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.
# 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
qmd update -c notes -c docs # Re-index only selected collections
qmd embed -c notes -c docs # Embed selected collections as one deduplicated set
# Export all matches for an agent
qmd search "API" --all --files --min-score 0.3qmd update and qmd embed coordinate SQLite writes with an atomic SQLite lease sidecar beside the active index (<index>.sqlite.qmd-writer-lock.sqlite). Filesystem scanning and embedding computation can proceed outside SQLite write transactions, but database commits are serialized. Lease acquire, heartbeat, stale takeover, and release are conditional SQL mutations keyed by owner token, so an old owner cannot delete or overwrite a replacement owner. Same-host leases are only recovered when the owner PID is gone; remote-host leases can be recovered after the stale timeout. Malformed sidecars are left untouched and reported with an actionable error instead of being renamed automatically.
Queries open the index read-only and use WAL snapshots, so hybrid search can return lexical plus available vector results while maintenance is running. Incomplete vector coverage is reported by qmd status/qmd doctor and does not force hybrid search to embed missing documents before returning. Collection-scoped FTS runs bounded SQLite BM25 candidate queries anchored to each selected collection's indexed filepath prefix, then exact-filters hydrated rows by collection. Collection-scoped vector search budgets selected collections in stable input order so one large source collection does not erase artifact hits from smaller selected collections. Single-collection exact scoring uses the configured 5000 ceiling; multi-collection interactive scoring uses a 1000 aggregate target within that ceiling so artifact-first scopes stay responsive. Multi-collection hybrid searches score the original semantic query once across the aggregate vector budget, retain lexical expansions, and skip the extra remote rerank by default (unless callers explicitly request it) so lexical plus semantic retrieval stays within interactive turn budgets. Structured searches share the aggregate collection budget.
Useful environment variables:
QMD_WRITER_LOCK_WAIT_MS: maximum time a maintenance writer waits for the writer lock before failing fast (default5000).QMD_WRITER_LOCK_STALE_MS: age after which an abandoned writer lock may be recovered (default600000).QMD_SQLITE_READ_BUSY_TIMEOUT_MS: SQLite busy timeout for read-only query handles (default500).QMD_EMBED_CLAIM_TTL_MS: lease duration for per-hash embedding claims (default3600000).QMD_OPENAI_QUERY_TIMEOUT_MS: per-call timeout for query-time OpenAI embedding, expansion, and rerank requests (default3000). Document embedding and maintenance batch embedding keep their normal retry behavior.QMD_SCOPED_VECTOR_EXACT_LIMIT: maximum selected active vector chunks to score exactly across collection-scoped vector search (default5000).
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" --fullAlthough 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 + rerankingget— Retrieve a document by path or docid (with fuzzy matching suggestions)multi_get— Batch retrieve by glob pattern, comma-separated list, or docidsstatus— 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@qmdOr configure MCP manually in ~/.claude/settings.json:
{
"mcpServers": {
"qmd": {
"command": "qmd",
"args": ["mcp"]
}
}
}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 activeThe 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.
| Tool | Parameter | Type | Notes |
|---|---|---|---|
query |
searches |
array | Typed sub-queries (lex/vec/hyde), 1–10. Required. First gets 2x weight. |
query |
collections |
string[] | Filter by collection names (OR). Array only — singular collection is silently ignored. |
query |
intent |
string | Disambiguation context (does not search on its own) |
query |
limit |
number | Max results (default 10) |
query |
minScore |
number | Minimum relevance 0–1 (default 0) |
query |
candidateLimit |
number | Max candidates to rerank (default 40) |
query |
rerank |
boolean | Run LLM reranking (default true); set false for RRF-only |
get |
file |
string | Path, docid (#abc123), or path:from:count (e.g. #abc123:120:40) |
get |
fromLine |
number | Start line (1-indexed); overrides the :from suffix |
get |
maxLines |
number | Limit returned lines |
get |
lineNumbers |
boolean | Prefix lines with numbers (default true) |
multi_get |
pattern |
string | Glob pattern or comma-separated list |
multi_get |
maxBytes |
number | Skip files larger than N (default 10240) |
multi_get |
maxLines |
number | Limit lines per file |
multi_get |
lineNumbers |
boolean | Prefix lines with numbers (default true) |
Unknown parameters are silently ignored (not rejected) — double-check names if
results seem unscoped. The HTTP /query and /search endpoints return
qmd://collection/path URIs in the file field, matching the CLI and MCP output.
