Purpose-built vector database on OCI with HNSW indexing, hybrid search, metadata filtering, and multi-tenant isolation using OKE and Autonomous Database.
Difficulty: intermediate
Tags: ai, vector-db, embeddings, similarity-search, oci
Vector databases power semantic search, recommendation systems, and RAG applications by finding the most similar items in high-dimensional embedding space. This OCI-native architecture implements HNSW (Hierarchical Navigable Small World) indexing via OKE-hosted vector engine, supports hybrid queries combining vector similarity with metadata filters via Autonomous Database, and provides multi-tenant isolation for SaaS use cases. Essential for teams building semantic search, recommendation engines, or RAG applications that require sub-millisecond similarity queries.