Fast-Track Knowledge Bases: How to Build Semantic AI Search

Cut through the jargon and ship semantic search in minutes — not weeks. In this developer-first session, recorded live with Andriy Burkov and Alejandro Cantu, you’ll see how MindsDB Knowledge Bases connects your private data to powerful language models through one SQL command, no extra infrastructure required.

We’ll point a Knowledge Base at a real dataset, watch it auto-handle chunking, embeddings, hybrid retrieval, and reranking, then fire off natural-language questions that come back with verifiable citations. Everything runs inside your existing database workflows — no separate vector store, orchestration scripts, or machine-learning guesswork.

What you’ll learn

• Spin up a Knowledge Base from the IMDb dataset in one Jupyter notebook cell.
• Peek under the hood — chunking, embeddings, retrieval — via live diagrams.
• Ask natural-language questions and inspect scores, citations, and traces.
• Clone the notebook, swap in your data, and ship semantic search — no vector DB or MLOps setup required.