commit 2778f145743392f19673615d03e3278567998839
Author: Breadway <plasticbread849@gmail.com>
Date:   Thu Jul 2 20:59:18 2026 +0800

    Initial commit

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..36f7f5b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,31 @@
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diff --git a/Cargo.lock b/Cargo.lock
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+ "proc-macro2",
+ "quote",
+ "syn",
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+ "zerofrom",
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+ "yoke",
+ "zerofrom",
+ "zerovec-derive",
+]
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+[[package]]
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+ "syn",
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+ "aes",
+ "arbitrary",
+ "bzip2",
+ "constant_time_eq",
+ "crc32fast",
+ "crossbeam-utils",
+ "deflate64",
+ "displaydoc",
+ "flate2",
+ "getrandom 0.3.4",
+ "hmac",
+ "indexmap",
+ "lzma-rs",
+ "memchr",
+ "pbkdf2",
+ "sha1",
+ "thiserror 2.0.18",
+ "time",
+ "xz2",
+ "zeroize",
+ "zopfli",
+ "zstd",
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+ "crc32fast",
+ "log",
+ "simd-adler32",
+]
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+ "zstd-safe",
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+[[package]]
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+ "zstd-sys",
+]
+
+[[package]]
+name = "zstd-sys"
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+ "cc",
+ "pkg-config",
+]
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000..a0b3ed4
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,3 @@
+[workspace]
+members = ["breadsearch-shared", "breadmill", "breadsearch"]
+resolver = "2"
diff --git a/DESIGN.md b/DESIGN.md
new file mode 100644
index 0000000..ce706f3
--- /dev/null
+++ b/DESIGN.md
@@ -0,0 +1,82 @@
+# breadsearch + breadmill — semantic system-wide search for BOS
+
+## Context
+BOS/bread has no content search — only breadbox's app launcher (exact/fuzzy over `.desktop` files). The goal is a **semantic** "find anything by meaning" engine: a flagship, differentiating BOS feature that's also the *right* workload for the AMD XDNA NPU (small encoder model, compute-bound single forward pass, always-on background embedding — none of the bandwidth-bound problems that make LLMs a bad NPU fit).
+
+Two components, mirroring the breadpad/breadman split and the breadbox/breadbox-sync precedent (GUI + background helper + shared lib in one repo):
+- **breadmill** — always-on daemon: walks files → extracts text → chunks → embeds → vector index; answers queries over a Unix socket. ("mill grain into flour.")
+- **breadsearch** — standalone GTK4 GUI, *forked from breadbox's UI*, that queries breadmill and shows ranked hits. ("sift the flour.")
+
+breadbox stays a pure app launcher, unchanged.
+
+## Decisions (confirmed with user)
+- **Index scope (v1):** curated roots — `~/Documents`, `~/Projects` (notes/docs, not code yet), `~/.config/breadpad`. Extract `md, txt, org, pdf, odt, docx`. Skip binaries/images/build dirs/`.git`.
+- **Repo layout:** one cargo workspace at `~/Projects/breadsearch/`.
+- **Embedding model:** `nomic-embed-text-v1.5` (768-dim ONNX, ~550MB). Requires task prefixes: `search_document: ` for indexed chunks, `search_query: ` for queries; mean-pool + L2-normalize.
+- **Compute:** CPU-first; NPU (XDNA via ONNX Runtime VitisAI EP) is a later backend swap, not a v1 dependency.
+
+## Workspace layout
+```
+~/Projects/breadsearch/
+  Cargo.toml              # [workspace] members = breadsearch-shared, breadmill, breadsearch
+  bakery.toml             # bread package manifest (binaries: breadsearch, breadmill)
+  config.example.toml     # ~/.config/breadsearch/config.toml template
+  README.md
+  breadsearch-shared/       # lib: XDG paths, config, IPC types + socket client
+  breadmill/              # daemon bin
+  breadsearch/              # GUI bin (forked breadbox UI)
+```
+
+## Component: breadsearch-shared (lib)
+Model on `breadbox/breadbox-shared/src/lib.rs` (XDG helpers + serde/toml config).
+- **Paths:** `config_dir()` → `~/.config/breadsearch`; `state_dir()` → `~/.local/state/breadsearch` (index); `cache_dir()` → `~/.cache/breadsearch` (models); `socket_path()` → `$XDG_RUNTIME_DIR/breadmill.sock`.
+- **Config** (serde + `toml`): `[index] roots, extensions, max_file_mb`; `[search] limit, snippet_len`; `[model] name, dim`.
+- **IPC types** (serde_json, newline-delimited JSON over the Unix socket):
+  - Request: `Query { query: String, limit: usize }`, `Status`, `Reindex`.
+  - Response: `Hits(Vec<Hit>)` where `Hit { title, path, snippet, score }`; `StatusInfo { indexed, pending, model_ready }`.
+- **Socket client** helper used by the GUI (connect, send, read one response).
+
+## Component: breadmill (daemon)
+Pipeline, isolated behind small traits so each stage is swappable:
+1. **Walk** — `ignore` crate (parallel, respects `.gitignore`) over configured roots; filter by extension + size.
+2. **Extract** — `md/txt/org`: read directly; `pdf`: `pdf-extract`; `docx/odt`: unzip + strip XML (`zip` + `quick-xml`), best-effort.
+3. **Chunk** — ~512-token windows with overlap; keep byte offsets for snippets.
+4. **Embed** — `Embedder` trait. v1 impl: `ort` (ONNX Runtime 2.x, CPU EP) + `tokenizers` (HF) running nomic-embed-text-v1.5. Apply `search_document:`/`search_query:` prefixes, mean-pool, normalize.
+5. **Store** — `rusqlite` for metadata (path, mtime, content-hash, chunk text/offsets) keyed by rowid + `usearch` (HNSW, 768-dim, cosine) for vectors keyed by the same id. Both persisted under `state_dir()`.
+6. **Incremental** — on start, diff roots against sqlite (mtime+hash): embed new/changed, drop deleted. Then live-watch with `notify` (debounced) to re-embed on change.
+7. **Serve** — `tokio` (or std threads) Unix-socket listener: `Query` → embed query → usearch top-k → join sqlite metadata → `Hits`. Also `Status`/`Reindex`.
+- **Model fetch:** first run downloads `model.onnx` + `tokenizer.json` from HF into `cache_dir()/models/` (needs network once); `breadmill --fetch-model` to pre-fetch. Log clearly if absent.
+- **Lifecycle:** systemd **user** service `breadmill.service` (pattern from `breadbox-sync.service` / breadd), `WantedBy=default.target`.
+
+## Component: breadsearch (GUI) — fork of breadbox
+Start from `breadbox/breadbox/src/main.rs`. **Reuse verbatim:** the gtk4-layer-shell overlay window (rename namespace/app-id to `breadsearch` / `com.breadway.breadsearch`), `SearchEntry` + `ScrolledWindow` + `ListBox`, ↑/↓/Enter/Esc handling, click-outside-to-close, PID-toggle (`breadsearch.pid`), and the theming path: `bread_theme::gtk::apply_shared()` + `apply_app_css(|| build_css(&load_palette()))` + user `style.css`. Pin `bread-theme` git tag `v0.2.8`, feature `gtk` (same as breadbox).
+**Swap:**
+- **Result source:** delete `load_sorted_entries`/`fuzzy_*`/`DesktopEntry`. On `search.connect_changed`, **debounce ~150ms** (`glib::timeout_add_local`) then query breadmill **off the UI thread** (`std::thread` + `glib::MainContext::channel`), clear the `ListBox`, append a row per `Hit`.
+- **Row content:** title (filename/heading) + muted path + snippet line; filetype icon via `gio::content_type_guess` → `Image::from_gicon`. Extend `build_css` with a `.hit-snippet` class.
+- **Action:** replace `do_launch` with open-file — `Enter`/row-activated → `xdg-open <path>`; `Ctrl+Enter` → open containing folder. Then close.
+
+## Key crates
+`ort` (ONNX Runtime), `tokenizers`, `usearch`, `rusqlite`, `ignore`, `notify`, `pdf-extract`, `zip`+`quick-xml`, `serde`/`serde_json`/`toml`, `gtk4` 0.11 + `gtk4-layer-shell` 0.8, `bread-theme` (git tag v0.2.8).
+
+## Packaging & BOS integration (last phase — post-1.0, per earlier decision)
+- `bakery.toml` (model on `breadbox/bakery.toml`): `binaries = ["breadsearch","breadmill"]`, system_deps for onnxruntime/gtk; `[[service]] unit="breadmill.service" enable=true`; `[config] dir="~/.config/breadsearch"`.
+- BOS: add `breadsearch`+`breadmill` to `build-local.sh` `BREAD_BINS`; autostart `breadmill.service`; Hyprland keybind (e.g. `SUPER+slash`) → `breadsearch` in the skel `hyprland.lua`.
+- Release: dual remotes (origin GitHub + forgejo), bakery index regen — per the bread release train.
+
+## Phasing (de-risked: ship CPU, NPU later)
+1. Scaffold workspace + `breadsearch-shared` (paths, config, IPC types, socket client).
+2. `breadmill` CPU pipeline end-to-end (walk→extract→chunk→embed→store→serve) + `--reindex`/`--fetch-model` + systemd unit.
+3. `breadsearch` GUI fork (socket query + xdg-open + theme).
+4. Packaging (bakery, config.example, README) + BOS wiring.
+5. **Later:** NPU `Embedder` impl (ort VitisAI/XDNA EP) — the go/no-go POC; pure backend swap.
+
+## Verification
+- `breadmill --fetch-model` then `--reindex` over a small test corpus; log embedded-chunk count; confirm `state_dir` index persists across restart.
+- Query the socket directly (a `breadmill query "..."` subcommand or `socat`) and confirm semantically-relevant hits with sane scores for a concept query (not keyword).
+- Launch `breadsearch`, type a *concept* (e.g. "tax stuff", "that suspend bug fix"), see relevant files ranked, `Enter` opens via xdg-open, `Ctrl+Enter` reveals folder, `Esc` closes; theme matches breadbox; hot-reloads on `bread-theme reload`.
+- Edit/add/delete a file in a root → `notify` re-index → new content findable within seconds.
+
+## Notes / risks
+- nomic prefixes + mean-pool + normalize must match between index and query or recall collapses.
+- `ort` linking: prefer the crate's downloaded/bundled ONNX Runtime to avoid version skew with Arch's `onnxruntime`.
+- Office formats (docx/odt) are best-effort in v1; md/txt/org/pdf are the reliable path.
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..373e4ee
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Breadway
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..19d1fee
--- /dev/null
+++ b/README.md
@@ -0,0 +1,134 @@
+# breadsearch
+
+Semantic document search for Bread OS. Type a concept — not a keyword — and get ranked hits from your documents.
+
+Two binaries in one Cargo workspace:
+
+- **breadmill** — background daemon. Walks configured directories, extracts text, chunks and embeds documents with [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) (768-dim ONNX), stores vectors in an HNSW index (usearch) backed by SQLite metadata, and serves queries over a Unix socket. Watches for filesystem changes and re-indexes incrementally.
+- **breadsearch** — GTK4 overlay GUI. Queries breadmill via the Unix socket and shows ranked results. Press Enter to open a file, Ctrl+Enter to reveal its folder, Esc to close. Bind it to a hotkey (e.g. Super+/) and invoke it as a toggle.
+
+## Build
+
+System dependencies: `gtk4`, `gtk4-layer-shell`, `librsvg`.
