Full RAG Pipeline: 4 Vector Stores, Hybrid Search, and Reranking in One Template

We Added Full RAG to Our Open-Source AI Template: 4 Vector Stores, Hybrid Search, and Reranking One template, every RAG decision already made — from vector store to reranking strategy. You know the drill. You want to add RAG to your AI app. So you start: pick a vector database, write an embedding pipeline, figure out chunking, wire up retrieval, add it to your agent as a tool, build a frontend to manage documents... Three weeks later you have a working prototype. Then someone asks "can we try Qdrant instead of Milvus?" and you realize your vector store is hardcoded in 14 places. We just shipped v0.2.2 of our open-source full-stack AI template, and RAG was the biggest addition. Not a toy demo — a production pipeline with 4 vector stores, 4 embedding providers, hybrid search, reranking, document versioning, and a management dashboard. All configurable. All swappable. Here's what we built and why. I'm Kacper, AI Engineer at Vstorm — an Applied Agentic AI Engineering Consultancy. We've shi