rag

Practical, shell-first guide to fixing flaky RAG pipelines: use curl, jq, ripgrep, sqlite3 FTS, strace, and GNU parallel to verify corpus coverage, prove retriever outputs, isolate latency, and validate prompt/context assembly; then guard quality with a tiny recall@k harness and gold queries in CI. Covers common pitfalls (stale indexes, embedding mismatch, bad chunking, filters, truncation) and reproducible bash smoke tests.

Turn RAG from guesswork to observability with drop-in Bash: define the right metrics (retrieval overlap/groundedness, latency p50/p95, error and cache rates, token cost, freshness), log every turn as JSONL, compute rolling stats with rag-metrics.sh, export Prometheus textfile gauges, trigger Slack/email alerts via rag-alert.sh, and gnuplot quick charts—catch drift, cut latency, and rein in spend.

CLI-first guide to optimizing RAG for accuracy, latency, and cost: choose and tune the right index (FAISS/Chroma/Qdrant/Milvus; IVF/HNSW), chunk smarter (256–512 tokens, light overlap), speed embeddings with batching/threads/GPU/caching, improve recall with hybrid BM25+dense and light reranking, and measure recall@k, p95 latency, and cost via FastAPI+ab and ragas—complete with multi-distro installs and reproducible scripts.

Learn to build a fully local, private RAG pipeline on Linux using only Bash, curl, jq, and Ollama: install prerequisites, index your docs by chunking and embedding with nomic-embed-text, retrieve top chunks via cosine similarity in jq, and query a local LLM (e.g., llama3.1). Includes setup commands, example scripts, tips, and use cases—no Python, no cloud.

Practical, Bash-friendly guide to chunking for RAG: why coherent, token-based chunks boost retrieval precision/recall, cut tokens, and reduce latency. Includes Linux setup and scripts for four strategies—fixed tokens with overlap, sentence-aware packing, Markdown heading-aware splits, and semantic splits—plus a sample pipeline, evaluation tips, and a default starting recipe.

A hands-on guide to building a lightweight, privacy-first Retrieval-Augmented Generation pipeline on Linux using Bash, Python, Chroma, and sentence-transformers. Ingest and chunk docs (incl. PDFs), embed and index locally, retrieve top-k chunks, then answer with a local LLM via Ollama or a hosted API. Includes distro-specific setup, runnable ingest/query scripts, real-world use cases, tweaks, and troubleshooting to turn your docs into a private Q&A.

Practical, Linux-first guide to Retrieval-Augmented Generation (“grep + summarize”): explains benefits (verifiable sources, private, Unix-composable) and walks you through a minimal Bash pipeline—index docs/logs with FAISS + sentence-transformers, query via Python, and generate with llama.cpp or an API—plus distro install commands, copy-paste scripts, real-world use cases, tuning tips, and troubleshooting.

Build a fast, private RAG assistant on Linux using Ollama (local LLM), Chroma (vector store), and Sentence-Transformers (embeddings). The guide shows installing deps, indexing folders of txt/md/pdf with a minimal Python script, chunking and embedding, then querying from the shell with grounded, cited answers. It covers tuning, automation, GPU tips, and swapping models or stores.

Build a private, Linux-native RAG system that turns your local docs into a searchable AI: ingest txt/md/pdf, chunk, embed with fastembed (BAAI/bge-small-en), index in FAISS, and query via a local Ollama LLM with cited sources. The guide covers apt/dnf/zypper setup, two minimal Python scripts, examples, tuning tips, cron automation, scaling to vector DBs, and troubleshooting.