Deploying RAG Without the Plumbing: MariaDB’s Unified Approach

Author: MariaDB Foundation - Bewertung: 1x - Views:1

AI + RAG with MariaDB (Without the Plumbing) | Demo of MariaDB AI RAG



In this session, Alejandro Duarte (Developer Relations Engineer at MariaDB plc) explains how to implement Retrieval Augmented Generation (RAG) with MariaDB — and how to do it without building and maintaining complex infrastructure yourself.



Large language models often hallucinate when they lack context. RAG solves this by retrieving relevant information from your data sources and injecting it into the prompt sent to the model. The result is more accurate, context-aware AI responses.



In this video, Alejandro walks through:



• What Retrieval Augmented Generation (RAG) is and why it matters

• Why LLMs hallucinate and how contextual retrieval fixes the problem

• The two main phases of RAG: ETL (embedding generation) and retrieval + generation

• How vector embeddings and semantic search work in MariaDB

• Real challenges when building RAG pipelines in production

• Document ingestion, chunking strategies, orchestration, and security concerns

• How MariaDB AI RAG simplifies the entire architecture



You will also see a live demo showing how MariaDB AI RAG can:



• Ingest documents (like PDFs)

• Automatically chunk and embed them

• Store vectors in MariaDB

• Perform hybrid search (vector + keyword)

• Generate answers using an LLM



All of this happens through simple REST APIs, allowing developers to build AI-powered applications without writing large amounts of infrastructure code.



The demo even shows how an LLM that initially hallucinated an answer can correctly retrieve the information once RAG is implemented.