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oci
AI Infrastructure
intermediate
Semantic search and similarity matching

Vector Database System

AI Infrastructure

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.

Data Flow

Vector API
Query Engine
Index Manager
Vector Index (HNSW)
Metadata Store
Vector Snapshots
Query Cache

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Service Breakdown (7 services)

Other7 services
Vector API
  • •Routes API traffic and enforces policies
  • •Manages authentication and rate limiting
  • •Provides a unified API endpoint
Query Engine
  • •Orchestrates containerized workloads at scale
  • •Auto-scales pods and underlying nodes
  • •Supports rolling updates and rollbacks
Index Manager
  • •Orchestrates containerized workloads at scale
  • •Auto-scales pods and underlying nodes
  • •Supports rolling updates and rollbacks
Vector Index (HNSW)
  • •Self-tuning database with automatic scaling
  • •Handles patching and backups autonomously
  • •Optimizes queries with ML-driven indexing
Metadata Store
  • •Handles flexible schema data at scale
  • •Provides low-latency reads and writes
  • •Scales horizontally with partitioning
Vector Snapshots
  • •Stores unstructured data with high durability
  • •Supports lifecycle rules for cost management
  • •Serves as a data lake foundation
Query Cache
  • •Caches frequently accessed data in-memory
  • •Reduces database round-trips and latency
  • •Supports TTL-based expiration policies

Scaling Strategy

OKE hosts the distributed vector indexing engine that scales horizontally by adding worker pods with GPU shapes. Each tenant's vectors are partitioned into separate namespaces for isolation. Write-heavy workloads use bulk indexing through OCI Queue-buffered batches. OCI Cache stores hot query results and embedding caches. Object Storage stores raw vectors and index snapshots for disaster recovery. Functions handles dimension reduction and re-ranking for complex queries.

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