hybrid search

RAG is the safer, cheaper path to grounded LLM answers, and this Linux-first guide shows how to run it locally today: install a private stack (fastembed + FAISS + Ollama), build/query indexes with Python scripts, and improve retrieval via smarter chunking, hybrid search, and reranking. Add a file watcher for fresh indexes, simple evals for trust, and pick lightweight models—setting you up for structured, multi-hop, observable RAG.

Plain-English guide for Bash-first users to hybrid search that fuses lexical precision (ripgrep) with semantic recall (embeddings) so you catch both "OOM" and "out of memory." Provides a minimal local setup: install deps, index files with embed.py, query with search.py, and fuse scores via awk/join (weighted sum or two-stage/RRF). Includes CLI-focused examples (logs, code, docs), tuning tips (alpha, chunking), and troubleshooting.