Building RAG Entirely Inside Oracle Database 26ai — No Vector DB, No App Layer, Just PL/SQL
Building Retrieval-Augmented Generation (RAG) Entirely Inside Oracle Database — with PL/SQL How I grounded a local LLM (Ollama + Qwen2.5) in Oracle 26ai's native Vector Search — no external vector database, no separate application layer. Munish Kumar Karna Senior Database Solution Architect | Oracle DBA | Oracle RAC, ASM, Data Guard, GoldenGate, OCI Why This Matters Retrieval-Augmented Generation (RAG) is usually built with a dedicated vector database, an orchestration framework, and an application server sitting between the data and the LLM. With Oracle Database 26ai, that entire pipeline can live inside the database itself. In this walkthrough, I demonstrate a complete, self-contained RAG pipeline built purely in PL/SQL: importing an ONNX embedding model, running a local LLM through Ollama, chunking and embedding a real PDF manual, and using Oracle's native VECTOR data type and DBMS_VECTOR package to ground the model...