# qmd — Mini CLI Search Engine for Your Docs and Knowledge Bases

> A fast local-first CLI search engine that indexes your documents, meeting notes, and knowledge bases for instant retrieval, tracking state-of-the-art approaches while keeping everything on your machine.

## Install

Save as a script file and run:

# qmd — Mini CLI Search Engine for Your Docs and Knowledge Bases

## Quick Use
```bash
npm install -g qmd
qmd index ~/docs
qmd search "deployment checklist"
```

## Introduction
qmd is a lightweight CLI search engine that indexes your local documents, meeting notes, knowledge bases, and other text files. It tracks current state-of-the-art retrieval approaches while being entirely local — no data leaves your machine.

## What qmd Does
- Indexes local directories of Markdown, text, PDF, and other document formats
- Provides fast full-text and semantic search from the command line
- Maintains an incremental index that updates as files change
- Returns ranked results with contextual snippets
- Works entirely offline with no cloud dependencies

## Architecture Overview
qmd is a TypeScript application that builds a local search index over your documents. It combines traditional full-text indexing with modern embedding-based semantic search for high-quality retrieval. The index is stored locally and updated incrementally when files change. Search queries run against both indexes and results are merged using a hybrid ranking strategy. The CLI outputs results with highlighted snippets and relevance scores.

## Self-Hosting & Configuration
- Install globally via npm or download a standalone binary
- Index any directory with `qmd index <path>`
- Configure which file types to include/exclude in `.qmdrc`
- Set the embedding model for semantic search (local or API-based)
- Index auto-updates on subsequent runs — only changed files are reprocessed

## Key Features
- Hybrid search combining full-text and semantic retrieval
- Fully local — no data sent to external services
- Incremental indexing for fast updates on large document collections
- Supports Markdown, plain text, PDF, and common document formats
- Minimal setup with sensible defaults

## Comparison with Similar Tools
- **grep/ripgrep** — pattern matching on raw text; qmd provides ranked semantic search
- **Elasticsearch** — enterprise search server; qmd is a lightweight local CLI tool
- **Obsidian Search** — built into the Obsidian app; qmd works across any directory
- **RAGFlow** — full RAG pipeline with LLM; qmd focuses on retrieval, not generation

## FAQ
**Q: How large a document collection can qmd handle?**
A: It is designed for personal and team-scale collections (thousands to tens of thousands of documents). It is not built for enterprise-scale corpora.

**Q: Does it require a GPU for semantic search?**
A: No. It can use small local embedding models that run on CPU. Optionally, you can configure an API-based embedder for higher quality.

**Q: Can I use it with AI coding agents?**
A: Yes. The JSON output mode makes it easy for agents to query your docs programmatically.

**Q: Is it open source?**
A: Yes. qmd is fully open source and available on GitHub.

## Sources
- https://github.com/tobi/qmd


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Source: https://tokrepo.com/en/workflows/asset-f223049c
Author: Script Depot