+
+```
+cargo build --release
+```
+
+The `ort` crate downloads a bundled ONNX Runtime at build time (no system `onnxruntime` needed for the default CPU build).
+
+Optional features:
+
+| Feature | What it adds |
+|---------|-------------|
+| `npu`   | AMD XDNA NPU via VitisAI ONNX Runtime EP (requires Ryzen AI SDK) |
+| `rocm`  | AMD iGPU via ROCm ONNX Runtime EP |
+
+```
+# NPU build
+cargo build --release -p breadmill --features npu
+```
+
+## Setup
+
+**1. Fetch the embedding model** (~550 MB, downloaded once from Hugging Face):
+
+```
+breadmill fetch-model
+```
+
+Model files are stored in `~/.cache/breadsearch/models/`.
+
+**2. Enable the systemd user service:**
+
+```
+cp packaging/breadmill.service ~/.config/systemd/user/
+systemctl --user daemon-reload
+systemctl --user enable --now breadmill
+```
+
+Or run it directly: `breadmill serve` (or just `breadmill`).
+
+**3. Copy the example config** (optional — built-in defaults are used otherwise):
+
+```
+mkdir -p ~/.config/breadsearch
+cp config.example.toml ~/.config/breadsearch/config.toml
+```
+
+## breadsearch (GUI)
+
+```
+breadsearch
+```
+
+Invoking again while running closes the window (PID-toggle). Bind to a hotkey in your compositor config.
+
+Results show: filename/title, full path, and a snippet from the matching chunk. Score is cosine similarity as a percentage.
+
+User CSS overrides go in `~/.config/breadsearch/style.css`.
+
+## breadmill (daemon / CLI)
+
+```
+# Start the daemon (also the default when called with no arguments)
+breadmill serve
+
+# Force a full re-index from scratch
+breadmill reindex
+
+# Download model files
+breadmill fetch-model
+
+# Query from the terminal
+breadmill query "tax stuff"
+
+# Show daemon status (chunks indexed, pending, model ready)
+breadmill status
+
+# Backend flags (requires the matching Cargo feature)
+breadmill --npu
+breadmill --rocm
+```
+
+## Config
+
+`~/.config/breadsearch/config.toml` — all keys are optional; built-in defaults are shown.
+
+```toml
+[index]
+roots        = ["~/Documents", "~/Projects", "~/.config/breadpad"]
+extensions   = ["md", "txt", "org", "pdf", "odt", "docx"]
+excludes     = []          # paths to skip (prefix match)
+max_file_mb  = 10.0
+
+[search]
+limit        = 10          # max results per query
+snippet_len  = 200         # max characters in result snippet
+
+[model]
+name         = "nomic-embed-text-v1.5"
+dim          = 768
+backend      = "cpu"       # "cpu", "npu", or "rocm"
+```
+
+`roots` and `excludes` support `~/` expansion. The index respects `.gitignore` files found during the walk.
+
+### NPU backend
+
+Set `backend = "npu"` in config (or pass `--npu`) when running a build compiled with `--features npu`. breadmill looks for the VitisAI EP config file in this order:
+
+1. `$VAIP_CONFIG`
+2. `~/.config/breadsearch/vaip_config.json`
+3. `~/.local/share/ryzen-ai-1.7.1/voe-4.0-linux_x86_64/vaip_config.json`
+4. `/etc/vaip_config.json`
+5. `/opt/xilinx/vaip_config.json`
+
+## Runtime paths
+
+| Purpose | Path |
+|---------|------|
+| Config  | `~/.config/breadsearch/` |
+| Index (SQLite + HNSW) | `~/.local/state/breadsearch/` |
+| Model cache | `~/.cache/breadsearch/models/` |
+| Unix socket | `$XDG_RUNTIME_DIR/breadmill.sock` |
diff --git a/bakery.toml b/bakery.toml
new file mode 100644
index 0000000..8b6e1ed
--- /dev/null
+++ b/bakery.toml
@@ -0,0 +1,18 @@
+name = "breadsearch"
+description = "Semantic system-wide search for BOS"
+binaries = ["breadsearch", "breadmill"]
+system_deps = ["gtk4", "gtk4-layer-shell", "librsvg"]
+bread_deps = []
+
+[[service]]
+unit = "breadmill.service"
+enable = true
+
+[config]
+dir = "~/.config/breadsearch"
+example = "config.example.toml"
+
+[install]
+post_install = [
+    "systemctl --user start breadmill.service 2>/dev/null || breadmill &",
+]
diff --git a/benchmark_embed.py b/benchmark_embed.py
new file mode 100644
index 0000000..dbd6f34
--- /dev/null
+++ b/benchmark_embed.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""
+Benchmark CPU (float32) vs CPU (int8 quantized) vs VitisAI (int8 quantized static).
+"""
+
+import os
+import time
+import numpy as np
+from pathlib import Path
+
+FLOAT32_MODEL = Path.home() / ".cache/breadsearch/models/model.onnx"
+QUANT_MODEL   = Path.home() / ".cache/breadsearch/models/model_quantized.onnx"
+STATIC_MODEL  = Path.home() / ".cache/breadsearch/models/model_quantized_static.onnx"
+CACHE_DIR     = Path.home() / ".cache/breadsearch/npu/nomic-quantized-static"
+VAIP_CONFIG   = Path.home() / ".config/breadsearch/vaip_config.json"
+RYZEN_AI_LIB  = Path.home() / ".local/share/ryzen-ai-1.7.1/lib"
+
+os.environ["RYZEN_AI_INSTALLATION_PATH"] = str(RYZEN_AI_LIB)
+os.environ["LD_LIBRARY_PATH"] = str(RYZEN_AI_LIB) + ":" + os.environ.get("LD_LIBRARY_PATH", "")
+
+import onnxruntime as ort
+
+WARMUP = 2
+RUNS   = 10
+SEQ_FLOAT = 512    # dynamic model accepts any seq len
+SEQ_STATIC = 512   # static model locked to this
+
+def make_input(seq_len: int):
+    ids   = np.ones((1, seq_len), dtype=np.int64)
+    mask  = np.ones((1, seq_len), dtype=np.int64)
+    types = np.zeros((1, seq_len), dtype=np.int64)
+    return {"input_ids": ids, "token_type_ids": types, "attention_mask": mask}
+
+def time_session(sess: ort.InferenceSession, feed: dict, n: int) -> list[float]:
+    times = []
+    for _ in range(n):
+        t0 = time.perf_counter()
+        sess.run(None, feed)
+        times.append(time.perf_counter() - t0)
+    return times
+
+def stats(times):
+    arr = np.array(times)
+    return arr.mean(), arr.min(), arr.max()
+
+print("=" * 60)
+print("BENCHMARK: nomic-embed-text-v1.5 embedding speed")
+print("=" * 60)
+
+# ── 1. float32 CPU ────────────────────────────────────────────
+print("\n[1] float32 model — CPU EP")
+sess = ort.InferenceSession(str(FLOAT32_MODEL), providers=["CPUExecutionProvider"])
+feed = make_input(SEQ_FLOAT)
+for _ in range(WARMUP): sess.run(None, feed)
+times = time_session(sess, feed, RUNS)
+mean, lo, hi = stats(times)
+print(f"    seq={SEQ_FLOAT}  mean={mean*1000:.0f}ms  min={lo*1000:.0f}ms  max={hi*1000:.0f}ms  ({RUNS} runs)")
+
+# ── 2. int8 quantized CPU (dynamic) ───────────────────────────
+print("\n[2] int8 quantized model (dynamic shapes) — CPU EP")
+sess = ort.InferenceSession(str(QUANT_MODEL), providers=["CPUExecutionProvider"])
+feed = make_input(SEQ_FLOAT)
+for _ in range(WARMUP): sess.run(None, feed)
+times = time_session(sess, feed, RUNS)
+mean2, lo2, hi2 = stats(times)
+print(f"    seq={SEQ_FLOAT}  mean={mean2*1000:.0f}ms  min={lo2*1000:.0f}ms  max={hi2*1000:.0f}ms  ({RUNS} runs)")
+print(f"    Speedup vs float32: {mean/mean2:.2f}x")
+
+# ── 3. int8 quantized static — VitisAI EP ─────────────────────
+print("\n[3] int8 quantized model (static shapes) — VitisAI EP (NPU+CPU)")
+providers = [
+    ("VitisAIExecutionProvider", {
+        "config_file": str(VAIP_CONFIG),
+        "cacheDir": str(CACHE_DIR),
+        "cacheKey": "nomic-quantized-static",
+    }),
+    "CPUExecutionProvider",
+]
+print("    Loading session (should be fast — already compiled)...")
+t_load = time.perf_counter()
+sess_npu = ort.InferenceSession(str(STATIC_MODEL), providers=providers)
+print(f"    Load time: {time.perf_counter()-t_load:.1f}s")
+feed_static = make_input(SEQ_STATIC)
+for _ in range(WARMUP): sess_npu.run(None, feed_static)
+times_npu = time_session(sess_npu, feed_static, RUNS)
+mean3, lo3, hi3 = stats(times_npu)
+print(f"    seq={SEQ_STATIC}  mean={mean3*1000:.0f}ms  min={lo3*1000:.0f}ms  max={hi3*1000:.0f}ms  ({RUNS} runs)")
+print(f"    Speedup vs float32: {mean/mean3:.2f}x")
+print(f"    Speedup vs int8 CPU: {mean2/mean3:.2f}x")
+
+print("\n" + "=" * 60)
+print("SUMMARY")
+print(f"  float32 CPU     : {mean*1000:.0f}ms/inference")
+print(f"  int8 CPU        : {mean2*1000:.0f}ms/inference  ({mean/mean2:.2f}x)")
+print(f"  int8 VitisAI    : {mean3*1000:.0f}ms/inference  ({mean/mean3:.2f}x)")
+print("=" * 60)
diff --git a/breadmill/Cargo.toml b/breadmill/Cargo.toml
new file mode 100644
index 0000000..91b4096
--- /dev/null
+++ b/breadmill/Cargo.toml
@@ -0,0 +1,55 @@
+[package]
+name = "breadmill"
+version = "0.1.0"
+edition = "2021"
+license = "MIT"
+
+[features]
+default = []
+npu = ["ort/vitis", "ort/load-dynamic"]
+rocm = ["ort/rocm", "ort/load-dynamic"]
+
+[[bin]]
+name = "breadmill"
+path = "src/main.rs"
+
+[dependencies]
+breadsearch-shared = { path = "../breadsearch-shared" }
+
+# Embedding: ONNX Runtime + HF tokenizers
+# download-binaries: fetches the MLAS-optimized ORT 1.24.x at build time (CPU default).
+# api-23: compatible with both the downloaded ORT 1.24.x and the Ryzen AI SDK ORT 1.23.3;
+#   ORT 1.24 is backwards-compatible and honours GetApi(23) requests.
+# npu feature adds load-dynamic + vitis: dlopen at runtime lets ORT_DYLIB_PATH redirect
+#   to the Ryzen AI SDK ORT; rpath from download-binaries means no ORT_DYLIB_PATH
+#   needed for the plain CPU path even in the npu build.