Use QMD as a library in your own Node.js or Bun applications.
npm install @tobilu/qmdimport { 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()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' })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 })// 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,
})// 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 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// 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}`)
},
})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 callback info for update()
UpdateResult, // Aggregated update result
EmbedProgress, // Progress callback info for embed()
EmbedResult, // Embedding result
StoreOptions, // createStore() options
CollectionConfig, // Inline config shape
IndexStatus, // From getStatus()
IndexHealthInfo, // From getIndexHealth()
} from '@tobilu/qmd'Utility exports:
import {
extractSnippet, // Extract a relevant snippet from text
addLineNumbers, // Add line numbers to text
DEFAULT_MULTI_GET_MAX_BYTES, // Default max file size for multiGet (10KB)
Maintenance, // Database maintenance operations
} from '@tobilu/qmd'// Close the store — disposes LLM models and DB connection
await store.close()The SDK requires explicit dbPath — no defaults are assumed. This makes it safe to embed in any application without side effects.
┌─────────────────────────────────────────────────────────────────────────────┐
│ QMD Hybrid Search Pipeline │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────┐
│ User Query │
└────────┬────────┘
│
┌──────────────┴──────────────┐
▼ ▼
┌────────────────┐ ┌────────────────┐
│ Query Expansion│ │ Original Query│
│ (fine-tuned) │ │ (×2 weight) │
└───────┬────────┘ └───────┬────────┘
│ │
│ 2 alternative queries │
└──────────────┬──────────────┘
│
┌───────────────────────┼───────────────────────┐
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Original Query │ │ Expanded Query 1│ │ Expanded Query 2│
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
┌───────┴───────┐ ┌───────┴───────┐ ┌───────┴───────┐
▼ ▼ ▼ ▼ ▼ ▼
┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐
│ BM25 │ │Vector │ │ BM25 │ │Vector │ │ BM25 │ │Vector │
│(FTS5) │ │Search │ │(FTS5) │ │Search │ │(FTS5) │ │Search │
└───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘
│ │ │ │ │ │
└───────┬───────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
└────────────────────────┼───────────────────────┘
│
▼
┌───────────────────────┐
│ RRF Fusion + Bonus │
│ Original query: ×2 │
│ Top-rank bonus: +0.05│
│ Top 30 Kept │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ LLM Re-ranking │
│ (qwen3-reranker) │
│ Yes/No + logprobs │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ Position-Aware Blend │
│ Top 1-3: 75% RRF │
│ Top 4-10: 60% RRF │
│ Top 11+: 40% RRF │
└───────────────────────┘
| Backend | Raw Score | Conversion | Range |
|---|---|---|---|
| FTS (BM25) | SQLite FTS5 BM25 | Math.abs(score) |
0 to ~25+ |
| Vector | Cosine distance | 1 / (1 + distance) |
0.0 to 1.0 |
| Reranker | LLM 0-10 rating | score / 10 |
0.0 to 1.0 |
The query command uses Reciprocal Rank Fusion (RRF) with position-aware blending:
- Query Expansion: Original query (×2 for weighting) + 1 LLM variation
- Parallel Retrieval: Each query searches both FTS and vector indexes
- RRF Fusion: Combine all result lists using
score = Σ(1/(k+rank+1))where k=60 - Top-Rank Bonus: Documents ranking #1 in any list get +0.05, #2-3 get +0.02
- Top-K Selection: Take top 30 candidates for reranking
- Re-ranking: LLM scores each document (yes/no with logprobs confidence)
- Position-Aware Blending:
- RRF rank 1-3: 75% retrieval, 25% reranker (preserves exact matches)
- RRF rank 4-10: 60% retrieval, 40% reranker
- RRF rank 11+: 40% retrieval, 60% reranker (trust reranker more)
Why this approach: Pure RRF can dilute exact matches when expanded queries don't match. The top-rank bonus preserves documents that score #1 for the original query. Position-aware blending prevents the reranker from destroying high-confidence retrieval results.
