Qmd

QMD — Query Markup Documents

Local, on-device search engine for personal knowledge bases. Indexes markdown notes, meeting transcripts, documentation, and any text-based files, then provides hybrid search combining keyword matching, semantic understanding, and LLM-powered reranking — all running locally with no cloud dependencies.

Created by Tobi Lütke. MIT licensed.

When to Use

• User asks to search their notes, docs, knowledge base, or meeting transcripts
• User wants to find something across a large collection of markdown/text files
• User wants semantic search ("find notes about X concept") not just keyword grep
• User has already set up qmd collections and wants to query them
• User asks to set up a local knowledge base or document search system
• Keywords: "search my notes", "find in my docs", "knowledge base", "qmd"

Prerequisites

Node.js >= 22 (required)

# Check version
node --version  # must be >= 22

# macOS — install or upgrade via Homebrew
brew install node@22

# Linux — use NodeSource or nvm
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs
# or with nvm:
nvm install 22 && nvm use 22

SQLite with Extension Support (macOS only)

macOS system SQLite lacks extension loading. Install via Homebrew:

brew install sqlite

Install qmd

npm install -g @tobilu/qmd
# or with Bun:
bun install -g @tobilu/qmd

First run auto-downloads 3 local GGUF models (~2GB total):

Verify Installation

qmd --version
qmd status

Quick Reference

Setup Workflow

1. Add Collections

Point qmd at directories containing your documents:

# Add a notes directory
qmd collection add ~/notes --name notes

# Add project docs
qmd collection add ~/projects/myproject/docs --name project-docs

# Add meeting transcripts
qmd collection add ~/meetings --name meetings

# List all collections
qmd collection list

2. Add Context Descriptions

Context metadata helps the search engine understand what each collection contains. This significantly improves retrieval quality:

qmd context add qmd://notes "Personal notes, ideas, and journal entries"
qmd context add qmd://project-docs "Technical documentation for the main project"
qmd context add qmd://meetings "Meeting transcripts and action items from team syncs"

3. Generate Embeddings

qmd embed

This processes all documents in all collections and generates vector embeddings. Re-run after adding new documents or collections.

4. Verify

qmd status   # shows index health, collection stats, model info

Search Patterns

Fast Keyword Search (BM25)

Best for: exact terms, code identifiers, names, known phrases. No models loaded — near-instant results.

qmd search "authentication middleware"
qmd search "handleError async"

Semantic Vector Search

Best for: natural language questions, conceptual queries. Loads embedding model (~3s first query).

qmd vsearch "how does the rate limiter handle burst traffic"
qmd vsearch "ideas for improving onboarding flow"

Hybrid Search with Reranking (Best Quality)

Best for: important queries where quality matters most. Uses all 3 models — query expansion, parallel BM25+vector, reranking.

qmd query "what decisions were made about the database migration"

Structured Multi-Mode Queries

Combine different search types in a single query for precision:

# BM25 for exact term + vector for concept
qmd query $'lex: rate limiter\nvec: how does throttling work under load'

# With query expansion
qmd query $'expand: database migration plan\nlex: "schema change"'

Query Syntax (lex/BM25 mode)

HyDE (Hypothetical Document Embeddings)

For complex topics, write what you expect the answer to look like:

qmd query $'hyde: The migration plan involves three phases. First, we add the new columns without dropping the old ones. Then we backfill data. Finally we cut over and remove legacy columns.'

Scoping to Collections

qmd search "query" --collection notes
qmd query "query" --collection project-docs

Output Formats

qmd search "query" --json        # JSON output (best for parsing)
qmd search "query" --limit 5     # Limit results
qmd get "#abc123"                # Get by document ID
qmd get "path/to/file.md"       # Get by file path
qmd get "file.md:50" -l 100     # Get specific line range
qmd multi-get "journals/*.md" --json  # Batch retrieve by glob

MCP Integration (Recommended)

qmd exposes an MCP server that provides search tools directly to Gauss Agent via the native MCP client. This is the preferred integration — once configured, the agent gets qmd tools automatically without needing to load this skill.

Option A: Stdio Mode (Simple)

Add to ~/.gauss/config.yaml:

mcp_servers:
  qmd:
    command: "qmd"
    args: ["mcp"]
    timeout: 30
    connect_timeout: 45

This registers tools: mcp_qmd_search, mcp_qmd_vsearch, mcp_qmd_deep_search, mcp_qmd_get, mcp_qmd_status.

Tradeoff: Models load on first search call (~19s cold start), then stay warm for the session. Acceptable for occasional use.

Option B: HTTP Daemon Mode (Fast, Recommended for Heavy Use)

Start the qmd daemon separately — it keeps models warm in memory:

# Start daemon (persists across agent restarts)
qmd mcp --http --daemon

# Runs on http://localhost:8181 by default

Then configure Gauss Agent to connect via HTTP:

mcp_servers:
  qmd:
    url: "http://localhost:8181/mcp"
    timeout: 30

Tradeoff: Uses 2GB RAM while running, but every query is fast (2-3s). Best for users who search frequently.