+ort = { version = "2.0.0-rc.12", default-features = false, features = ["std", "tracing", "download-binaries", "tls-native", "copy-dylibs", "api-23"] }
+tokenizers = "0"
+
+# Vector index
+usearch = "2"
+
+# Metadata store
+rusqlite = { version = "0", features = ["bundled"] }
+
+# File walking (respects .gitignore)
+ignore = "0"
+
+# Live filesystem watching
+notify = "6"
+
+# Text extraction
+pdf-extract = "0"
+zip = "2"
+quick-xml = { version = "0", features = ["serialize"] }
+
+# Hashing
+sha2 = "0"
+hex = "0"
+
+# HTTP for model download
+ureq = "2"
+
+# Serialization
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
diff --git a/breadmill/src/chunk.rs b/breadmill/src/chunk.rs
new file mode 100644
index 0000000..dcddca8
--- /dev/null
+++ b/breadmill/src/chunk.rs
@@ -0,0 +1,142 @@
+pub struct Chunk {
+    pub text: String,
+    pub start: usize,
+    pub end: usize,
+}
+
+/// Split `text` into overlapping word-based windows, then enforce `max_chunk_chars`.
+///
+/// Any word-window that exceeds `max_chunk_chars` characters is split further at
+/// character boundaries so that no chunk passed to the embedder is pathologically
+/// large (e.g. minified JSON where a single "word" is hundreds of KB).
+///
+/// Set `max_chunk_chars = 0` to skip the character cap.
+pub fn chunk_text(text: &str, words_per_chunk: usize, overlap_words: usize, max_chunk_chars: usize) -> Vec<Chunk> {
+    let word_chunks = chunk_by_words(text, words_per_chunk, overlap_words);
+
+    if max_chunk_chars == 0 {
+        return word_chunks;
+    }
+
+    let mut result = Vec::new();
+    for chunk in word_chunks {
+        if chunk.text.len() <= max_chunk_chars {
+            result.push(chunk);
+        } else {
+            result.extend(split_by_chars(chunk, max_chunk_chars));
+        }
+    }
+    result
+}
+
+fn chunk_by_words(text: &str, words_per_chunk: usize, overlap_words: usize) -> Vec<Chunk> {
+    let mut positions: Vec<(usize, usize)> = Vec::new();
+    let mut in_word = false;
+    let mut word_start = 0;
+
+    for (i, c) in text.char_indices() {
+        if c.is_whitespace() {
+            if in_word {
+                positions.push((word_start, i));
+                in_word = false;
+            }
+        } else if !in_word {
+            word_start = i;
+            in_word = true;
+        }
+    }
+    if in_word {
+        positions.push((word_start, text.len()));
+    }
+
+    if positions.is_empty() {
+        return Vec::new();
+    }
+
+    let step = words_per_chunk.saturating_sub(overlap_words).max(1);
+    let mut chunks = Vec::new();
+    let mut i = 0;
+
+    while i < positions.len() {
+        let last = (i + words_per_chunk - 1).min(positions.len() - 1);
+        let start = positions[i].0;
+        let end = positions[last].1;
+
+        chunks.push(Chunk {
+            text: text[start..end].to_string(),
+            start,
+            end,
+        });
+
+        if last == positions.len() - 1 {
+            break;
+        }
+        i += step;
+    }
+
+    chunks
+}
+
+fn split_by_chars(chunk: Chunk, max_chars: usize) -> Vec<Chunk> {
+    let text = &chunk.text;
+    let mut result = Vec::new();
+    let mut seg_start = 0usize;
+    let mut count = 0usize;
+
+    for (byte_idx, _) in text.char_indices() {
+        if count > 0 && count % max_chars == 0 {
+            result.push(Chunk {
+                text: text[seg_start..byte_idx].to_string(),
+                start: chunk.start + seg_start,
+                end: chunk.start + byte_idx,
+            });
+            seg_start = byte_idx;
+        }
+        count += 1;
+    }
+    if seg_start < text.len() {
+        result.push(Chunk {
+            text: text[seg_start..].to_string(),
+            start: chunk.start + seg_start,
+            end: chunk.end,
+        });
+    }
+    result
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn basic_chunk() {
+        let text = "one two three four five six seven eight nine ten";
+        let chunks = chunk_text(text, 4, 1, 0);
+        assert!(!chunks.is_empty());
+        for c in &chunks {
+            assert!(!c.text.is_empty());
+            assert!(c.start <= c.end);
+            assert_eq!(&text[c.start..c.end], c.text);
+        }
+    }
+
+    #[test]
+    fn char_cap_splits_large_chunks() {
+        // Simulate a "word" that is 200 chars long — exceeds cap of 50.
+        let text = "a".repeat(200);
+        let chunks = chunk_text(&text, 1, 0, 50);
+        assert_eq!(chunks.len(), 4);
+        for c in &chunks {
+            assert!(c.text.len() <= 50);
+            assert_eq!(&text[c.start..c.end], c.text);
+        }
+    }
+
+    #[test]
+    fn char_cap_disabled() {
+        let text = "a".repeat(200);
+        let chunks = chunk_text(&text, 1, 0, 0);
+        assert_eq!(chunks.len(), 1);
+        assert_eq!(chunks[0].text.len(), 200);
+    }
+}
diff --git a/breadmill/src/embed.rs b/breadmill/src/embed.rs
new file mode 100644
index 0000000..9287518
--- /dev/null
+++ b/breadmill/src/embed.rs
@@ -0,0 +1,258 @@
+use std::path::{Path, PathBuf};
+
+use ort::{
+    session::{Session, builder::{GraphOptimizationLevel, SessionBuilder}},
+    value::Tensor,
+};
+use tokenizers::Tokenizer;
+
+const DOCUMENT_PREFIX: &str = "search_document: ";
+const QUERY_PREFIX: &str = "search_query: ";
+
+/// Hard token cap for nomic-embed-text-v1.5 (8192-token context window).
+/// Sequences longer than this are truncated before ONNX inference to prevent
+/// quadratic attention memory blowup.
+pub const MAX_SEQ_LEN: usize = 8192;
+
+pub enum Backend {
+    Cpu,
+    /// AMD XDNA NPU via the VitisAI ONNX Runtime execution provider.
+    /// `cache_dir` is used to store the compiled NPU model between runs.
+    Npu { cache_dir: PathBuf },
+    /// AMD iGPU via the ROCm ONNX Runtime execution provider.
+    Rocm,
+}
+
+pub struct OrtEmbedder {
+    session: Session,
+    tokenizer: Tokenizer,
+    dim: usize,
+}
+
+impl OrtEmbedder {
+    pub fn load(model_path: &Path, tokenizer_path: &Path, dim: usize, backend: Backend) -> Result<Self, String> {
+        let builder = Session::builder()
+            .map_err(|e| e.to_string())?
+            .with_optimization_level(GraphOptimizationLevel::All)
+            .map_err(|e| e.to_string())?;
+
+        let mut builder = configure_eps(builder, &backend)?;
+        let session = builder.commit_from_file(model_path).map_err(|e| e.to_string())?;
+        let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(|e| e.to_string())?;
+
+        Ok(Self { session, tokenizer, dim })
+    }
+
+    pub fn embed_document(&mut self, text: &str) -> Result<Vec<f32>, String> {
+        self.embed_with_prefix(text, DOCUMENT_PREFIX)
+    }
+
+    pub fn embed_query(&mut self, text: &str) -> Result<Vec<f32>, String> {
+        self.embed_with_prefix(text, QUERY_PREFIX)
+    }
+
+    fn embed_with_prefix(&mut self, text: &str, prefix: &str) -> Result<Vec<f32>, String> {
+        let input = format!("{}{}", prefix, text);
+
+        let encoding = self
+            .tokenizer
+            .encode(input, true)
+            .map_err(|e| e.to_string())?;
+
+        let mut ids: Vec<i64> = encoding.get_ids().iter().map(|&x| x as i64).collect();
+        let mut mask: Vec<i64> = encoding
+            .get_attention_mask()
+            .iter()
+            .map(|&x| x as i64)
+            .collect();
+        let mut type_ids: Vec<i64> = encoding
+            .get_type_ids()
+            .iter()
+            .map(|&x| x as i64)
+            .collect();
+
+        if ids.len() > MAX_SEQ_LEN {
+            eprintln!(
+                "breadmill: truncating {} tokens to {} (chunk too large)",
+                ids.len(),
+                MAX_SEQ_LEN
+            );
+            ids.truncate(MAX_SEQ_LEN);
+            mask.truncate(MAX_SEQ_LEN);
+            type_ids.truncate(MAX_SEQ_LEN);
+        }
+
+        let seq_len = ids.len() as i64;
+
+        let id_tensor =
+            Tensor::<i64>::from_array((vec![1i64, seq_len], ids.clone())).map_err(|e| e.to_string())?;
+        let mask_tensor =
+            Tensor::<i64>::from_array((vec![1i64, seq_len], mask.clone())).map_err(|e| e.to_string())?;
+        let type_tensor =
+            Tensor::<i64>::from_array((vec![1i64, seq_len], type_ids)).map_err(|e| e.to_string())?;
+
+        let outputs = self
+            .session
+            .run(ort::inputs! {
+                "input_ids" => id_tensor,
+                "attention_mask" => mask_tensor,
+                "token_type_ids" => type_tensor,
+            })
+            .map_err(|e| e.to_string())?;
+
+        // last_hidden_state: shape [1, seq_len, dim]
+        let (shape, data) = outputs["last_hidden_state"]
+            .try_extract_tensor::<f32>()
+            .map_err(|e| e.to_string())?;
+
+        let actual_seq = shape[1] as usize;
+        let actual_dim = shape[2] as usize;
+
+        // Mean-pool over non-padding positions
+        let mut result = vec![0.0f32; actual_dim];
+        let mut count = 0usize;
+
+        for t in 0..actual_seq {
+            if mask[t] > 0 {
+                for d in 0..actual_dim {
+                    result[d] += data[t * actual_dim + d];
+                }
+                count += 1;
+            }
+        }
+
+        if count > 0 {
+            for x in &mut result {
+                *x /= count as f32;
+            }
+        }
+
+        l2_normalize(&mut result);
+
+        // Clamp/pad to configured dim
+        result.truncate(self.dim);
+        while result.len() < self.dim {
+            result.push(0.0);
+        }
+
+        Ok(result)
+    }
+}
+
+fn l2_normalize(v: &mut Vec<f32>) {
+    let norm: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
+    if norm > 1e-10 {
+        for x in v.iter_mut() {
+            *x /= norm;
+        }
+    }
+}
+
+// ---- Execution provider selection -------------------------------------------
+
+fn configure_eps(builder: SessionBuilder, backend: &Backend) -> Result<SessionBuilder, String> {
+    match backend {
+        Backend::Cpu => Ok(builder),
+        Backend::Npu { cache_dir } => npu_session(builder, cache_dir),
+        Backend::Rocm => rocm_session(builder),
+    }
+}
+
+#[cfg(feature = "npu")]
+fn npu_session(builder: SessionBuilder, cache_dir: &Path) -> Result<SessionBuilder, String> {
+    let vitis_ep = build_vitis_ep(cache_dir)?;
+    eprintln!("breadmill: using NPU (VitisAI) execution provider");
+    if std::env::var("ORT_DYLIB_PATH").is_err() {
+        eprintln!(
+            "breadmill: hint — set ORT_DYLIB_PATH to the Ryzen AI SDK ORT, e.g.:\n  \
+             ORT_DYLIB_PATH=~/.local/share/ryzen-ai-1.7.1/lib/libonnxruntime.so"
+        );
+    }
+    builder
+        .with_execution_providers([vitis_ep, ort::ep::CPU::default().build()])
+        .map_err(|e| e.to_string())
+}
+
+#[cfg(not(feature = "npu"))]
+fn npu_session(builder: SessionBuilder, _cache_dir: &Path) -> Result<SessionBuilder, String> {
+    eprintln!("breadmill: NPU backend requested but not compiled in (rebuild with --features npu); using CPU");
+    Ok(builder)
+}
+
+// ---- VitisAI EP (NPU) -------------------------------------------------------
+
+#[cfg(feature = "npu")]
+fn build_vitis_ep(cache_dir: &Path) -> Result<ort::ep::ExecutionProviderDispatch, String> {
+    let vaip_config = find_vaip_config()?;
+    let npu_cache = cache_dir.join("npu");
+    std::fs::create_dir_all(&npu_cache).map_err(|e| e.to_string())?;
+    eprintln!("breadmill: vaip_config: {}", vaip_config.display());
+    eprintln!("breadmill: NPU model cache: {}", npu_cache.display());
+    Ok(ort::ep::Vitis::default()
+        .with_config_file(vaip_config.to_string_lossy())
+        .with_cache_dir(npu_cache.to_string_lossy())
+        .with_cache_key("nomic-embed-text-v1.5")
+        .build())
+}
+
+// ---- ROCm EP (AMD iGPU) -----------------------------------------------------
+
+#[cfg(feature = "rocm")]
+fn rocm_session(builder: SessionBuilder) -> Result<SessionBuilder, String> {
+    eprintln!("breadmill: using ROCm execution provider (device 0)");
+    builder
+        .with_execution_providers([
+            ort::execution_providers::ROCmExecutionProvider::default().build(),
+            ort::ep::CPU::default().build(),
+        ])
+        .map_err(|e| e.to_string())
+}
+
+#[cfg(not(feature = "rocm"))]
+fn rocm_session(builder: SessionBuilder) -> Result<SessionBuilder, String> {
+    eprintln!("breadmill: ROCm backend requested but not compiled in (rebuild with --features rocm); using CPU");
+    Ok(builder)
+}
+
+/// Locate the VitisAI EP config file required by the AMD Ryzen AI SDK.
