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.
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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.
RAG AI Knowledge Base
OpenAI Pattern
Model Serving Platform
AI Infrastructure
Batch Inference Pipeline
AI Infrastructure
Content Moderation AI Pipeline
AI Infrastructure
Multi-Agent AI System
AI Infrastructure
LLM Inference Pipeline
AI Infrastructure
Vector Database System
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