Knowledge Management
QMD Local Search
Semantic and keyword search across all your markdown files
QMD is a local search engine for markdown files. Three search modes:
- BM25 keyword search (
qmd search) -- fast, no model needed - Semantic vector search (
qmd vsearch) -- finds conceptually related content even without keyword overlap - Hybrid search (
qmd query) -- both combined with reranking, best quality
My agent uses it constantly -- finding related notes for cross-linking, searching memory, answering questions about vault content.
Setup
bun install -g github:tobi/qmdSetup Prompt
Set up QMD collections for searching my files:
1. A "workspace" collection pointing to the OpenClaw workspace directory
2. A "memory" collection pointing to the memory subdirectory
3. A "vault" collection pointing to my knowledge vault
Run `qmd update` to index all files, then `qmd embed` to generate
embeddings for semantic search.
Commands reference:
- qmd search "query" -- keyword search (fast)
- qmd vsearch "query" -- semantic search (needs embeddings)
- qmd query "query" -- hybrid search + reranking (best quality)
- qmd update -- re-index after changes
- qmd embed -- update embeddings after changes
Set up a nightly cron job at 2 AM to run `qmd update && qmd embed`
so the index stays fresh.Models
QMD downloads embedding models on first use (~329MB). The query expansion and reranker models (~1.9GB total) download on first qmd query or qmd vsearch. First run will be slow -- after that it's fast.