+///
+/// Search order:
+/// 1. `VAIP_CONFIG` environment variable
+/// 2. `~/.config/breadsearch/vaip_config.json`
+/// 3. `/etc/vaip_config.json`
+/// 4. `/opt/xilinx/vaip_config.json`
+#[cfg(feature = "npu")]
+fn find_vaip_config() -> Result<PathBuf, String> {
+    if let Ok(p) = std::env::var("VAIP_CONFIG") {
+        let path = PathBuf::from(&p);
+        if path.exists() {
+            return Ok(path);
+        }
+        return Err(format!("VAIP_CONFIG={p} does not exist"));
+    }
+
+    let user_path = breadsearch_shared::config_dir().join("vaip_config.json");
+    if user_path.exists() {
+        return Ok(user_path);
+    }
+
+    // Standard system / SDK paths (checked in priority order)
+    let home = std::env::var("HOME").unwrap_or_default();
+    let sdk_paths = [
+        format!("{home}/.local/share/ryzen-ai-1.7.1/voe-4.0-linux_x86_64/vaip_config.json"),
+        "/etc/vaip_config.json".into(),
+        "/opt/xilinx/vaip_config.json".into(),
+    ];
+    for p in &sdk_paths {
+        let path = Path::new(p.as_str());
+        if path.exists() {
+            return Ok(path.to_path_buf());
+        }
+    }
+
+    Err(
+        "vaip_config.json not found; set VAIP_CONFIG=/path/to/vaip_config.json, \
+         copy to ~/.config/breadsearch/vaip_config.json, or install the AMD Ryzen AI SDK"
+            .into(),
+    )
+}
diff --git a/breadmill/src/extract.rs b/breadmill/src/extract.rs
new file mode 100644
index 0000000..c0d7811
--- /dev/null
+++ b/breadmill/src/extract.rs
@@ -0,0 +1,84 @@
+use std::{
+    fs, io::Read, path::Path,
+};
+
+pub type ExtractResult = Result<String, String>;
+
+/// Extract plain text from a file based on its extension.
+/// Returns Err on hard failures; Err with message if format unsupported.
+pub fn extract(path: &Path) -> ExtractResult {
+    let ext = path
+        .extension()
+        .and_then(|e| e.to_str())
+        .unwrap_or("")
+        .to_lowercase();
+
+    match ext.as_str() {
+        "md" | "txt" | "org" => read_text(path),
+        "pdf" => extract_pdf(path),
+        "docx" => extract_docx(path),
+        "odt" => extract_odt(path),
+        other => Err(format!("unsupported extension: {}", other)),
+    }
+}
+
+fn read_text(path: &Path) -> ExtractResult {
+    fs::read_to_string(path).map_err(|e| e.to_string())
+}
+
+fn extract_pdf(path: &Path) -> ExtractResult {
+    // pdf-extract panics on some malformed PDFs; catch_unwind prevents indexer thread death.
+    let path = path.to_path_buf();
+    match std::panic::catch_unwind(|| pdf_extract::extract_text(&path)) {
+        Ok(result) => result.map_err(|e| e.to_string()),
+        Err(_) => Err("pdf-extract panicked on malformed content stream".into()),
+    }
+}
+
+fn extract_docx(path: &Path) -> ExtractResult {
+    extract_office_xml(path, "word/document.xml", "w:t")
+}
+
+fn extract_odt(path: &Path) -> ExtractResult {
+    extract_office_xml(path, "content.xml", "text:p")
+}
+
+/// Open a zip-based office format and concatenate text from the named XML entry.
+/// We grab all Text events as a best-effort extraction.
+fn extract_office_xml(path: &Path, xml_entry: &str, _tag_hint: &str) -> ExtractResult {
+    let file = fs::File::open(path).map_err(|e| e.to_string())?;
+    let mut archive = zip::ZipArchive::new(file).map_err(|e| e.to_string())?;
+
+    let mut xml_bytes = Vec::new();
+    archive
+        .by_name(xml_entry)
+        .map_err(|e| format!("entry '{}' not found: {}", xml_entry, e))?
+        .read_to_end(&mut xml_bytes)
+        .map_err(|e| e.to_string())?;
+
+    let xml_str = String::from_utf8_lossy(&xml_bytes);
+    let mut reader = quick_xml::Reader::from_str(&xml_str);
+    reader.config_mut().trim_text(true);
+
+    let mut text = String::new();
+    let mut buf = Vec::new();
+
+    loop {
+        match reader.read_event_into(&mut buf) {
+            Ok(quick_xml::events::Event::Text(e)) => {
+                if let Ok(s) = e.decode() {
+                    if !text.is_empty() {
+                        text.push(' ');
+                    }
+                    text.push_str(&s);
+                }
+            }
+            Ok(quick_xml::events::Event::Eof) => break,
+            Err(e) => return Err(e.to_string()),
+            _ => {}
+        }
+        buf.clear();
+    }
+
+    Ok(text)
+}
diff --git a/breadmill/src/indexer.rs b/breadmill/src/indexer.rs
new file mode 100644
index 0000000..3ad20b5
--- /dev/null
+++ b/breadmill/src/indexer.rs
@@ -0,0 +1,414 @@
+use std::{
+    collections::{HashMap, HashSet},
+    fs,
+    path::{Path, PathBuf},
+    sync::{Arc, Mutex, atomic::{AtomicBool, AtomicUsize, Ordering}},
+    time::{Duration, Instant, UNIX_EPOCH},
+};
+
+use ignore::WalkBuilder;
+use notify::{RecommendedWatcher, RecursiveMode, Watcher, EventKind};
+use sha2::{Digest, Sha256};
+
+use crate::{embed::OrtEmbedder, extract, chunk, power, store::Store};
+
+pub struct SharedState {
+    pub store: Mutex<Store>,
+    pub embedder: Mutex<Option<OrtEmbedder>>,
+    pub model_ready: AtomicBool,
+    pub indexed: AtomicUsize,
+    pub pending: AtomicUsize,
+    pub reindex_signal: AtomicBool,
+}
+
+impl SharedState {
+    pub fn new(store: Store) -> Self {
+        SharedState {
+            store: Mutex::new(store),
+            embedder: Mutex::new(None),
+            model_ready: AtomicBool::new(false),
+            indexed: AtomicUsize::new(0),
+            pending: AtomicUsize::new(0),
+            reindex_signal: AtomicBool::new(false),
+        }
+    }
+}
+
+pub struct Indexer {
+    state: Arc<SharedState>,
+    config: breadsearch_shared::Config,
+    state_dir: PathBuf,
+}
+
+impl Indexer {
+    pub fn new(state: Arc<SharedState>, config: breadsearch_shared::Config, state_dir: PathBuf) -> Self {
+        Indexer { state, config, state_dir }
+    }
+
+    pub fn run(self) {
+        self.initial_scan();
+        self.watch_loop();
+    }
+
+    /// True when embedding should be skipped right now: the user turned
+    /// indexing off entirely, or the machine is on battery and
+    /// `power.run_on_battery` is not set. Cheap sysfs reads — safe to call
+    /// per-file and on every watch_loop tick.
+    fn indexing_paused(&self) -> bool {
+        if !self.config.power.enabled {
+            return true;
+        }
+        !self.config.power.run_on_battery && !power::on_ac_power()
+    }
+
+    pub fn full_reindex(&self) {
+        eprintln!("breadmill: full reindex triggered");
+        {
+            let mut store = self.state.store.lock().unwrap();
+            // Clear all state
+            let _ = store.conn.execute_batch("DELETE FROM chunks; DELETE FROM files;");
+            let _ = store.index.reserve(4096);
+        }
+        self.initial_scan();
+    }
+
+    fn initial_scan(&self) {
+        eprintln!("breadmill: scanning roots...");
+
+        let roots: Vec<PathBuf> = self.config.index.roots
+            .iter()
+            .map(|r| expand_home(r))
+            .collect();
+
+        let excludes: Vec<PathBuf> = self.config.index.excludes
+            .iter()
+            .map(|r| expand_home(r))
+            .collect();
+
+        // Snapshot existing indexed files
+        let known: HashMap<String, (i64, String)> = {
+            let store = self.state.store.lock().unwrap();
+            store.all_files()
+                .unwrap_or_default()
+                .into_iter()
+                .map(|f| (f.path, (f.mtime, f.hash)))
+                .collect()
+        };
+
+        let mut seen: HashSet<String> = HashSet::new();
+        let max_bytes = (self.config.index.max_file_mb * 1024.0 * 1024.0) as u64;
+
+        for root in &roots {
+            if !root.exists() {
+                continue;
+            }
+
+            for entry in WalkBuilder::new(root)
+                .hidden(false)
+                .ignore(true)
+                .git_ignore(true)
+                .build()
+                .filter_map(|e| e.ok())
+                .filter(|e| e.path().is_file())
+            {
+                let path = entry.path();
+
+                if excludes.iter().any(|excl| path.starts_with(excl)) {
+                    continue;
+                }
+
+                if !self.is_indexed_extension(path) {
+                    continue;
+                }
+
+                let meta = match fs::metadata(path) {
+                    Ok(m) => m,
+                    Err(_) => continue,
+                };
+
+                if meta.len() > max_bytes {
+                    continue;
+                }
+
+                let path_str = path.to_string_lossy().into_owned();
+                seen.insert(path_str.clone());
+
+                let mtime = mtime_secs(&meta);
+
+                if let Some((known_mtime, _)) = known.get(&path_str) {
+                    if *known_mtime == mtime {
+                        continue; // unchanged
+                    }
+                }
+
+                self.state.pending.fetch_add(1, Ordering::Relaxed);
+                self.index_file(path, &path_str, mtime);
+                self.state.pending.fetch_sub(1, Ordering::Relaxed);
+            }
+        }
+
+        // Drop files that were deleted
+        let to_delete: Vec<String> = known
+            .keys()
+            .filter(|p| !seen.contains(*p))
+            .cloned()
+            .collect();
+
+        if !to_delete.is_empty() {
+            let mut store = self.state.store.lock().unwrap();
+            for path in to_delete {
+                eprintln!("breadmill: removing deleted file: {}", path);
+                let _ = store.delete_file(&path);
+            }
+        }
+
+        let count = {
+            let store = self.state.store.lock().unwrap();
+            let n = store.chunk_count();
+            let _ = store.save_index(&self.state_dir);
+            n
+        };
+        self.state.indexed.store(count, Ordering::Relaxed);
+        eprintln!("breadmill: initial scan done — {} chunks indexed", count);
+    }
+
+    fn watch_loop(self) {
+        let (tx, rx) = std::sync::mpsc::channel();
+
+        let mut watcher: RecommendedWatcher = match notify::recommended_watcher(tx) {
+            Ok(w) => w,
+            Err(e) => {
+                eprintln!("breadmill: watcher init failed: {}", e);
+                return;
+            }
+        };
+
+        for root in self.config.index.roots.iter().map(|r| expand_home(r)) {
+            if root.exists() {
+                let _ = watcher.watch(&root, RecursiveMode::Recursive);
+            }
+        }
+
+        let mut pending_paths: HashSet<PathBuf> = HashSet::new();
+        let mut last_event = Instant::now();
+        let quiet = Duration::from_secs(2);
+        let mut was_paused = self.indexing_paused();
+        let mut last_power_check = Instant::now();
+
+        eprintln!("breadmill: watching for changes");
+
+        loop {
+            // Drain the reindex signal
+            if self.state.reindex_signal.swap(false, Ordering::Relaxed) {
+                self.full_reindex();
+            }
+
+            // Re-check the power gate periodically (sysfs reads are cheap but
+            // no need to do it every 500ms tick). On a paused->active
+            // transition, re-run the incremental scan to catch up anything
+            // skipped while gated.