Keeping the Daemon Running

macOS (launchd)

cat > ~/Library/LaunchAgents/com.qmd.daemon.plist << 'EOF'
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
  <key>Label</key>
  <string>com.qmd.daemon</string>
  <key>ProgramArguments</key>
  <array>
    <string>qmd</string>
    <string>mcp</string>
    <string>--http</string>
    <string>--daemon</string>
  </array>
  <key>RunAtLoad</key>
  <true/>
  <key>KeepAlive</key>
  <true/>
  <key>StandardOutPath</key>
  <string>/tmp/qmd-daemon.log</string>
  <key>StandardErrorPath</key>
  <string>/tmp/qmd-daemon.log</string>
</dict>
</plist>
EOF

launchctl load ~/Library/LaunchAgents/com.qmd.daemon.plist

Linux (systemd user service)

mkdir -p ~/.config/systemd/user

cat > ~/.config/systemd/user/qmd-daemon.service << 'EOF'
[Unit]
Description=QMD MCP Daemon
After=network.target

[Service]
ExecStart=qmd mcp --http --daemon
Restart=on-failure
RestartSec=10
Environment=PATH=/usr/local/bin:/usr/bin:/bin

[Install]
WantedBy=default.target
EOF

systemctl --user daemon-reload
systemctl --user enable --now qmd-daemon
systemctl --user status qmd-daemon

MCP Tools Reference

Once connected, these tools are available as mcp_qmd_*:

The MCP tools accept structured JSON queries for multi-mode search:

{
  "searches": [
    {"type": "lex", "query": "authentication middleware"},
    {"type": "vec", "query": "how user login is verified"}
  ],
  "collections": ["project-docs"],
  "limit": 10
}

CLI Usage (Without MCP)

When MCP is not configured, use qmd directly via terminal:

terminal(command="qmd query 'what was decided about the API redesign' --json", timeout=30)

For setup and management tasks, always use terminal:

terminal(command="qmd collection add ~/Documents/notes --name notes")
terminal(command="qmd context add qmd://notes 'Personal research notes and ideas'")
terminal(command="qmd embed")
terminal(command="qmd status")

How the Search Pipeline Works

Understanding the internals helps choose the right search mode:

1. Query Expansion — A fine-tuned 1.7B model generates 2 alternative queries. The original gets 2x weight in fusion.
2. Parallel Retrieval — BM25 (SQLite FTS5) and vector search run simultaneously across all query variants.
3. RRF Fusion — Reciprocal Rank Fusion (k=60) merges results. Top-rank bonus: #1 gets +0.05, #2-3 get +0.02.
4. LLM Reranking — qwen3-reranker scores top 30 candidates (0.0-1.0).
5. Position-Aware Blending — Ranks 1-3: 75% retrieval / 25% reranker. Ranks 4-10: 60/40. Ranks 11+: 40/60 (trusts reranker more for long tail).

Smart Chunking: Documents are split at natural break points (headings, code blocks, blank lines) targeting ~900 tokens with 15% overlap. Code blocks are never split mid-block.

Best Practices

1. Always add context descriptions — qmd context add dramatically improves retrieval accuracy. Describe what each collection contains.
2. Re-embed after adding documents — qmd embed must be re-run when new files are added to collections.
3. Use qmd search for speed — when you need fast keyword lookup (code identifiers, exact names), BM25 is instant and needs no models.
4. Use qmd query for quality — when the question is conceptual or the user needs the best possible results, use hybrid search.
5. Prefer MCP integration — once configured, the agent gets native tools without needing to load this skill each time.
6. Daemon mode for frequent users — if the user searches their knowledge base regularly, recommend the HTTP daemon setup.
7. First query in structured search gets 2x weight — put the most important/certain query first when combining lex and vec.

Troubleshooting

"Models downloading on first run"

Normal — qmd auto-downloads ~2GB of GGUF models on first use. This is a one-time operation.

Cold start latency (~19s)

This happens when models aren't loaded in memory. Solutions:

• Use HTTP daemon mode (qmd mcp --http --daemon) to keep warm
• Use qmd search (BM25 only) when models aren't needed
• MCP stdio mode loads models on first search, stays warm for session

macOS: "unable to load extension"

Install Homebrew SQLite: brew install sqlite Then ensure it's on PATH before system SQLite.

"No collections found"

Run qmd collection add <path> --name <name> to add directories, then qmd embed to index them.

Embedding model override (CJK/multilingual)

Set QMD_EMBED_MODEL environment variable for non-English content:

export QMD_EMBED_MODEL="your-multilingual-model"

Data Storage

• Index & vectors: ~/.cache/qmd/index.sqlite
• Models: Auto-downloaded to local cache on first run
• No cloud dependencies — everything runs locally

References

• GitHub: tobi/qmd
• QMD Changelog