Replaced Embedding RAG with PageIndex on My Own Site — Here's What Actually Happened
After hitting a wall with hybrid BM25 + Embedding + Rerank search (wrong retrievals, high infra cost, Japanese embedding model limitations), I implemented **PageIndex** — a vector-free RAG architecture — as the AI assistant for my company site. The result: no VectorDB, no embedding model, no Reranker needed. Two-stage LLM-based filtering via a URL tree navigated to the correct pages for queries like *"What products do you have?"* and *"What ASR-related blog posts exist?"* — zero vector math involved.