+            if last_power_check.elapsed() >= Duration::from_secs(30) {
+                last_power_check = Instant::now();
+                let now_paused = self.indexing_paused();
+                if was_paused && !now_paused {
+                    eprintln!("breadmill: power gate opened — resuming indexing");
+                    self.initial_scan();
+                }
+                was_paused = now_paused;
+            }
+
+            match rx.recv_timeout(Duration::from_millis(500)) {
+                Ok(Ok(event)) => {
+                    match event.kind {
+                        EventKind::Create(_) | EventKind::Modify(_) | EventKind::Remove(_) => {
+                            for p in event.paths {
+                                pending_paths.insert(p);
+                            }
+                            last_event = Instant::now();
+                        }
+                        _ => {}
+                    }
+                }
+                Ok(Err(e)) => eprintln!("breadmill: watch error: {}", e),
+                Err(_) => {} // timeout — check quiet period
+            }
+
+            if !pending_paths.is_empty() && last_event.elapsed() >= quiet {
+                for path in pending_paths.drain() {
+                    self.handle_fs_event(&path);
+                }
+                let count = {
+                    let store = self.state.store.lock().unwrap();
+                    let n = store.chunk_count();
+                    let _ = store.save_index(&self.state_dir);
+                    n
+                };
+                self.state.indexed.store(count, Ordering::Relaxed);
+            }
+        }
+    }
+
+    fn handle_fs_event(&self, path: &Path) {
+        let excludes: Vec<PathBuf> = self.config.index.excludes
+            .iter()
+            .map(|r| expand_home(r))
+            .collect();
+
+        if excludes.iter().any(|excl| path.starts_with(excl)) {
+            return;
+        }
+
+        if !path.is_file() {
+            let path_str = path.to_string_lossy().into_owned();
+            // File deleted — remove from index
+            let mut store = self.state.store.lock().unwrap();
+            let _ = store.delete_file(&path_str);
+            return;
+        }
+
+        if !self.is_indexed_extension(path) {
+            return;
+        }
+
+        let meta = match fs::metadata(path) {
+            Ok(m) => m,
+            Err(_) => return,
+        };
+
+        let max_bytes = (self.config.index.max_file_mb * 1024.0 * 1024.0) as u64;
+        if meta.len() > max_bytes {
+            return;
+        }
+
+        let path_str = path.to_string_lossy().into_owned();
+        let mtime = mtime_secs(&meta);
+
+        self.state.pending.fetch_add(1, Ordering::Relaxed);
+        self.index_file(path, &path_str, mtime);
+        self.state.pending.fetch_sub(1, Ordering::Relaxed);
+    }
+
+    fn index_file(&self, path: &Path, path_str: &str, mtime: i64) {
+        if self.indexing_paused() {
+            // Leave the file unrecorded so it's picked up again once indexing
+            // resumes (initial_scan/watch_loop treat it as not-yet-indexed).
+            return;
+        }
+
+        eprintln!("breadmill: extracting {}", path_str);
+        let text = match extract::extract(path) {
+            Ok(t) if !t.trim().is_empty() => t,
+            Ok(_) => return,
+            Err(e) => {
+                eprintln!("breadmill: extract {}: {}", path_str, e);
+                return;
+            }
+        };
+
+        let hash = sha256_str(text.as_bytes());
+
+        // Check if hash changed (catches content changes without mtime change)
+        {
+            let store = self.state.store.lock().unwrap();
+            if let Ok(files) = store.all_files() {
+                if files.iter().any(|f| f.path == path_str && f.hash == hash) {
+                    return;
+                }
+            }
+        }
+
+        // 2000 char cap keeps even minified single-line files to ~500–2000 tokens,
+        // avoiding quadratic attention blowup while still splitting at word boundaries
+        // for natural-language files.
+        let chunks = chunk::chunk_text(&text, 400, 80, 2_000);
+        eprintln!("breadmill: embedding {} ({} chars, {} chunks)", path_str, text.len(), chunks.len());
+        let mut embedder_guard = self.state.embedder.lock().unwrap();
+
+        if !self.state.model_ready.load(Ordering::Relaxed) {
+            eprintln!("breadmill: model not ready, skipping embed for {}", path_str);
+            return;
+        }
+
+        let embedder = match embedder_guard.as_mut() {
+            Some(e) => e,
+            None => return,
+        };
+
+        {
+            let mut store = self.state.store.lock().unwrap();
+            let _ = store.delete_file(path_str); // remove old chunks/vectors first
+        }
+
+        let mut any_ok = false;
+        let mut chunks_added = 0usize;
+
+        for (i, chunk) in chunks.iter().enumerate() {
+            eprintln!("breadmill: embed chunk {}/{} ({} chars) for {}", i + 1, chunks.len(), chunk.text.len(), path_str);
+            match embedder.embed_document(&chunk.text) {
+                Ok(embedding) => {
+                    let mut store = self.state.store.lock().unwrap();
+                    // Ensure file row exists before inserting chunks (FK constraint)
+                    let _ = store.upsert_file(path_str, mtime, &hash);
+                    let _ = store.insert_chunk(
+                        path_str,
+                        &chunk.text,
+                        chunk.start,
+                        chunk.end,
+                        &embedding,
+                    );
+                    any_ok = true;
+                    chunks_added += 1;
+                }
+                Err(e) => eprintln!("breadmill: embed error for {}: {}", path_str, e),
+            }
+        }
+
+        if !any_ok {
+            eprintln!("breadmill: no chunks embedded for {}", path_str);
+            // Record the file so the mtime+hash check skips it on the next startup
+            // rather than re-entering the same embed-fail loop.
+            let store = self.state.store.lock().unwrap();
+            let _ = store.upsert_file(path_str, mtime, &hash);
+        } else {
+            // Increment live so `status` reflects progress before the full scan ends.
+            self.state.indexed.fetch_add(chunks_added, Ordering::Relaxed);
+            eprintln!("breadmill: done {} ({} chunks indexed)", path_str, chunks_added);
+        }
+    }
+
+    fn is_indexed_extension(&self, path: &Path) -> bool {
+        path.extension()
+            .and_then(|e| e.to_str())
+            .map(|ext| {
+                self.config
+                    .index
+                    .extensions
+                    .iter()
+                    .any(|e| e.eq_ignore_ascii_case(ext))
+            })
+            .unwrap_or(false)
+    }
+}
+
+fn mtime_secs(meta: &fs::Metadata) -> i64 {
+    meta.modified()
+        .ok()
+        .and_then(|t| t.duration_since(UNIX_EPOCH).ok())
+        .map(|d| d.as_secs() as i64)
+        .unwrap_or(0)
+}
+
+fn sha256_str(bytes: &[u8]) -> String {
+    let mut hasher = Sha256::new();
+    hasher.update(bytes);
+    hex::encode(hasher.finalize())
+}
+
+pub fn expand_home(path: &str) -> PathBuf {
+    if path.starts_with("~/") {
+        let home = std::env::var("HOME").unwrap_or_else(|_| "/tmp".into());
+        PathBuf::from(home).join(&path[2..])
+    } else {
+        PathBuf::from(path)
+    }
+}
diff --git a/breadmill/src/main.rs b/breadmill/src/main.rs
new file mode 100644
index 0000000..eb6c9b4
--- /dev/null
+++ b/breadmill/src/main.rs
@@ -0,0 +1,242 @@
+use std::{
+    io::Read,
+    path::{Path, PathBuf},
+    sync::{Arc, atomic::Ordering},
+};
+
+use breadsearch_shared::{Request, Response};
+
+mod chunk;
+mod embed;
+mod extract;
+mod indexer;
+mod power;
+mod serve;
+mod store;
+
+use embed::{Backend, OrtEmbedder};
+use indexer::{Indexer, SharedState};
+use store::Store;
+
+const MODEL_URL: &str =
+    "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5/resolve/main/onnx/model.onnx";
+const TOKENIZER_URL: &str =
+    "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5/resolve/main/tokenizer.json";
+
+fn main() {
+    let raw_args: Vec<String> = std::env::args().collect();
+
+    // Extract global flags before command dispatch.
+    let use_npu = raw_args.iter().any(|a| a == "--npu");
+    let use_rocm = raw_args.iter().any(|a| a == "--rocm");
+
+    // Build a view of argv without backend flags for command matching.
