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 s, Hybrid Search, and Reranking One template, every RAG decision already made - from vector 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 is hardcoded in 14 places.