dowse

English | [简体中文](README.zh-CN.md)

<p align="center">
  <img src="crates/dowse-app/src-tauri/icons/128x128@2x.png" width="96" height="96" alt="dowse logo">
</p>

<h1 align="center">dowse</h1>

<p align="center">
  Open-source local file content search for Windows. Find file names, PDF and Office contents, source code, and text inside screenshots — one hotkey away.
</p>

<p align="center">
  <a href="https://lter.space/dowse/">Website</a> ·
  <a href="https://lter.space/dowse/en/">English product page</a> ·
  <a href="https://lter.space/dowse/windows-file-content-search/">Windows file-content search guide（中文）</a>
</p>

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  <a href="#license"><img src="https://img.shields.io/badge/license-MIT%2FApache--2.0-blue.svg" alt="License"></a>
  <a href="https://github.com/ltspace/dowse/releases/latest"><img src="https://img.shields.io/github/v/release/ltspace/dowse" alt="Latest release"></a>
  <a href="https://github.com/ltspace/dowse/actions/workflows/ci.yml"><img src="https://github.com/ltspace/dowse/actions/workflows/ci.yml/badge.svg" alt="CI status"></a>
  <a href="https://github.com/ltspace/dowse/stargazers"><img src="https://img.shields.io/github/stars/ltspace/dowse?style=flat" alt="GitHub stars"></a>
  <img src="https://img.shields.io/badge/platform-Windows-0078D6?logo=windows&logoColor=white" alt="Platform: Windows">
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  <a href="https://github.com/ltspace/dowse/releases"><img src="https://img.shields.io/github/downloads/ltspace/dowse/total" alt="Downloads"></a>
  <a href="https://glama.ai/mcp/servers/ltspace/dowse"><img src="https://glama.ai/mcp/servers/ltspace/dowse/badges/score.svg" alt="Glama MCP server score"></a>
</p>

The name comes from a dowsing rod.

![dowse overlay mid-search, query "sql" with ranked file results and a live preview pane](docs/screenshots/hero.png)

## Motivation

No Windows tool satisfies all three of the following at once:

- Keep a persistent index of file contents, not just file names (Everything can scan contents on demand, but its instant index is centered on names and paths)
- Recognize and index text inside images on ordinary Windows PCs without requiring a Copilot+ device
- One hotkey to summon, full keyboard operation, no perceptible latency

The closest open-source implementation is sist2, but it targets Linux (on Windows it only runs via Docker), treats Chinese text as trigrams, and the project is no longer maintained. dowse is a Windows-native implementation built around these three points.

## Features

| | |
|---|---|
| 🔍 **File name search** | Instant, as you type |
| 📄 **Document content search** | Plain text, Markdown, code, and document formats (PDF, Word, Excel, PowerPoint) |
| 🖼️ **Screenshot / image OCR** | Text inside PNG/JPG/WebP/BMP images, fully offline (Windows.Media.Ocr) |
| 🈶 **Chinese word segmentation** | jieba + BM25 ranking, not trigrams — plus automatic GBK encoding detection |
| ⚡ **Incremental indexing** | File-watch during runtime, mtime/size reconciliation at startup |
| 🤖 **MCP server** | Exposes local search to AI agents over stdio |
| 🚀 **NTFS fast path** | MFT enumeration + USN Journal, admin-only, falls back transparently otherwise |

## dowse vs. the alternatives

| | dowse | Everything | Windows Search | sist2 |
|---|:---:|:---:|:---:|:---:|
| File name search | ✓ | ✓ | ✓ | ✓ |
| Document content search | ✓ (persistent index) | on-demand scan | depends on indexed locations and filters | ✓ |
| Screenshot / image OCR | ✓ | ✗ | device/version dependent | limited (optional Tesseract) |
| Proper Chinese segmentation | ✓ (jieba) | — | limited | ✗ (trigrams) |
| Fully local, no network | ✓ | ✓ | ✓ | ✓ |
| Global hotkey overlay | ✓ | ✓ | ✓ (Win key) | ✗ (web UI) |
| Windows-native | ✓ | ✓ | ✓ | ✗ (Linux-first, Docker on Windows) |

## Chinese text handling

- Word segmentation via jieba, ranking via BM25 (tantivy engine). No trigrams.
- Automatic file encoding detection (chardetng). GBK-encoded files are decoded correctly before indexing — this matters because a large share of Chinese-language documents on Windows, especially older ones, are still saved in GBK rather than UTF-8, and a search tool that assumes UTF-8 will silently mis-index or garble them.
- Multi-term queries default to AND semantics. Quoted phrase queries match on exact position. Inline operators narrow things down further: `path:reports`, `mtime:>2026-01-01`, `size:>10mb`, uppercase `OR` between groups, `-term` to exclude.
- OCR runs on the Windows-native engine (Windows.Media.Ocr), fully offline. The zh-Hans language pack also covers mixed Chinese/English text, no extra configuration required.