+    let args: Vec<&str> = raw_args
+        .iter()
+        .skip(1)
+        .filter(|a| a.as_str() != "--npu" && a.as_str() != "--rocm")
+        .map(|s| s.as_str())
+        .collect();
+
+    match args.first().copied() {
+        Some("--version") | Some("-V") => {
+            println!("breadmill {}", env!("CARGO_PKG_VERSION"));
+        }
+        Some("--fetch-model") | Some("fetch-model") => {
+            if let Err(e) = fetch_model() {
+                eprintln!("breadmill: {}", e);
+                std::process::exit(1);
+            }
+        }
+        Some("--reindex") | Some("reindex") => {
+            if let Err(e) = run_daemon(true, use_npu, use_rocm) {
+                eprintln!("breadmill: {}", e);
+                std::process::exit(1);
+            }
+        }
+        Some("query") => {
+            let q = args.get(1).copied().unwrap_or("");
+            if q.is_empty() {
+                eprintln!("usage: breadmill query <text>");
+                std::process::exit(1);
+            }
+            cli_query(q);
+        }
+        Some("status") => {
+            cli_status();
+        }
+        None | Some("serve") | Some("--serve") => {
+            if let Err(e) = run_daemon(false, use_npu, use_rocm) {
+                eprintln!("breadmill: {}", e);
+                std::process::exit(1);
+            }
+        }
+        Some(cmd) => {
+            eprintln!("breadmill: unknown command: {}", cmd);
+            eprintln!(
+                "usage: breadmill [serve|reindex|fetch-model|query <text>|status] [--npu|--rocm] [--version]"
+            );
+            std::process::exit(1);
+        }
+    }
+}
+
+// ---- Daemon -----------------------------------------------------------------
+
+fn run_daemon(force_reindex: bool, use_npu: bool, use_rocm: bool) -> Result<(), String> {
+    let config = breadsearch_shared::Config::load();
+    let state_dir = breadsearch_shared::state_dir();
+    let cache_dir = breadsearch_shared::cache_dir();
+    let socket_path = breadsearch_shared::socket_path();
+    let dim = config.model.dim;
+    let snippet_len = config.search.snippet_len;
+    let search_limit = config.search.limit;
+
+    std::fs::create_dir_all(&state_dir).map_err(|e| e.to_string())?;
+    std::fs::create_dir_all(&cache_dir).map_err(|e| e.to_string())?;
+
+    let backend = if use_npu || config.model.backend == "npu" {
+        eprintln!("breadmill: NPU backend selected");
+        Backend::Npu { cache_dir: cache_dir.clone() }
+    } else if use_rocm || config.model.backend == "rocm" {
+        eprintln!("breadmill: ROCm backend selected");
+        Backend::Rocm
+    } else {
+        Backend::Cpu
+    };
+
+    let store = Store::open(&state_dir, dim)?;
+    let state = Arc::new(SharedState::new(store));
+
+    // Load embedder if model files present
+    let model_dir = model_dir(&cache_dir);
+    let model_path = model_dir.join("model.onnx");
+    let tokenizer_path = model_dir.join("tokenizer.json");
+
+    if model_path.exists() && tokenizer_path.exists() {
+        eprintln!("breadmill: loading model...");
+        match OrtEmbedder::load(&model_path, &tokenizer_path, dim, backend) {
+            Ok(embedder) => {
+                *state.embedder.lock().unwrap() = Some(embedder);
+                state.model_ready.store(true, Ordering::Relaxed);
+                eprintln!("breadmill: model loaded");
+            }
+            Err(e) => eprintln!("breadmill: model load failed: {} — run --fetch-model", e),
+        }
+    } else {
+        eprintln!(
+            "breadmill: model files not found in {} — run: breadmill --fetch-model",
+            model_dir.display()
+        );
+    }
+
+    // Indexer runs in a background thread
+    {
+        let state_clone = Arc::clone(&state);
+        let config_clone = config.clone();
+        let state_dir_clone = state_dir.clone();
+
+        std::thread::spawn(move || {
+            let indexer = Indexer::new(state_clone, config_clone, state_dir_clone);
+            if force_reindex {
+                indexer.full_reindex();
+            }
+            indexer.run();
+        });
+    }
+
+    // Server runs on the main thread (blocking)
+    serve::run(&socket_path, Arc::clone(&state), snippet_len, search_limit);
+
+    Ok(())
+}
+
+// ---- Model fetch ------------------------------------------------------------
+
+fn fetch_model() -> Result<(), String> {
+    let cache_dir = breadsearch_shared::cache_dir();
+    let model_dir = model_dir(&cache_dir);
+    std::fs::create_dir_all(&model_dir).map_err(|e| e.to_string())?;
+
+    download_if_missing(MODEL_URL, &model_dir.join("model.onnx"))?;
+    download_if_missing(TOKENIZER_URL, &model_dir.join("tokenizer.json"))?;
+
+    eprintln!("breadmill: model files ready in {}", model_dir.display());
+    Ok(())
+}
+
+fn download_if_missing(url: &str, dest: &Path) -> Result<(), String> {
+    if dest.exists() {
+        eprintln!("  already present: {}", dest.display());
+        return Ok(());
+    }
+
+    eprintln!("  downloading {} ...", url);
+    let agent = ureq::AgentBuilder::new()
+        .timeout(std::time::Duration::from_secs(300))
+        .build();
+
+    let response = agent.get(url).call().map_err(|e| e.to_string())?;
+    let mut bytes = Vec::new();
+    response
+        .into_reader()
+        .read_to_end(&mut bytes)
+        .map_err(|e| e.to_string())?;
+
+    if bytes.is_empty() {
+        return Err(format!("empty download from {}", url));
+    }
+
+    // Write atomically via temp file
+    let tmp = dest.with_extension("tmp");
+    std::fs::write(&tmp, &bytes).map_err(|e| e.to_string())?;
+    std::fs::rename(&tmp, dest).map_err(|e| e.to_string())?;
+
+    eprintln!("  saved {} ({:.1} MB)", dest.display(), bytes.len() as f64 / 1_048_576.0);
+    Ok(())
+}
+
+fn model_dir(cache_dir: &Path) -> PathBuf {
+    cache_dir.join("models")
+}
+
+// ---- CLI helpers ------------------------------------------------------------
+
+fn cli_query(query: &str) {
+    let req = Request::Query {
+        query: query.to_string(),
+        limit: 10,
+    };
+    match breadsearch_shared::send_request(&req) {
+        Ok(Response::Hits { hits }) => {
+            if hits.is_empty() {
+                println!("no results");
+            }
+            for (i, h) in hits.iter().enumerate() {
+                println!(
+                    "{:2}. {} ({:.3})\n    {}\n    {}\n",
+                    i + 1,
+                    h.title,
+                    h.score,
+                    h.path,
+                    h.snippet.lines().next().unwrap_or(""),
+                );
+            }
+        }
+        Ok(Response::Error { message }) => eprintln!("error: {}", message),
+        Ok(_) => eprintln!("unexpected response"),
+        Err(e) => eprintln!("could not reach breadmill: {}", e),
+    }
+}
+
+fn cli_status() {
+    match breadsearch_shared::send_request(&Request::Status) {
+        Ok(Response::StatusInfo(s)) => {
+            println!("indexed:     {}", s.indexed);
+            println!("pending:     {}", s.pending);
+            println!("model ready: {}", s.model_ready);
+        }
+        Ok(_) => eprintln!("unexpected response"),
+        Err(e) => eprintln!("could not reach breadmill: {}", e),
+    }
+}
diff --git a/breadmill/src/power.rs b/breadmill/src/power.rs
new file mode 100644
index 0000000..49ad02c
--- /dev/null
+++ b/breadmill/src/power.rs
@@ -0,0 +1,31 @@
+use std::fs;
+use std::path::Path;
+
+/// Best-effort check for whether the system is currently on AC/mains power.
+/// Scans `/sys/class/power_supply` for a Mains or USB supply with `online=1`.
+/// Systems with no such supply at all (desktops, no battery) are treated as
+/// always on power, so this never blocks indexing on hardware without a
+/// battery to protect.
+pub fn on_ac_power() -> bool {
+    let dir = Path::new("/sys/class/power_supply");
+    let Ok(entries) = fs::read_dir(dir) else {
+        return true;
+    };
+
+    let mut found_mains = false;
+    for entry in entries.flatten() {
+        let path = entry.path();
+        let supply_type = fs::read_to_string(path.join("type")).unwrap_or_default();
+        let supply_type = supply_type.trim();
+        if supply_type != "Mains" && supply_type != "USB" {
+            continue;
+        }
+        found_mains = true;
+        let online = fs::read_to_string(path.join("online")).unwrap_or_default();
+        if online.trim() == "1" {
+            return true;
+        }
+    }
+
+    !found_mains
+}
diff --git a/breadmill/src/serve.rs b/breadmill/src/serve.rs
new file mode 100644
index 0000000..cd67e67
--- /dev/null
+++ b/breadmill/src/serve.rs
@@ -0,0 +1,127 @@
+use std::{
+    io::{BufRead, BufReader, Write},
+    os::unix::net::{UnixListener, UnixStream},
+    path::Path,
+    sync::Arc,
+};
+
+use breadsearch_shared::{Request, Response, StatusInfo};
+
+use crate::indexer::SharedState;
+
+pub fn run(socket_path: &Path, state: Arc<SharedState>, snippet_len: usize, search_limit: usize) {
+    let _ = std::fs::remove_file(socket_path);
+
+    let listener = match UnixListener::bind(socket_path) {
+        Ok(l) => l,
+        Err(e) => {
+            eprintln!("breadmill: bind {}: {}", socket_path.display(), e);
+            return;
+        }
+    };
+
+    eprintln!("breadmill: listening on {}", socket_path.display());
+
+    for stream in listener.incoming() {
+        match stream {
+            Ok(s) => {
+                let state = Arc::clone(&state);
+                std::thread::spawn(move || {
+                    handle(s, state, snippet_len, search_limit);
+                });
+            }
+            Err(e) => eprintln!("breadmill: accept error: {}", e),
+        }
+    }
+}
+
+fn handle(stream: UnixStream, state: Arc<SharedState>, snippet_len: usize, search_limit: usize) {
+    let stream_write = match stream.try_clone() {
+        Ok(s) => s,
+        Err(_) => return,
+    };
+
+    let mut reader = BufReader::new(&stream);
+    let mut writer = std::io::BufWriter::new(stream_write);
+
+    let mut line = String::new();
+    if reader.read_line(&mut line).is_err() {
+        return;
+    }
+
+    let response = match serde_json::from_str::<Request>(line.trim()) {
+        Ok(req) => {
+            let r = std::panic::catch_unwind(std::panic::AssertUnwindSafe(|| {
+                dispatch(req, &state, snippet_len, search_limit)
+            }));
+            match r {
+                Ok(resp) => resp,
+                Err(_) => Response::Error { message: "internal error".into() },
+            }
+        }
+        Err(e) => Response::Error { message: e.to_string() },
+    };
+
+    if let Ok(mut json) = serde_json::to_string(&response) {
+        json.push('\n');
+        let _ = writer.write_all(json.as_bytes());
+        let _ = writer.flush();
+    }
+}
+
+fn dispatch(
+    req: Request,
+    state: &SharedState,
+    snippet_len: usize,
+    search_limit: usize,
+) -> Response {
+    use std::sync::atomic::Ordering;
+
+    match req {
+        Request::Query { query, limit } => {
+            if !state.model_ready.load(Ordering::Relaxed) {
+                return Response::Error {
+                    message: "model not ready — run breadmill --fetch-model".into(),
+                };
+            }
+
+            let embedding = {
+                let mut embedder = state.embedder.lock().unwrap();
+                match embedder.as_mut() {
+                    Some(e) => match e.embed_query(&query) {
+                        Ok(v) => v,
+                        Err(e) => return Response::Error { message: e },
+                    },
+                    None => {
+                        return Response::Error {
+                            message: "embedder unavailable".into(),
+                        }
+                    }
+                }
+            };
+
+            let limit = limit.min(search_limit).max(1);
+            let store = state.store.lock().unwrap();
+
+            match store.search(&embedding, limit, snippet_len) {
+                Ok(hits) => Response::Hits { hits },
+                Err(e) => Response::Error { message: e },
+            }
+        }
+
+        Request::Status => {
+            use std::sync::atomic::Ordering;
+            Response::StatusInfo(StatusInfo {
+                indexed: state.indexed.load(Ordering::Relaxed),
+                pending: state.pending.load(Ordering::Relaxed),
+                model_ready: state.model_ready.load(Ordering::Relaxed),
+            })
+        }
+
+        Request::Reindex => {
+            use std::sync::atomic::Ordering;
+            state.reindex_signal.store(true, Ordering::Relaxed);
+            Response::Ok
+        }
+    }
+}
diff --git a/breadmill/src/store.rs b/breadmill/src/store.rs
new file mode 100644
index 0000000..077736d
--- /dev/null
+++ b/breadmill/src/store.rs
@@ -0,0 +1,235 @@
+use std::path::Path;
+
+use rusqlite::{Connection, params};
+use usearch::{Index, IndexOptions, MetricKind, ScalarKind, new_index};
+
+pub struct Store {
+    pub conn: Connection,
+    pub index: Index,
+    pub dim: usize,
+}
+
+// usearch::Index wraps a raw C++ pointer; access is serialized by the Mutex<Store>.
