The real headache in RAG isn’t retrieval or generation — it’s the layer in between. Where does the data live? How do you keep it in sync? Who glues it all together? seekdb and Dify are both open-source. Your RAG stack — from storage to orchestration — can be self-hosted, auditable, and customizable, without locking you into closed services. This post walks through three paths, all built on one stack: RAG from scratch with seekdb, Dify + seekdb, and a knowledge base desktop app. Pick the one that fits and get it running. Where seekdb Fits in the RAG Pipeline A typical RAG pipeline looks like: load documents → chunk → embed → store; at query time: retrieve → (optionally) rerank → feed to LLM → generate. If your storage is a patchwork of MySQL + vector DB + full-text engine, you end up managing sync, multi-source queries, and fusion yourself. seekdb’s role: one database that holds relational data, vectors, and full-text in the same place. Write once, index automatically; one hybrid query returns results.…