Having rolled my own RAG the other week, I personally would recommend talking through it with Claude Opus or a similar model.

My baseline was (vibe coded) full-text search with SQLite, and we landed on long chunks with overlap, with a really simple nearest-neighbor search: quantize the index to a sign bit per scalar, which makes it super cheap to estimate dot products, then calculate better (8-bit quant index times native precision for the query) dot products to sort the top documents. A vector database would make sense for a much larger corpus, but I currently have fewer than two million rows. Claude vibe-coded it to use an OpenAI-speaking local inference server and made semantic search an optional augmentation for the full-text search.