qmd - Quick Markdown Search

Local search engine for Markdown notes, docs, and knowledge bases. Index once, search fast.

When to use (trigger phrases)

•"search my notes / docs / knowledge base"
•"find related notes"
•"retrieve a markdown document from my collection"
•"search local markdown files"

Default behavior (important)

•Prefer qmd search (BM25). It's typically instant and should be the default.
•Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start).
•Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.

Prerequisites

•Bun >= 1.0.0
•macOS: brew install sqlite (SQLite extensions)
•Ensure PATH includes: $HOME/.bun/bin

Install Bun (macOS): brew install oven-sh/bun/bun

Install

bun install -g https://github.com/tobi/qmd

Setup

bash

qmd collection add /path/to/notes --name notes --mask "**/*.md"
qmd context add qmd://notes "Description of this collection"  # optional
qmd embed  # one-time to enable vector + hybrid search

What it indexes

•Intended for Markdown collections (commonly **/*.md).
•In our testing, "messy" Markdown is fine: chunking is content-based (roughly a few hundred tokens per chunk), not strict heading/structure based.
•Not a replacement for code search; use code search tools for repositories/source trees.

Search modes

•qmd search (default): fast keyword match (BM25)
•qmd vsearch (last resort): semantic similarity (vector). Often slow due to local LLM work before the vector lookup.
•qmd query (generally skip): hybrid search + LLM reranking. Often slower than vsearch and may timeout.

Performance notes

•qmd search is typically instant.
•qmd vsearch can be ~1 minute on some machines because query expansion may load a local model (e.g., Qwen3-1.7B) into memory per run; the vector lookup itself is usually fast.
•qmd query adds LLM reranking on top of vsearch, so it can be even slower and less reliable for interactive use.
•If you need repeated semantic searches, consider keeping the process/model warm (e.g., a long-lived qmd/MCP server mode if available in your setup) rather than invoking a cold-start LLM each time.

Common commands

bash

qmd search "query"             # default
qmd vsearch "query"
qmd query "query"
qmd search "query" -c notes     # Search specific collection
qmd search "query" -n 10        # More results
qmd search "query" --json       # JSON output
qmd search "query" --all --files --min-score 0.3

Useful options

•-n <num>: number of results
•-c, --collection <name>: restrict to a collection
•--all --min-score <num>: return all matches above a threshold
•--json / --files: agent-friendly output formats
•--full: return full document content

Retrieve

bash

qmd get "path/to/file.md"       # Full document
qmd get "#docid"                # By ID from search results
qmd multi-get "journals/2025-05*.md"
qmd multi-get "doc1.md, doc2.md, #abc123" --json

Maintenance

bash

qmd status                      # Index health
qmd update                      # Re-index changed files
qmd embed                       # Update embeddings

Keeping the index fresh

Set up a cron job or hook to automatically re-index. For example, a daily 5 AM reindex:

bash

# Via Clawdbot cron (isolated job, runs silently):
clawdbot cron add \
  --name "qmd-reindex" \
  --cron "0 5 * * *" \
  --tz "America/New_York" \
  --session isolated \
  --message "Run: export PATH=\"\$HOME/.bun/bin:\$PATH\" && qmd update && qmd embed" 

# Or via system crontab:
0 5 * * * export PATH="$HOME/.bun/bin:$PATH" && qmd update && qmd embed

This ensures your vault search stays current as you add or edit notes.

Models and cache

•Uses local GGUF models; first run auto-downloads them.
•Default cache: ~/.cache/qmd/models/ (override with XDG_CACHE_HOME).

Relationship to Clawdbot memory search

•qmd searches your local files (notes/docs) that you explicitly index into collections.
•Clawdbot's memory_search searches agent memory (saved facts/context from prior interactions).
•Use both: memory_search for "what did we decide/learn before?", qmd for "what's in my notes/docs on disk?".