| Score | Meaning |
|---|---|
| 0.8 - 1.0 | Highly relevant |
| 0.5 - 0.8 | Moderately relevant |
| 0.2 - 0.5 | Somewhat relevant |
| 0.0 - 0.2 | Low relevance |
- Node.js >= 22
- Bun >= 1.0.0
- macOS: Homebrew SQLite (for extension support)
brew install sqlite
QMD uses three local GGUF models (auto-downloaded on first use):
| Model | Purpose | Size |
|---|---|---|
embeddinggemma-300M-Q8_0 |
Vector embeddings (default) | ~300MB |
qwen3-reranker-0.6b-q8_0 |
Re-ranking | ~640MB |
qmd-query-expansion-1.7B-q4_k_m |
Query expansion (fine-tuned) | ~1.1GB |
Models are downloaded from HuggingFace and cached in ~/.cache/qmd/models/.
Override the default embedding model via the QMD_EMBED_MODEL environment variable.
This is useful for multilingual corpora (e.g. Chinese, Japanese, Korean) where
embeddinggemma-300M has limited coverage.
# Use Qwen3-Embedding-0.6B for better multilingual (CJK) support
export QMD_EMBED_MODEL="hf:Qwen/Qwen3-Embedding-0.6B-GGUF/Qwen3-Embedding-0.6B-Q8_0.gguf"
# After changing the model, re-embed all collections:
qmd embed -fSupported local paths:
- EmbeddingGemma (default) — preserves QMD's existing asymmetric prompts.
- gte-modernbert-base — opt-in quality-oriented CPU preset (~160 MB Q8 GGUF).
- bge-small-en-v1.5 — opt-in fast 384d CPU baseline.
- Qwen3-Embedding — existing multilingual control; not selected automatically.
QMD_EMBED_PRESET=quality-cpu qmd pull # gte-modernbert-base Q8
QMD_EMBED_PRESET=fast-cpu qmd pull # bge-small-en-v1.5 Q8
QMD_EMBED_PRESET=embeddinggemma qmd pullPresets only select a model; they do not download models during tests or benchmarks.
Runtime support still depends on the installed node-llama-cpp/GGUF backend. After
changing any embedding compatibility factor, run qmd embed -f. QMD now fingerprints
provider/backend, model reference and revision, artifact/quantization, pooling,
normalization, dimensions, tokenizer identity/revision, exact query/document prompt
renderings, and chunking parameters. Search hydration rejects vectors whose model or
fingerprint is not current, including during incremental migrations.
As an alternative to local embedding models, you can use OpenAI's API for faster, more reliable embeddings:
# ~/.config/qmd/index.yml
embedding:
provider: openai
openai:
api_key: sk-... # Optional, falls back to QMD_OPENAI_API_KEY or OPENAI_API_KEY env var
model: text-embedding-3-small # Optional, this is the default
dimensions: 1536 # Optional API dimensions control
expansion_model: gpt-4o-mini # Optional, model for query expansion/reranking
base_url: https://api.openai.com/v1 # Optional, for OpenAI-compatible APIs (Ollama, vLLM, etc.)Benefits:
- ~10x faster than local CPU inference
- No GPU required - works on any machine
- More reliable - no local model loading issues
- Cost: ~$0.02 per 1M tokens (very cheap)
- OpenAI-compatible - works with Ollama, vLLM, Azure, and other compatible APIs via
base_url
When using OpenAI embeddings, query expansion and reranking use the OpenAI API instead of local models.
For mutable aliases or custom OpenAI-compatible services, declare compatibility metadata explicitly:
embedding:
provider: openai
identity:
backend: openai-compatible-v1
revision: deployment-2026-09-01
quantization: provider-managed
pooling: provider-managed
normalization: provider-managed
dimensions: 1536
tokenizer: cl100k_base
tokenizer_revision: tiktoken-1.x
query_prompt: "{{query}}"
document_prompt: "{{title}}\n{{text}}"
openai:
model: text-embedding-3-small
dimensions: 1536Changing any field produces a new 128-bit embedding fingerprint. Legacy six-character fingerprints remain readable for migration diagnostics but never alias the new schema.
