The latest release of Aerospike Vector Search features a self-healing hierarchical navigable small world (HNSW) index, an approach that enables scale-out data ingestion by allowing data to be ingested while asynchronously building the index across devices. By scaling ingestion and index growth independently from query processing, the system ensures uninterrupted performance, accurate results, and optimal query speed for real-time decision-making, Aerospike said.
The new release also introduces a new Python client and sample apps for common vector use cases to speed deployment. The Aerospike data model allows developers to add vectors to existing records, eliminating the need for separate search systems, while Aerospike Vector Search makes it easy to integrate semantic search into existing AI applications through integration with popular frameworks and popular cloud partners, Aerospike said. Aerospike’s LangChain extension helps speed the development of RAG (retrieval-augmented generation) applications.
Aerospike’s multi-model database engine includes document, key-value, graph, and vector search within one system. Aerospike graph and vector databases work independently and jointly to support AI use cases such as RAG, semantic search recommendations, fraud prevention, and ad targeting, Aerospike said. The Aerospike database is available on major public clouds.