enable-semantic-search

Enable Semantic Search

Set up local AI-powered semantic search for your vault. This is a concierge experience — it analyzes your vault, discovers what collections make sense, and creates a tailored search setup.

What You're Enabling

Semantic search finds content by meaning, not just keywords:

• Search "product-led growth" → finds notes saying "PLG", "self-serve motion", "freemium adoption"
• Search "customer churn" → finds notes about "retention problems", "users leaving", "cancellation patterns"

Your skills (/daily-plan, /meeting-prep, /triage, etc.) automatically use semantic search when available, finding more relevant context than keyword matching alone.

Pre-Flight Checks

Run these checks before proceeding:

# Check if qmd is already installed
which qmd

# Check if Bun is available (required)
which bun

# macOS only: Check SQLite via Homebrew
brew list sqlite 2>/dev/null && echo "SQLite OK" || echo "Need: brew install sqlite"

# Check available disk space (need ~2.5GB)
df -h ~ | tail -1

If qmd is already installed, run qmd status to check existing setup. If collections already exist, skip to the Collection Health Check section at the bottom.

Step 1: Explain What We're Installing

Present this to the user:

═══════════════════════════════════════════════════════════════════════
                    SEMANTIC SEARCH FOR YOUR VAULT
═══════════════════════════════════════════════════════════════════════

What is this?
─────────────
Right now, finding notes requires knowing the exact words used.
Search "product-led growth" — won't find notes saying "PLG" or
"self-serve motion".

Semantic search understands meaning, not just keywords. It finds
conceptually related content even when terminology differs.


How does it work?
─────────────────
Your notes get converted to "embeddings" — mathematical
representations of meaning. When you search, your query becomes
an embedding too. The system finds notes whose meaning is close
to your query's meaning.

Think of it like: instead of matching letters, we're matching ideas.


What gets installed?
────────────────────
Three small AI models run locally on your machine:

  MODEL                      PURPOSE                    SIZE
  ─────────────────────────────────────────────────────────────
  EmbeddingGemma-300M        Converts text to meaning   ~300MB
                             vectors. The core
                             "understanding" model.

  Qwen3-Reranker-0.6b       Re-orders results by       ~640MB
                             true relevance. Improves
                             result quality.

  QMD-Query-Expansion-1.7B   Expands your search to     ~1.1GB
                             include related terms.
                             "PLG" → also searches
                             "product-led", "freemium"

  Total: ~2GB one-time download


Privacy & Security
──────────────────
- Everything runs locally — your notes never leave your machine
- No API keys required
- No cloud services
- Models downloaded from HuggingFace (open source)
- Index stored in ~/.cache/qmd/ (not in your vault)


What changes in your workflow?
──────────────────────────────
Enabling semantic search silently upgrades these skills:

  /daily-plan    — Enriches meeting prep with thematically
                   related past discussions

  /meeting-prep  — Discovers past discussions related by
                   meaning, not just name matching

  /triage        — Matches inbox items to goals by meaning,
                   catches semantic duplicates

  Person Lookup  — Finds "the VP of Sales mentioned..." even
                   without a name

  Search & Recall — All vault searches use hybrid retrieval
                    (BM25 + vectors + LLM reranking)


System Requirements
───────────────────
  ~2.5GB disk space (for models + index)
  macOS: Homebrew SQLite required (brew install sqlite)
  Bun runtime (will install if missing)