vector search

Hands-on guide for Linux users to build a Bash-first RAG workflow that grounds LLM answers in your own runbooks, logs, and code while keeping data local and auditable; explains why RAG matters, shows four real case studies, and ships a minimal stack (FAISS, Sentence-Transformers, Ollama) with apt/dnf/zypper installs, ingest/query scripts, best practices, and systemd timers for automated indexing.

A practical, Linux-first guide to stopping LLM hallucinations with Retrieval-Augmented Generation: prepare and chunk docs, embed with sentence-transformers into Chroma, retrieve wide then rerank with a cross-encoder, prompt defensively with citations and token budgets, and evaluate with ragas. Includes apt/dnf/zypper setup, end-to-end scripts, local LLM options, and tips for reproducibility, privacy, and automation.

Bring meaning to grep: this guide adds private, offline semantic search to Linux using a single-file Python CLI and SQLite. It indexes notes/code into 20-line chunks, creates fastembed vectors, and answers fuzzy queries with cosine similarity and TSV results you can pipe to fzf/$EDITOR. Includes distro-specific installs, indexing/query usage, cron updates, multiple DBs, model swaps, and scaling tips.

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.