npm install -g @tobilu/qmd
# or
bun install -g @tobilu/qmdgit clone https://github.com/tobi/qmd
cd qmd
npm install
npm link# Create a collection from current directory
qmd collection add . --name myproject
# Create a collection with explicit path and custom glob mask
qmd collection add ~/Documents/notes --name notes --mask "**/*.md"
# List all collections
qmd collection list
# Remove a collection
qmd collection remove myproject
# Rename a collection
qmd collection rename myproject my-project
# List files in a collection
qmd ls notes
qmd ls notes/subfolder
# Show collection details (path, glob mask, include status, context count)
qmd collection show notes
# Include or exclude a collection from default (unscoped) queries
qmd collection include notes
qmd collection exclude notes
# Run a command before every `qmd update` (e.g. git pull); empty arg clears it
qmd collection update-cmd notes 'git pull --rebase'
qmd collection update-cmd notes# Embed all indexed documents (900 tokens/chunk, 15% overlap)
qmd embed
# Force re-embed everything
qmd embed -f
# Enable AST-aware chunking for code files (TS, JS, Python, Go, Rust)
qmd embed --chunk-strategy auto
# Also works with query for consistent chunk selection
qmd query "auth flow" --chunk-strategy auto
# Memory control for large corpora / constrained systems
qmd embed --max-docs-per-batch 50 # cap docs per embedding batch
qmd embed --max-batch-mb 64 # cap batch size in MBAST-aware chunking (--chunk-strategy auto) uses tree-sitter to chunk code
files at function, class, and import boundaries instead of arbitrary text
positions. This produces higher-quality chunks and better search results for
codebases. Markdown and other file types always use regex-based chunking
regardless of strategy.
The default is regex (existing behavior). Use --chunk-strategy auto to
opt in. Run qmd status to verify which grammars are available.
Note: Tree-sitter grammars are optional dependencies. If they are not installed,
--chunk-strategy autofalls back to regex-only chunking automatically. Tested on both Node.js and Bun.
Context adds descriptive metadata to collections and paths, helping search understand your content.
# Add context to a collection (using qmd:// virtual paths)
qmd context add qmd://notes "Personal notes and ideas"
qmd context add qmd://docs/api "API documentation"
# Add context from within a collection directory
cd ~/notes && qmd context add "Personal notes and ideas"
cd ~/notes/work && qmd context add "Work-related notes"
# Add global context (applies to all collections)
qmd context add / "Knowledge base for my projects"
# List all contexts
qmd context list
# Remove context
qmd context rm qmd://notes/old┌──────────────────────────────────────────────────────────────────┐
│ Search Modes │
├──────────┬───────────────────────────────────────────────────────┤
│ search │ BM25 full-text search only │
│ vsearch │ Vector semantic search only │
│ query │ Hybrid: FTS + Vector + Query Expansion + Re-ranking │
└──────────┴───────────────────────────────────────────────────────┘
# Full-text search (fast, keyword-based)
qmd search "authentication flow"
# Vector search (semantic similarity)
qmd vsearch "how to login"
# Hybrid search with re-ranking (best quality)
qmd query "user authentication"Two aliases exist for the semantic/hybrid modes: vector-search (→ vsearch)
and deep-search (→ query).
# Search options
-n <num> # Number of results (default: 5, or 20 for --files/--json)
-c, --collection # Restrict search to a specific collection
--all # Return all matches (use with --min-score to filter)
--min-score <num> # Minimum score threshold (default: 0)
--full # Show full document content
--line-numbers # Add line numbers to output
--explain # Include retrieval score traces (query, JSON/CLI output)
--index <name> # Use named index
--intent "<text>" # Disambiguation context (e.g. "web page load times")
--no-rerank # Skip LLM reranking (RRF scores only; faster on CPU)
-C, --candidate-limit <n> # Max candidates to rerank (default: 40)
--full-path # Emit on-disk filesystem paths instead of qmd:// URIs
# Output formats (for search and multi-get)
--format <kind> # cli (default) | json | csv | md | xml | files
# (--json, --csv, --md, --xml, --files are legacy aliases)
# Get options
qmd get <file>[:from[:count]] # Get document; optional start line and count
-l <num> # Maximum lines to return
--from <num> # Start line (overrides the :from suffix)
--no-line-numbers # Disable line numbering (on by default)
# Multi-get options
-l <num> # Maximum lines per file
--max-bytes <num> # Skip files larger than N bytes (default: 10KB)The -c/--collection flag filters results by collection name (as shown by
qmd collection list). Collections are a global registry — you can search any
collection from any directory:
qmd search "auth" -c notes # single collection
qmd search "auth" -c notes -c docs # multiple collections (OR)With no -c flag, all default-included collections are searched. Collections
marked excluded (qmd collection exclude <name>) are skipped unless named
explicitly with -c.