+unsafe impl Send for Store {}
+
+#[derive(Debug)]
+pub struct FileMeta {
+    pub path: String,
+    pub mtime: i64,
+    pub hash: String,
+}
+
+impl Store {
+    pub fn open(state_dir: &Path, dim: usize) -> Result<Self, String> {
+        std::fs::create_dir_all(state_dir).map_err(|e| e.to_string())?;
+
+        let db_path = state_dir.join("meta.db");
+        let conn = Connection::open(&db_path).map_err(|e| e.to_string())?;
+
+        conn.execute_batch(
+            "PRAGMA journal_mode=WAL;
+             PRAGMA foreign_keys=ON;
+             CREATE TABLE IF NOT EXISTS files (
+                 path  TEXT PRIMARY KEY,
+                 mtime INTEGER NOT NULL,
+                 hash  TEXT NOT NULL
+             );
+             CREATE TABLE IF NOT EXISTS chunks (
+                 id          INTEGER PRIMARY KEY,
+                 path        TEXT NOT NULL REFERENCES files(path) ON DELETE CASCADE,
+                 chunk_text  TEXT NOT NULL,
+                 chunk_start INTEGER NOT NULL,
+                 chunk_end   INTEGER NOT NULL
+             );",
+        )
+        .map_err(|e| e.to_string())?;
+
+        let idx_path = state_dir.join("vectors.usearch");
+        let options = IndexOptions {
+            dimensions: dim,
+            metric: MetricKind::Cos,
+            quantization: ScalarKind::F32,
+            connectivity: 16,
+            expansion_add: 128,
+            expansion_search: 64,
+            multi: false,
+        };
+        let index = new_index(&options).map_err(|e| e.to_string())?;
+
+        if idx_path.exists() {
+            index
+                .load(idx_path.to_str().unwrap())
+                .map_err(|e| e.to_string())?;
+        } else {
+            index.reserve(4096).map_err(|e| e.to_string())?;
+        }
+
+        Ok(Self { conn, index, dim })
+    }
+
+    // ---- file state ---------------------------------------------------------
+
+    pub fn all_files(&self) -> Result<Vec<FileMeta>, String> {
+        let mut stmt = self
+            .conn
+            .prepare("SELECT path, mtime, hash FROM files")
+            .map_err(|e| e.to_string())?;
+        let rows = stmt
+            .query_map([], |row| {
+                Ok(FileMeta {
+                    path: row.get(0)?,
+                    mtime: row.get(1)?,
+                    hash: row.get(2)?,
+                })
+            })
+            .map_err(|e| e.to_string())?
+            .filter_map(|r| r.ok())
+            .collect();
+        Ok(rows)
+    }
+
+    pub fn upsert_file(&self, path: &str, mtime: i64, hash: &str) -> Result<(), String> {
+        self.conn
+            .execute(
+                "INSERT OR REPLACE INTO files (path, mtime, hash) VALUES (?1, ?2, ?3)",
+                params![path, mtime, hash],
+            )
+            .map_err(|e| e.to_string())?;
+        Ok(())
+    }
+
+    /// Delete a file and all its chunks from SQLite; also remove chunk vectors.
+    pub fn delete_file(&mut self, path: &str) -> Result<(), String> {
+        // Collect chunk IDs before deletion for usearch removal
+        let ids = self.chunk_ids_for(path)?;
+
+        self.conn
+            .execute("DELETE FROM files WHERE path = ?1", params![path])
+            .map_err(|e| e.to_string())?;
+
+        for id in ids {
+            let _ = self.index.remove(id); // best-effort; stale entries are harmless
+        }
+
+        Ok(())
+    }
+
+    fn chunk_ids_for(&self, path: &str) -> Result<Vec<u64>, String> {
+        let mut stmt = self
+            .conn
+            .prepare("SELECT id FROM chunks WHERE path = ?1")
+            .map_err(|e| e.to_string())?;
+        let ids = stmt
+            .query_map(params![path], |row| row.get::<_, i64>(0))
+            .map_err(|e| e.to_string())?
+            .filter_map(|r| r.ok())
+            .map(|id| id as u64)
+            .collect();
+        Ok(ids)
+    }
+
+    // ---- chunk operations ---------------------------------------------------
+
+    pub fn insert_chunk(
+        &mut self,
+        path: &str,
+        text: &str,
+        start: usize,
+        end: usize,
+        embedding: &[f32],
+    ) -> Result<u64, String> {
+        self.conn
+            .execute(
+                "INSERT INTO chunks (path, chunk_text, chunk_start, chunk_end)
+                 VALUES (?1, ?2, ?3, ?4)",
+                params![path, text, start as i64, end as i64],
+            )
+            .map_err(|e| e.to_string())?;
+
+        let id = self.conn.last_insert_rowid() as u64;
+
+        // Grow index if needed
+        if self.index.size() + 1 > self.index.capacity() {
+            self.index
+                .reserve(self.index.capacity() + 4096)
+                .map_err(|e| e.to_string())?;
+        }
+
+        self.index.add(id, embedding).map_err(|e| e.to_string())?;
+
+        Ok(id)
+    }
+
+    pub fn chunk_count(&self) -> usize {
+        self.conn
+            .query_row("SELECT COUNT(*) FROM chunks", [], |row| {
+                row.get::<_, i64>(0)
+            })
+            .unwrap_or(0) as usize
+    }
+
+    // ---- query --------------------------------------------------------------
+
+    pub fn search(
+        &self,
+        embedding: &[f32],
+        limit: usize,
+        snippet_len: usize,
+    ) -> Result<Vec<breadsearch_shared::Hit>, String> {
+        let results = self
+            .index
+            .search(embedding, limit)
+            .map_err(|e| e.to_string())?;
+
+        let mut hits = Vec::new();
+
+        for (key, distance) in results.keys.iter().zip(results.distances.iter()) {
+            // Convert cosine distance → similarity score (higher = better)
+            let score = 1.0 - distance;
+
+            let maybe_chunk = self
+                .conn
+                .query_row(
+                    "SELECT chunk_text, path FROM chunks WHERE id = ?1",
+                    params![*key as i64],
+                    |row| Ok((row.get::<_, String>(0)?, row.get::<_, String>(1)?)),
+                )
+                .ok();
+
+            if let Some((chunk_text, path)) = maybe_chunk {
+                let snippet = truncate_to_chars(&chunk_text, snippet_len);
+                let title = std::path::Path::new(&path)
+                    .file_name()
+                    .and_then(|n| n.to_str())
+                    .unwrap_or(&path)
+                    .to_string();
+
+                hits.push(breadsearch_shared::Hit {
+                    title,
+                    path,
+                    snippet,
+                    score,
+                });
+            }
+        }
+
+        Ok(hits)
+    }
+
+    // ---- persistence --------------------------------------------------------
+
+    pub fn save_index(&self, state_dir: &Path) -> Result<(), String> {
+        let idx_path = state_dir.join("vectors.usearch");
+        self.index
+            .save(idx_path.to_str().unwrap())
+            .map_err(|e| e.to_string())
+    }
+}
+
+fn truncate_to_chars(s: &str, max_chars: usize) -> String {
+    if s.chars().count() <= max_chars {
+        return s.to_string();
+    }
+    let truncated: String = s.chars().take(max_chars).collect();
+    format!("{}…", truncated.trim_end())
+}
diff --git a/breadsearch-shared/Cargo.toml b/breadsearch-shared/Cargo.toml
new file mode 100644
index 0000000..703db23
--- /dev/null
+++ b/breadsearch-shared/Cargo.toml
@@ -0,0 +1,14 @@
+[package]
+name = "breadsearch-shared"
+version = "0.1.0"
+edition = "2021"
+license = "MIT"
+
+[lib]
+name = "breadsearch_shared"
+path = "src/lib.rs"
+
+[dependencies]
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+toml = "0.8"
diff --git a/breadsearch-shared/src/lib.rs b/breadsearch-shared/src/lib.rs
new file mode 100644
index 0000000..0d02cb8
--- /dev/null
+++ b/breadsearch-shared/src/lib.rs
@@ -0,0 +1,268 @@
+use std::{
+    env, fs,
+    io::{BufRead, BufReader, Write},
+    os::unix::net::UnixStream,
+    path::PathBuf,
+};
+
+use serde::{Deserialize, Serialize};
+
+// ---- XDG path helpers -------------------------------------------------------
+
+pub fn home_dir() -> PathBuf {
+    PathBuf::from(env::var("HOME").unwrap_or_else(|_| "/tmp".into()))
+}
+
+pub fn config_dir() -> PathBuf {
+    env::var("XDG_CONFIG_HOME")
+        .map(PathBuf::from)
+        .unwrap_or_else(|_| home_dir().join(".config"))
+        .join("breadsearch")
+}
+
+pub fn state_dir() -> PathBuf {
+    env::var("XDG_STATE_HOME")
+        .map(PathBuf::from)
+        .unwrap_or_else(|_| home_dir().join(".local/state"))
+        .join("breadsearch")
+}
+
+pub fn cache_dir() -> PathBuf {
+    env::var("XDG_CACHE_HOME")
+        .map(PathBuf::from)
+        .unwrap_or_else(|_| home_dir().join(".cache"))
+        .join("breadsearch")
+}
+
+pub fn socket_path() -> PathBuf {
+    env::var("XDG_RUNTIME_DIR")
+        .map(PathBuf::from)
+        .unwrap_or_else(|_| PathBuf::from("/tmp"))
+        .join("breadmill.sock")
+}
+
+// ---- Config -----------------------------------------------------------------
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Config {
+    #[serde(default)]
+    pub index: IndexConfig,
+    #[serde(default)]
+    pub search: SearchConfig,
+    #[serde(default)]
+    pub model: ModelConfig,
+    #[serde(default)]
+    pub power: PowerConfig,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct IndexConfig {
+    #[serde(default = "default_roots")]
+    pub roots: Vec<String>,
+    #[serde(default)]
+    pub excludes: Vec<String>,
+    #[serde(default = "default_extensions")]
+    pub extensions: Vec<String>,
+    #[serde(default = "default_max_file_mb")]
+    pub max_file_mb: f64,
+}
+
+fn default_roots() -> Vec<String> {
+    let home = home_dir();
+    vec![
+        home.join("Documents").to_string_lossy().into_owned(),
+        home.join("Projects").to_string_lossy().into_owned(),
+        home.join(".config/breadpad").to_string_lossy().into_owned(),
+    ]
+}
+
+fn default_extensions() -> Vec<String> {
+    ["md", "txt", "org", "pdf", "odt", "docx"]
+        .iter()
+        .map(|s| s.to_string())
+        .collect()
+}
+
+fn default_max_file_mb() -> f64 {
+    10.0
+}
+
+impl Default for IndexConfig {
+    fn default() -> Self {
+        Self {
+            roots: default_roots(),
+            excludes: vec![],
+            extensions: default_extensions(),
+            max_file_mb: default_max_file_mb(),
+        }
+    }
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SearchConfig {
+    #[serde(default = "default_limit")]
+    pub limit: usize,
+    #[serde(default = "default_snippet_len")]
+    pub snippet_len: usize,
+}
+
+fn default_limit() -> usize {
+    10
+}
+
+fn default_snippet_len() -> usize {
+    200
+}
+
+impl Default for SearchConfig {
+    fn default() -> Self {
+        Self {
+            limit: default_limit(),
+            snippet_len: default_snippet_len(),
+        }
+    }
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ModelConfig {
+    #[serde(default = "default_model_name")]
+    pub name: String,
+    #[serde(default = "default_dim")]
+    pub dim: usize,
+    /// Compute backend: "cpu" or "npu" (VitisAI/XDNA).
