QMD - Query Markup Documents

An on-device search engine for everything you need to remember. Index your markdown notes, meeting transcripts, documentation, and knowledge bases. Search with keywords or natural language. Ideal for your agentic flows.

QMD combines BM25 full-text search, vector semantic search, and LLM re-ranking—all running locally via node-llama-cpp with GGUF models.

You can read more about QMD's progress in the CHANGELOG.

Quick Start

# Install globally (Node or Bun)
npm install -g @tobilu/qmd
# or
bun install -g @tobilu/qmd

# Or run directly
npx @tobilu/qmd ...
bunx @tobilu/qmd ...

# Create collections for your notes, docs, and meeting transcripts
qmd collection add ~/notes --name notes
qmd collection add ~/Documents/meetings --name meetings
qmd collection add ~/work/docs --name docs

# Add context to help with search results, each piece of context will be returned when matching sub documents are returned. This works as a tree. This is the key feature of QMD as it allows LLMs to make much better contextual choices when selecting documents. Don't sleep on it!
qmd context add qmd://notes "Personal notes and ideas"
qmd context add qmd://meetings "Meeting transcripts and notes"
qmd context add qmd://docs "Work documentation"

# Generate embeddings for semantic search
qmd embed

# Search across everything
qmd search "project timeline"           # Fast keyword search
qmd vsearch "how to deploy"             # Semantic search
qmd query "quarterly planning process"  # Hybrid + reranking (best quality)

# Get a specific document
qmd get "meetings/2024-01-15.md"

# Get a document by docid (shown in search results)
qmd get "#abc123"

# Get multiple documents by glob pattern
qmd multi-get "journals/2025-05*.md"

# Search within a specific collection
qmd search "API" -c notes

# Export all matches for an agent
qmd search "API" --all --files --min-score 0.3

Using with AI Agents

QMD's --json and --files output formats are designed for agentic workflows:

# Get structured results for an LLM
qmd search "authentication" --json -n 10

# List all relevant files above a threshold
qmd query "error handling" --all --files --min-score 0.4

# Retrieve full document content
qmd get "docs/api-reference.md" --full

MCP Server

Although the tool works perfectly fine when you just tell your agent to use it on the command line, it also exposes an MCP (Model Context Protocol) server for tighter integration.

Tools exposed:

• query — Search with typed sub-queries (lex/vec/hyde), combined via RRF + reranking
• get — Retrieve a document by path or docid (with fuzzy matching suggestions)
• multi_get — Batch retrieve by glob pattern, comma-separated list, or docids
• status — Index health and collection info

Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "qmd": {
      "command": "qmd",
      "args": ["mcp"]
    }
  }
}

Claude Code — Install the plugin (recommended):

claude plugin marketplace add tobi/qmd
claude plugin install qmd@qmd

Or configure MCP manually in ~/.claude/settings.json:

{
  "mcpServers": {
    "qmd": {
      "command": "qmd",
      "args": ["mcp"]
    }
  }
}

HTTP Transport

By default, QMD's MCP server uses stdio (launched as a subprocess by each client). For a shared, long-lived server that avoids repeated model loading, use the HTTP transport:

# Foreground (Ctrl-C to stop)
qmd mcp --http                    # localhost:8181
qmd mcp --http --port 8080        # custom port

# Background daemon
qmd mcp --http --daemon           # start, writes PID to ~/.cache/qmd/mcp.pid
qmd mcp stop                      # stop via PID file
qmd status                        # shows "MCP: running (PID ...)" when active

The HTTP server exposes two endpoints:

• POST /mcp — MCP Streamable HTTP (JSON responses, stateless)
• GET /health — liveness check with uptime

LLM models stay loaded in VRAM across requests. Embedding/reranking contexts are disposed after 5 min idle and transparently recreated on the next request (~1s penalty, models remain loaded).

Point any MCP client at http://localhost:8181/mcp to connect.

SDK / Library Usage

Use QMD as a library in your own Node.js or Bun applications.

