OpenAI Pattern
Retrieval-Augmented Generation (RAG) combines the power of large language models with your own data. This OCI-native architecture ingests documents into Autonomous Database's built-in vector store, generates embeddings at query time via OCI Generative AI, retrieves the most relevant context via similarity search, and feeds it to an LLM for grounded, hallucination-reduced responses. Perfect for teams building enterprise AI assistants that need accurate, citation-backed answers from proprietary knowledge bases.
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The ingestion pipeline scales independently from the query path. OCI Queue Service buffers document uploads for batch embedding generation, while Functions handles bursty query traffic with automatic scaling. Autonomous Database provides built-in vector search that scales with OCPU auto-scaling, and NoSQL Database stores conversation history with on-demand capacity.
Vector Database System
AI Infrastructure
Purpose-built vector database on OCI with HNSW indexing, hybrid search, metadata filtering, and multi-tenant isolation using OKE and Autonomous Database.
Model Serving Platform
AI Infrastructure
Multi-model serving platform on OCI with canary deployments via OKE, A/B testing, OCI Cache feature store, and automatic model rollback.
Batch Inference Pipeline
AI Infrastructure
Large-scale batch prediction pipeline on OCI for processing millions of records through ML models with Data Science and OKE.
Content Moderation AI Pipeline
AI Infrastructure
Automated content moderation on Azure with Azure AI Content Safety, multi-modal analysis, human review queues, and policy enforcement.
Multi-Agent AI System
AI Infrastructure
Orchestrated multi-agent system where specialized AI agents collaborate on complex tasks with shared memory and tool use.
LLM Inference Pipeline
AI Infrastructure
Production LLM serving on Azure with AKS GPU workers, request batching, KV cache management, and streaming token delivery.
RAG AI Knowledge Base
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