vector database

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.

Practical, Linux-first guide to trustworthy vector database benchmarking: bash-friendly apt/dnf/zypper setup, a reproducible harness to generate data or plug in embeddings, and measurements of recall@k, p50/p95 latency and QPS for FAISS (in-process) and Qdrant (via Podman). Match recall before comparing speed, pin CPUs, test filters/concurrency, track cost, and extend the script to other engines.

Learn to build a private, terminal-first RAG assistant on Linux that answers questions from your own PDFs, notes, and wikis without the cloud. The guide installs prerequisites and Ollama, ingests docs to text via Bash, embeds with sentence-transformers, indexes with FAISS, and queries a local LLM. It includes scripts (ingest.sh, build_index.py, ask.py), tuning tips, real-world uses, and troubleshooting.

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.

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.

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.