Note: With multiple
-cflags, results come from a global top-K pool and are then filtered. If one collection dominates the rankings, matches from smaller collections may not appear at the default limit — raise-nor use--all.
Default output is colorized CLI format (respects NO_COLOR env).
When stdout is a TTY, result paths are emitted as clickable terminal hyperlinks (OSC 8). Clicking a path opens the file in your editor using an editor URI template.
When stdout is not a TTY (for example piped to another command or redirected to a file), QMD emits plain text paths with no escape sequences.
TTY example:
docs/guide.md:42 #a1b2c3
Title: Software Craftsmanship
Context: Work documentation
Score: 93%
This section covers the **craftsmanship** of building
quality software with attention to detail.
See also: engineering principles
notes/meeting.md:15 #d4e5f6
Title: Q4 Planning
Context: Personal notes and ideas
Score: 67%
Discussion about code quality and craftsmanship
in the development process.
Configure the editor link target with QMD_EDITOR_URI (or editor_uri in config):
# VS Code (default)
export QMD_EDITOR_URI="vscode://file/{path}:{line}:{col}"
# Cursor
export QMD_EDITOR_URI="cursor://file/{path}:{line}:{col}"
# Zed
export QMD_EDITOR_URI="zed://file/{path}:{line}:{col}"
# Sublime Text
export QMD_EDITOR_URI="subl://open?url=file://{path}&line={line}"Template placeholders:
-
{path}absolute filesystem path (URI-encoded) -
{line}1-based line number -
{col}or{column}1-based column number -
Path: Collection-relative path (e.g.,
docs/guide.md) -
Docid: Short hash identifier (e.g.,
#a1b2c3) - use withqmd get #a1b2c3 -
Title: Extracted from document (first heading or filename)
-
Context: Path context if configured via
qmd context add -
Score: Color-coded (green >70%, yellow >40%, dim otherwise)
-
Snippet: Context around match with query terms highlighted
# Get 10 results with minimum score 0.3
qmd query -n 10 --min-score 0.3 "API design patterns"
# Output as markdown for LLM context
qmd search --md --full "error handling"
# JSON output for scripting
qmd query --json "quarterly reports"
# Inspect how each result was scored (RRF + rerank blend)
qmd query --json --explain "quarterly reports"
# Use separate index for different knowledge base
qmd --index work search "quarterly reports"The --explain flag attaches a score breakdown to each result: the FTS/vector
backend scores plus the RRF fusion math (rank, weight, top-rank bonus) and every
sub-query's contribution. Abbreviated:
{
"docid": "#6c90f0",
"score": 0.89,
"file": "qmd://qmd/README.md",
"explain": {
"ftsScores": [0.892, 0.907],
"vectorScores": [0.540, 0.484],
"rrf": {
"rank": 1,
"weight": 0.75,
"baseScore": 0.123,
"topRankBonus": 0.05,
"totalScore": 0.173,
"contributions": [
{ "source": "fts", "queryType": "original", "query": "reranking",
"rank": 1, "weight": 2, "backendScore": 0.892, "rrfContribution": 0.0328 }
]
}
}
}# Show index status and collections with contexts
qmd status
# Re-index all collections. If a collection has a configured update command
# (e.g. `git pull`), it runs first — set one with `qmd collection update-cmd`.
qmd update
# Diagnose the install (runtime, sqlite-vec, embedding fingerprints, GPU probe)
qmd doctor
# Initialize a project-local index in the current directory
qmd init
# Get document by filepath (with fuzzy matching suggestions)
qmd get notes/meeting.md
# Get document by docid (from search results)
qmd get "#abc123"
# Get document starting at line 50, max 100 lines
qmd get notes/meeting.md:50 -l 100
# Read 40 lines starting at line 120 via the :from:count suffix (works with docids)
qmd get notes/meeting.md:120:40
qmd get "#abc123:120:40"
# get / multi-get are line-numbered by default; disable with --no-line-numbers
qmd get notes/meeting.md --no-line-numbers
# Get multiple documents by glob pattern
qmd multi-get "journals/2025-05*.md"
# Get multiple documents by comma-separated list (supports docids)
qmd multi-get "doc1.md, doc2.md, #abc123"
# Limit multi-get to files under 20KB
qmd multi-get "docs/*.md" --max-bytes 20480
# Output multi-get as JSON for agent processing
qmd multi-get "docs/*.md" --json
# Clean up cache and orphaned data
qmd cleanupMeasure search quality across all four backends with qmd bench and a fixture file
of queries with known-relevant documents.