Installation

npm install @tobilu/qmd

Quick Start

import { createStore } from '@tobilu/qmd'

const store = await createStore({
  dbPath: './my-index.sqlite',
  config: {
    collections: {
      docs: { path: '/path/to/docs', pattern: '**/*.md' },
    },
  },
})

const results = await store.search({ query: "authentication flow" })
console.log(results.map(r => `${r.title} (${Math.round(r.score * 100)}%)`))

await store.close()

Store Creation

createStore() accepts three modes:

import { createStore } from '@tobilu/qmd'

// 1. Inline config — no files needed besides the DB
const store = await createStore({
  dbPath: './index.sqlite',
  config: {
    collections: {
      docs: { path: '/path/to/docs', pattern: '**/*.md' },
      notes: { path: '/path/to/notes' },
    },
  },
})

// 2. YAML config file — collections defined in a file
const store2 = await createStore({
  dbPath: './index.sqlite',
  configPath: './qmd.yml',
})

// 3. DB-only — reopen a previously configured store
const store3 = await createStore({ dbPath: './index.sqlite' })

Search

The unified search() method handles both simple queries and pre-expanded structured queries:

// Simple query — auto-expanded via LLM, then BM25 + vector + reranking
const results = await store.search({ query: "authentication flow" })

// With options
const results2 = await store.search({
  query: "rate limiting",
  intent: "API throttling and abuse prevention",
  collection: "docs",
  limit: 5,
  minScore: 0.3,
  explain: true,
})

// Pre-expanded queries — skip auto-expansion, control each sub-query
const results3 = await store.search({
  queries: [
    { type: 'lex', query: '"connection pool" timeout -redis' },
    { type: 'vec', query: 'why do database connections time out under load' },
  ],
  collections: ["docs", "notes"],
})

// Skip reranking for faster results
const fast = await store.search({ query: "auth", rerank: false })

For direct backend access:

// BM25 keyword search (fast, no LLM)
const lexResults = await store.searchLex("auth middleware", { limit: 10 })

// Vector similarity search (embedding model, no reranking)
const vecResults = await store.searchVector("how users log in", { limit: 10 })

// Manual query expansion for full control
const expanded = await store.expandQuery("auth flow", { intent: "user login" })
const results4 = await store.search({ queries: expanded })

Retrieval

// Get a document by path or docid
const doc = await store.get("docs/readme.md")
const byId = await store.get("#abc123")

if (!("error" in doc)) {
  console.log(doc.title, doc.displayPath, doc.context)
}

// Get document body with line range
const body = await store.getDocumentBody("docs/readme.md", {
  fromLine: 50,
  maxLines: 100,
})

// Batch retrieve by glob or comma-separated list
const { docs, errors } = await store.multiGet("docs/**/*.md", {
  maxBytes: 20480,
})

Collections

// Add a collection
await store.addCollection("myapp", {
  path: "/src/myapp",
  pattern: "**/*.ts",
  ignore: ["node_modules/**", "*.test.ts"],
})

// List collections with document stats
const collections = await store.listCollections()
// => [{ name, pwd, glob_pattern, doc_count, active_count, last_modified, includeByDefault }]

// Get names of collections included in queries by default
const defaults = await store.getDefaultCollectionNames()

// Remove / rename
await store.removeCollection("myapp")
await store.renameCollection("old-name", "new-name")

Context

Context adds descriptive metadata that improves search relevance and is returned alongside results:

// Add context for a path within a collection
await store.addContext("docs", "/api", "REST API reference documentation")

// Set global context (applies to all collections)
await store.setGlobalContext("Internal engineering documentation")

// List all contexts
const contexts = await store.listContexts()
// => [{ collection, path, context }]

// Remove context
await store.removeContext("docs", "/api")
await store.setGlobalContext(undefined)  // clear global

Indexing

// Re-index collections by scanning the filesystem
const result = await store.update({
  collections: ["docs"],  // optional — defaults to all
  onProgress: ({ collection, file, current, total }) => {
    console.log(`[${collection}] ${current}/${total} ${file}`)
  },
})
// => { collections, indexed, updated, unchanged, removed, needsEmbedding }

// Generate vector embeddings
const embedResult = await store.embed({
  force: false,           // true to re-embed everything
  chunkStrategy: "auto",  // "regex" (default) or "auto" (AST for code files)
  onProgress: ({ current, total, collection }) => {
    console.log(`Embedding ${current}/${total}`)
  },
})

Types

Key types exported for SDK consumers:

import type {
  QMDStore,            // The store interface
  SearchOptions,       // Options for search()
  LexSearchOptions,    // Options for searchLex()
  VectorSearchOptions, // Options for searchVector()
  HybridQueryResult,   // Search result with score, snippet, context
  SearchResult,        // Result from searchLex/searchVector
  ExpandedQuery,       // Typed sub-query { type: 'lex'|'vec'|'hyde', query }
  DocumentResult,      // Document metadata + body
  DocumentNotFound,    // Error with similarFiles suggestions
  MultiGetResult,      // Batch retrieval result
  UpdateProgress,      // Progress c