qmd-searchFast local search for markdown files, notes, and docs using qmd CLI. Use instead of `find` for file discovery. Combines BM25 full-text search, vector semantic search, and LLM reranking—all running locally. Use when searching for files, finding code, locating documentation, or discovering content in indexed collections.
Install via ClawdBot CLI:
clawdbot install bheemreddy181/qmd-searchfind across large directories (avoids hangs)# Keyword search (BM25)
qmd search "alpaca API" -c projects
# Semantic search (understands meaning)
qmd vsearch "how to implement stop loss"
# Combined search with reranking (best quality)
qmd query "trading rules for breakouts"
# File paths only (fast discovery)
qmd search "config" --files -c kell
# Full document content
qmd search "pattern detection" --full --line-numbers# List collections
qmd collection list
# Add new collection
qmd collection add /path/to/folder --name myproject --mask "*.md,*.py"
# Re-index after changes
qmd update# Get full file
qmd get myproject/README.md
# Get specific lines
qmd get myproject/config.py:50 -l 30
# Get multiple files by glob
qmd multi-get "*.yaml" -l 50 --max-bytes 10240--files — paths + scores (for file discovery)--json — structured with snippets--md — markdown formatted-n 10 — limit results-c name) to scope searchesqmd update after adding new filesqmd embed to enable vector search (one-time, takes a few minutes)qmd search --files over find for large directoriesAll run locally — no API keys needed.
Generated Mar 1, 2026
Developers use qmd to quickly locate code snippets, configuration files, and markdown documentation within large project repositories. It replaces slow find commands with fast, semantic search, improving productivity during debugging and onboarding.
Researchers index their notes, papers, and datasets to perform combined keyword and semantic searches. This helps in discovering relevant literature and connecting ideas across documents without relying on cloud services.
Support teams index internal documentation and troubleshooting guides to quickly find solutions for customer issues. The local search ensures data privacy while providing accurate, context-aware results.
Writers and bloggers use qmd to search through drafts, notes, and published articles for inspiration or to avoid duplication. The semantic search helps in finding related content based on meaning rather than just keywords.
Offer a free version with basic search capabilities and charge for advanced features like team collaboration, priority support, and enhanced indexing options. This targets individual users and small teams looking for efficient local search tools.
Sell licenses for on-premises deployment in organizations with strict data security requirements. Include custom integrations, dedicated support, and scalability options for large document collections across departments.
Integrate qmd into popular IDEs and development platforms as a plugin or API service. Monetize through marketplace sales, usage-based pricing, or partnerships with software vendors to enhance developer workflows.
💬 Integration Tip
Integrate qmd into existing workflows by setting up automated indexing scripts and using collections to organize different project directories for efficient search scoping.
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Smart ClawdBot documentation access with local search index, cached snippets, and on-demand fetch. Token-efficient and freshness-aware.
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