From a git checkout, an example fixture and its test corpus ship in the repo:
# One-time setup (indexes the repo's test corpus into its own collection)
qmd collection add test/eval-docs --name eval-docs
qmd embed -c eval-docs
# Run the benchmark (table output)
qmd bench src/bench/fixtures/example.json
# JSON output for programmatic analysis
qmd bench src/bench/fixtures/example.json --jsonThe example fixture (
src/bench/fixtures/example.json) and its test corpus (test/eval-docs/) exist only in a git checkout — they are not part of the published npm package. If you installed vianpm/npx, write your own fixture (see below) against a collection you have already indexed:qmd bench my-fixture.json -c my-collection
Each query runs against four backends. JSON output records the exact fixture SHA-256, Recall@10/40/100, MRR, binary nDCG@10, latency, search throughput, SQLite index size, peak process RSS, and the active embedding identity/fingerprint. The ordinary command keeps its historical top-10 retrieval depth, so use the frozen matrix runner below when Recall@40/100 must be measured from 100 returned candidates:
| Backend | What it tests | LLM required |
|---|---|---|
bm25 |
Keyword search only (FTS5) | No |
vector |
Semantic similarity only | Embedding model |
hybrid |
BM25 + vector fusion (no reranking) | Embedding model |
full |
Full pipeline with LLM reranking | All three models |
The SDK also exposes opt-in bounded adaptation (adaptive: true,
adaptiveMaxCandidates, default hard ceiling 100). It expands the candidate/rerank
set by at most 2x only when explicit weak-evidence signals fire: insufficient
candidates, a single retrieval signal, low cross-signal agreement, or a flat score
head. It does not run an unbounded recursive search loop.
Score interpretation: 1.00 = perfect and 0.00 = complete miss. Do not
compare runs unless fixture_sha256, corpus/index state, embedding identity, and host
conditions are controlled. The example is a harness fixture, not a published quality
claim; QMD does not claim model or pipeline parity without measured frozen runs.
Custom fixtures are JSON:
{
"description": "My benchmark",
"version": 1,
"collection": "my-collection",
"queries": [
{
"id": "find-auth",
"query": "authentication flow",
"type": "semantic",
"expected_files": ["docs/auth-design.md"],
"expected_in_top_k": 3
}
]
}expected_files are collection-relative paths as shown by qmd ls. The type
field (exact, semantic, topical, cross-domain, alias) labels queries for
grouping — it does not change search behavior.
Backend failures are marked not_measured with the underlying reason and are excluded
from summaries; placeholder metric fields remain only for JSON compatibility.
For the reproducible five-model comparison (quality-cpu, fast-cpu, EmbeddingGemma,
Qwen3 control, and OpenAI text-embedding-3-small) across vector-only,
hybrid-no-rerank, and full modes, see bench/README.md. The runner
uses already-prepared independent indexes and never downloads a model matrix.
Index stored in: ~/.cache/qmd/index.sqlite
collections -- Indexed directories with name and glob patterns
path_contexts -- Context descriptions by virtual path (qmd://...)
