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
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
RAG AI Knowledge Base
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