docmd-search

Quick Start

Run docmd-search instantly on any folder:

npx docmd-search ./docs

That's it.

• Files are discovered and chunked automatically
• Embeddings are generated locally (no cloud API)
• Search is available in the terminal immediately

Install for regular usage

npm install -g docmd-search

# Install ML dependencies (one-time)
npm install -g @huggingface/transformers onnxruntime-node

docmd-search ./docs          # index + interactive search
docmd-search ./docs --ui     # index + web UI
docmd-search --settings      # configure model

Features

Designed to work offline, ship nothing to the browser, and stay out of your way.

Offline by default

• All embeddings generated locally with ONNX Runtime
• No data leaves your machine
• No cloud API keys needed

Instant search

• Progressive indexing: search available from the first batch
• Incremental: only re-indexes changed files
• Resumable: interrupted indexing resumes from last checkpoint

Tiny client

• Browser runtime is <3KB gzipped
• No model weights in the browser
• Hybrid scoring: keyword matching + vector similarity

Built-in capabilities

• Multi-batch index format with automatic compression
• Navigation tree generation for web UIs
• First-run setup wizard with model selection
• Interactive terminal search with live results

How It Works

Build time (Node.js)                    Search time (Browser, <3KB)
───────────────────                     ──────────────────────────
 Crawl files                             Load manifest.json
   → Chunk by heading                      → Load batch 000 (instant)
     → Embed via ONNX                        → Background-load rest
       → Quantize Float32 → Int8               → Keyword + cosine
         → Compress (ternary/PQ)                 → Ranked results
           → Save multi-batch index

Models

First run prompts you to select an embedding model:

Change model later: docmd-search --settings

Configuration (optional)

No configuration is required to get started.

Global (~/.docmd-search/config.json):

{
  "model": "Xenova/all-MiniLM-L6-v2",
  "wizardCompleted": true
}

Per-project (_docmd-search/config.json):

{
  "model": "Xenova/bge-small-en-v1.5",
  "chunkSize": 512,
  "include": ["**/*.md"],
  "exclude": ["**/drafts/**"]
}

Config resolution: defaults → global → project → CLI flags.

Programmatic Usage

Use in scripts or CI pipelines:

import { indexDirectory, loadAllBatches } from 'docmd-search';

const index = await indexDirectory({
  rootDir: './docs',
  outDir: '_docmd-search',
});

Browser client:

import { load, search } from 'docmd-search/client';

await load('/path/to/_docmd-search');
const results = search('deploy kubernetes', 10);

Project Structure

Keeps the codebase flat and modular.

src/
├── bin/docmd-search.ts   # CLI entry point
├── client/index.ts       # Browser runtime (<3KB)
├── config.ts             # Config + model profiles
├── index-io.ts           # Multi-batch format + compression
├── index.ts              # Barrel exports
├── indexer/
│   ├── chunk.ts          # Heading-aware chunking
│   ├── crawl.ts          # File discovery
│   └── index.ts          # Progressive pipeline
├── model.ts              # ONNX embedding manager
├── tui.ts                # Terminal UI
├── types.ts              # Core types
└── ui/
    └── launcher.ts       # Web UI via docmd

Part of the docmd ecosystem

docmd-search works standalone with any documentation project. It also integrates with docmd as a semantic search plugin.

Community & Support

• Contributions are welcome
• If you find it useful, consider sponsoring or starring the repo ⭐

License

MIT License. See LICENSE for details.