW index is an index created for each partition or sub partition of a partitioned table. Instead of building a single, global HNSW (Hierarchical Navigable Small World) graph across all vectors in a large, partitioned table, Oracle enables the creation of individual HNSW graphs for each (sub)partition. This local partitioned indexing approach improves scalability, performance, and manageability essential for enterprise workloads involving high-dimensional similarity search applications, such as semantic search, recommendation systems, and AI-driven analytics.
• Included Columns with HNSW Indexes
  Included columns in vector indexes facilitate faster searches with attribute filters by incorporating non-vector columns within an In-Memory Neighbor Graph Vector Index.
• Scalar Quantized HNSW Indexes
  Scalar quantized HNSW indexes can be used to reduce memory requirements and accelerate similarity search.
• Hierarchical Navigable Small World Index Syntax and Parameters
  Syntax and examples for creating Hierarchical Navigable Small World vector indexes.