Retrieval-Augmented Generation pipeline with vector search, embedding generation, and LLM orchestration for enterprise AI apps.
Difficulty: intermediate
Tags: ai, rag, vector-search, llm, embeddings, oci
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.