documents -- Markdown content with metadata and docid (6-char hash)
documents_fts -- FTS5 full-text index
content_vectors -- Embedding chunks (hash, seq, pos, 900 tokens each)
vectors_vec -- sqlite-vec vector index (hash_seq key)
llm_cache -- Cached LLM responses (query expansion, rerank scores)| Variable | Default | Description |
|---|---|---|
XDG_CACHE_HOME |
~/.cache |
Cache directory location |
QMD_LLAMA_GPU |
auto |
Force llama.cpp GPU backend (metal, vulkan, cuda) or disable GPU with false |
QMD_FORCE_CPU |
unset | Set to 1/true to force CPU mode before any CUDA/Vulkan/Metal probing. Equivalent CLI flag: --no-gpu. |
QMD_EMBED_PARALLELISM |
automatic | Override embedding/reranking context parallelism (1-8). Windows CUDA defaults to 1 because parallel CUDA contexts can crash with ggml-cuda.cu:98; use Vulkan or raise this only if your driver is stable. |
Collection ──► Glob Pattern ──► Markdown Files ──► Parse Title ──► Hash Content
│ │ │
│ │ ▼
│ │ Generate docid
│ │ (6-char hash)
│ │ │
└──────────────────────────────────────────────────►└──► Store in SQLite
│
▼
FTS5 Index
Documents are chunked into ~900-token pieces with 15% overlap using smart boundary detection:
Document ──► Smart Chunk (~900 tokens) ──► Format each chunk ──► node-llama-cpp ──► Store Vectors
│ "title | text" embedBatch()
│
└─► Chunks stored with:
- hash: document hash
- seq: chunk sequence (0, 1, 2...)
- pos: character position in original
Instead of cutting at hard token boundaries, QMD uses a scoring algorithm to find natural markdown break points. This keeps semantic units (sections, paragraphs, code blocks) together.
Break Point Scores:
| Pattern | Score | Description |
|---|---|---|
# Heading |
100 | H1 - major section |
## Heading |
90 | H2 - subsection |
### Heading |
80 | H3 |
#### Heading |
70 | H4 |
##### Heading |
60 | H5 |
###### Heading |
50 | H6 |
``` |
80 | Code block boundary |
--- / *** |
60 | Horizontal rule |
| Blank line | 20 | Paragraph boundary |
- item / 1. item |
5 | List item |
| Line break | 1 | Minimal break |
Algorithm:
- Scan document for all break points with scores
- When approaching the 900-token target, search a 200-token window before the cutoff
- Score each break point:
finalScore = baseScore × (1 - (distance/window)² × 0.7) - Cut at the highest-scoring break point
The squared distance decay means a heading 200 tokens back (score ~30) still beats a simple line break at the target (score 1), but a closer heading wins over a distant one.
Code Fence Protection: Break points inside code blocks are ignored—code stays together. If a code block exceeds the chunk size, it's kept whole when possible.
AST-Aware Chunking (Code Files):
For supported code files, QMD also parses the source with tree-sitter and adds AST-derived break points that are merged with the regex scores above:
| AST Node | Score | Languages |
|---|---|---|
| Class / interface / struct / impl / trait | 100 | All |
| Function / method | 90 | All |
| Type alias / enum | 80 | All |
| Import / use declaration | 60 | All |
Supported for .ts, .tsx, .js, .jsx, .py, .go, and .rs files. Enable with --chunk-strategy auto. Markdown and other file types always use regex chunking.
Query ──► LLM Expansion ──► [Original, Variant 1, Variant 2]
│
┌─────────┴─────────┐
▼ ▼
For each query: FTS (BM25)
│ │
▼ ▼
Vector Search Ranked List
│
▼
Ranked List
│
└─────────┬─────────┘
▼
RRF Fusion (k=60)
Original query ×2 weight
Top-rank bonus: +0.05/#1, +0.02/#2-3
│
▼
Top 30 candidates
│
▼
LLM Re-ranking
(yes/no + logprob confidence)
│
▼
Position-Aware Blend
Rank 1-3: 75% RRF / 25% reranker
Rank 4-10: 60% RRF / 40% reranker
Rank 11+: 40% RRF / 60% reranker
│
▼
Final Results
Models are configured in src/llm.ts as HuggingFace URIs:
const DEFAULT_EMBED_MODEL = "hf:ggml-org/embeddinggemma-300M-GGUF/embeddinggemma-300M-Q8_0.gguf";
const DEFAULT_RERANK_MODEL = "hf:ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF/qwen3-reranker-0.6b-q8_0.gguf";
const DEFAULT_GENERATE_MODEL = "hf:tobil/qmd-query-expansion-1.7B-gguf/qmd-query-expansion-1.7B-q4_k_m.gguf";// For queries
"task: search result | query: {query}"
// For documents
"title: {title} | text: {content}"
Uses node-llama-cpp's createRankingContext() and rankAndSort() API for cross-encoder reranking. Returns documents sorted by relevance score (0.0 - 1.0).
Used for generating query variations via LlamaChatSession.
MIT