## Performance

Design targets; exceeding them is treated as a defect. "Measured" is a from-scratch
benchmark of `dowse 0.7.0` (i7-13700K / 24 logical cores / 64GB RAM, single machine,
single session, 2026-07-12), reusing the byte-identical corpus from the v0.6.1 round-3
benchmark for direct comparability. Full raw output (index/search logs, JSON result
files) is kept with the benchmark working directory, outside this repo.

| Metric | Design target | Measured (v0.7.0, 2026-07-12) |
|---|---|---|
| Hotkey to window visible | < 50ms | not measured — CLI-only benchmark, no overlay-app instrumentation |
| Keystroke to results rendered | < 80ms | not measured — same |
| OCR, single image | ~112ms / 1080p screenshot | ~170ms isolated (480×200 synthetic image), unchanged from v0.6.1 — the OCR pipeline was not modified this release. Sub-30ms readings on immediate repeat runs of the same image reflect OS-level recognition caching, not real recognition, and are excluded here. Not real 1080p screenshots |
| Resident memory | < 150MB idle | not measured (idle); peak working set during full-corpus indexing was ~327MB — a different metric, not a regression against the idle target |
| Installer size | < 50MB (revised 2026-07 — the original 15MB target predates the bundled CLI sidecar) | **14.98MB** (`dowse-app_0.9.0_x64-setup.exe`, published release) |
| Full-text index build, 10,000 files / 437MB | seconds (planned filename-only fast path) | 10.0–10.6s — current full-content `dowse index`, not the planned filename-only MFT path |
| Full-text index build + OCR, 15,100 files (incl. 5,100 images) | — | ~46.6s first pass, all 5,100/5,100 images OCR'd in that same pass — no pending, no second pass needed |
| Search latency, P50 (5 required categories) | — | 30.7–161.1ms across single word / Chinese phrase / English phrase / multi-word AND / zero-result, on a 15,100-document index |
| Search latency, P95 | — | 39.1–172.3ms, same 5 categories |
| `ext:` filter query latency | — | P50 155.6ms, same band as the non-zero-result query categories |
| Index size ÷ corpus size | — | 0.36 (text-only), down from 0.54 in the v0.6.1 round |

Full-corpus rows measured on the same 10,000-file / 437.66MB text corpus plus 5,100
synthetic 480×200 OCR images (89.8MB) used for the v0.6.1 round-3 numbers above —
byte-identical, reused directly rather than regenerated. Indexing is roughly 2x faster
and the on-disk text index roughly a third smaller than v0.6.1; both track the new
tokenizer (lowercase normalization, alphanumeric-boundary splitting of Latin words)
producing a leaner term dictionary. The zero-result query dropped from 135ms (v0.6.1)
to a startup-noise-level 31ms, consistent with less index to scan before concluding a
term is absent. OCR recognition speed is unchanged this release, since the pipeline was
not touched: single-image recognition stays around 170ms, and the sub-30ms readings on
repeated identical images are OS-level caching artifacts, not real recognition. The
full-corpus text-plus-OCR pass got faster (83s to 46.6s) from the quicker tokenizer and
write path, not from faster recognition.

## Quick start

**Download** — grab the installer from the [latest release](https://github.com/ltspace/dowse/releases/latest) (`dowse-app_*_x64-setup.exe`), run it, then `Alt+\`` to summon.

The installer is unsigned, so Windows SmartScreen will flag it on first run. To proceed, click **More info** and then **Run anyway**. A code-signing certificate is a recurring cost that is hard to justify for an independent project; it may be reconsidered for a future release.

**Install the CLI** — the library and command-line tool ship as one `dowse` package:

```powershell
cargo install dowse                 # once published to crates.io
cargo install --path crates/dowse   # from a local checkout
```

**Build from source:**

```powershell
git clone https://github.com/ltspace/dowse && cd dowse

# CLI
cargo run -p dowse -- index D:\docs      # build the index
cargo run -p dowse -- search 限流         # search
cargo run -p dowse -- search "精确短语"   # phrase query
cargo run -p dowse -- add E:\projects     # add another root incrementally (no full rebuild)
cargo run -p dowse -- rules show          # view index rules (excluded dirs, extra extensions, size cap)

# Overlay app (Tauri 2 + Svelte 5)
cd crates/dowse-app
npm install
cargo tauri build      # produces the installer under target/release/bundle
```

Overlay app: `Alt+\`` to summon, `↑↓` to select, `Enter` to open, `Ctrl+Enter` to reveal in Explorer, `Ctrl+C` to copy path, `Esc` to hide. Two nearly invisible dropdowns sit at the right of the search bar — file type filter (`Ctrl+P`) and sort order (`Ctrl+S`, relevance / newest / oldest / largest); both stay faint until you select a non-default value. Right-click a result row for a native Explorer-style context menu (open / reveal in folder / copy path / copy name). A pin toggle at the top-right keeps the window open when it loses focus (session-only, resets on restart). With an empty input, the overlay lists your recent searches (last 10, stored locally) — `↑↓`/`Enter` to reuse one, `Delete` to remove it. `Ctrl+,` opens the settings panel — general (hotkey re