+    #[serde(default = "default_backend")]
+    pub backend: String,
+}
+
+fn default_model_name() -> String {
+    "nomic-embed-text-v1.5".into()
+}
+
+fn default_dim() -> usize {
+    768
+}
+
+fn default_backend() -> String {
+    "cpu".into()
+}
+
+impl Default for ModelConfig {
+    fn default() -> Self {
+        Self {
+            name: default_model_name(),
+            dim: default_dim(),
+            backend: default_backend(),
+        }
+    }
+}
+
+impl Default for Config {
+    fn default() -> Self {
+        Self {
+            index: IndexConfig::default(),
+            search: SearchConfig::default(),
+            model: ModelConfig::default(),
+            power: PowerConfig::default(),
+        }
+    }
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct PowerConfig {
+    /// Master on/off switch for indexing (embedding). When false, breadmill
+    /// still serves queries over the existing index but never embeds new files.
+    #[serde(default = "default_true")]
+    pub enabled: bool,
+    /// Whether to keep embedding while running on battery. Embedding is the
+    /// compute-heavy step (CPU/NPU/GPU forward pass), so this defaults to
+    /// false to avoid draining battery; indexing resumes automatically once
+    /// AC power is reconnected.
+    #[serde(default)]
+    pub run_on_battery: bool,
+}
+
+fn default_true() -> bool {
+    true
+}
+
+impl Default for PowerConfig {
+    fn default() -> Self {
+        Self {
+            enabled: true,
+            run_on_battery: false,
+        }
+    }
+}
+
+impl Config {
+    pub fn load() -> Self {
+        let path = config_dir().join("config.toml");
+        let content = match fs::read_to_string(&path) {
+            Ok(s) => s,
+            Err(e) if e.kind() == std::io::ErrorKind::NotFound => return Self::default(),
+            Err(e) => {
+                eprintln!("breadsearch: could not read {}: {}", path.display(), e);
+                return Self::default();
+            }
+        };
+        match toml::from_str(&content) {
+            Ok(c) => c,
+            Err(e) => {
+                eprintln!("breadsearch: parse error in {}: {}", path.display(), e);
+                Self::default()
+            }
+        }
+    }
+}
+
+// ---- IPC types (newline-delimited JSON over Unix socket) --------------------
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(tag = "type", rename_all = "snake_case")]
+pub enum Request {
+    Query { query: String, limit: usize },
+    Status,
+    Reindex,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+#[serde(tag = "type", rename_all = "snake_case")]
+pub enum Response {
+    Hits { hits: Vec<Hit> },
+    StatusInfo(StatusInfo),
+    Ok,
+    Error { message: String },
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Hit {
+    pub title: String,
+    pub path: String,
+    pub snippet: String,
+    pub score: f32,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct StatusInfo {
+    pub indexed: usize,
+    pub pending: usize,
+    pub model_ready: bool,
+}
+
+// ---- Socket client ----------------------------------------------------------
+
+pub fn send_request(req: &Request) -> std::io::Result<Response> {
+    let mut stream = UnixStream::connect(socket_path())?;
+
+    let mut line = serde_json::to_string(req)
+        .map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))?;
+    line.push('\n');
+    stream.write_all(line.as_bytes())?;
+    stream.flush()?;
+
+    let mut response = String::new();
+    BufReader::new(stream).read_line(&mut response)?;
+
+    serde_json::from_str(response.trim())
+        .map_err(|e| std::io::Error::new(std::io::ErrorKind::InvalidData, e))
+}
diff --git a/breadsearch/Cargo.toml b/breadsearch/Cargo.toml
new file mode 100644
index 0000000..fc469e0
--- /dev/null
+++ b/breadsearch/Cargo.toml
@@ -0,0 +1,16 @@
+[package]
+name = "breadsearch"
+version = "0.1.0"
+edition = "2021"
+license = "MIT"
+
+[[bin]]
+name = "breadsearch"
+path = "src/main.rs"
+
+[dependencies]
+breadsearch-shared = { path = "../breadsearch-shared" }
+bread-theme = { git = "https://github.com/Breadway/bread-ecosystem", tag = "v0.2.8", features = ["gtk"] }
+gtk4 = { version = "0.11", features = ["v4_12"] }
+gtk4-layer-shell = "0.8"
+serde_json = "1"
diff --git a/breadsearch/src/main.rs b/breadsearch/src/main.rs
new file mode 100644
index 0000000..58f4b47
--- /dev/null
+++ b/breadsearch/src/main.rs
@@ -0,0 +1,429 @@
+use bread_theme::{hex_to_rgba, ink_on, load_palette, Palette};
+use breadsearch_shared::{Hit, Request, Response};
+use std::{
+    cell::RefCell,
+    env, fs,
+    path::PathBuf,
+    process::Command,
+    rc::Rc,
+    sync::mpsc,
+};
+
+use gtk4::{
+    glib,
+    pango::EllipsizeMode,
+    prelude::*,
+    Application, ApplicationWindow, Box as GBox, CssProvider, EventControllerKey, Image, Label,
+    ListBox, Orientation, PolicyType, ScrolledWindow, SearchEntry, SelectionMode,
+};
+use gtk4_layer_shell::{Edge, KeyboardMode, Layer, LayerShell};
+
+// ---- Theming ----------------------------------------------------------------
+
+fn build_css(p: &Palette) -> String {
+    let bg_panel = hex_to_rgba(&p.background, 0.60);
+    format!(
+        "window {{ background-color: transparent; }}\
+         .launcher-bg {{ background-color: {bg_panel}; color: {on_bg}; border-radius: 8px;\
+             box-shadow: 0 8px 32px rgba(0,0,0,0.6); }}\
+         searchentry {{ background-color: {surface}; color: {on_surface}; caret-color: {accent};\
+             border: none; outline: none; box-shadow: none;\
+             padding: 12px 16px; border-radius: 6px 6px 0 0; }}\
+         listbox {{ background-color: transparent; padding: 4px; }}\
+         row {{ padding: 8px 12px; color: {on_bg}; background-color: transparent;\
+             border-radius: 6px; }}\
+         row:hover {{ background-color: {surface}; color: {on_surface}; }}\
+         row:selected {{ background-color: {surface}; color: {on_surface}; }}\
+         .hit-title {{ font-size: 14px; }}\
+         .hit-muted {{ opacity: 0.6; font-size: 12px; }}\
+         .hit-snippet {{ opacity: 0.75; font-size: 11px; font-style: italic; }}\
+         .hit-score {{ opacity: 0.5; font-size: 11px; }}\
+         image {{ margin-right: 8px; }}",
+        bg_panel   = bg_panel,
+        surface    = p.color0,
+        accent     = p.color4,
+        on_bg      = ink_on(&p.background),
+        on_surface = ink_on(&p.color0),
+    )
+}
+
+// ---- PID file toggle --------------------------------------------------------
+
+fn pid_file() -> PathBuf {
+    env::var("XDG_RUNTIME_DIR")
+        .map(PathBuf::from)
+        .unwrap_or_else(|_| PathBuf::from("/tmp"))
+        .join("breadsearch.pid")
+}
+
+fn is_breadsearch_pid(pid: u32) -> bool {
+    fs::read_to_string(format!("/proc/{}/comm", pid))
+        .map(|s| s.trim() == "breadsearch")
+        .unwrap_or(false)
+}
+
+fn toggle_or_continue() -> bool {
+    let pf = pid_file();
+    if let Ok(content) = fs::read_to_string(&pf) {
+        if let Ok(pid) = content.trim().parse::<u32>() {
+            if is_breadsearch_pid(pid) {
+                let _ = Command::new("kill").arg(pid.to_string()).status();
+                return false;
+            }
+        }
+    }
+    let _ = fs::write(&pf, std::process::id().to_string());
+    true
+}
+
+fn cleanup_pid() {
+    let _ = fs::remove_file(pid_file());
+}
+
+// ---- Row builder ------------------------------------------------------------
+
+fn make_hit_row(hit: &Hit) -> gtk4::ListBoxRow {
+    let row = gtk4::ListBoxRow::new();
+    let vbox = GBox::new(Orientation::Vertical, 2);
+    vbox.set_margin_start(6);
+    vbox.set_margin_end(6);
+    vbox.set_margin_top(4);
+    vbox.set_margin_bottom(4);
+
+    let hbox = GBox::new(Orientation::Horizontal, 0);
+
+    let (content_type, _) =
+        gtk4::gio::functions::content_type_guess(Some(&hit.path), None::<&[u8]>);
+    let gicon = gtk4::gio::content_type_get_icon(&content_type);
+    let img = Image::from_gicon(&gicon);
+    img.set_pixel_size(24);
+    hbox.append(&img);
+
+    let title = Label::new(Some(&hit.title));
+    title.add_css_class("hit-title");
+    title.set_xalign(0.0);
+    title.set_hexpand(true);
+    title.set_ellipsize(EllipsizeMode::End);
+    hbox.append(&title);
+
+    let score_lbl = Label::new(Some(&format!("{:.0}%", hit.score * 100.0)));
+    score_lbl.add_css_class("hit-score");
+    score_lbl.set_xalign(1.0);
+    hbox.append(&score_lbl);
+
+    vbox.append(&hbox);
+
+    let path_lbl = Label::new(Some(&hit.path));
+    path_lbl.add_css_class("hit-muted");
+    path_lbl.set_xalign(0.0);
+    path_lbl.set_ellipsize(EllipsizeMode::Start);
+    vbox.append(&path_lbl);
+
+    let snippet_text = hit.snippet.lines().next().unwrap_or("").trim().to_string();
+    if !snippet_text.is_empty() {
+        let snippet = Label::new(Some(&snippet_text));
+        snippet.add_css_class("hit-snippet");
+        snippet.set_xalign(0.0);
+        snippet.set_ellipsize(EllipsizeMode::End);
+        vbox.append(&snippet);
+    }
+
+    row.set_child(Some(&vbox));
+    unsafe { row.set_data("hit_path", hit.path.clone()) };
+    row
+}
+
+fn row_path(row: &gtk4::ListBoxRow) -> Option<String> {
+    unsafe { row.data::<String>("hit_path").map(|p| p.as_ref().clone()) }
+}
+
+// ---- List population --------------------------------------------------------
+
+fn clear_list(list: &ListBox) {
+    while let Some(child) = list.first_child() {
+        list.remove(&child);
+    }
+}
+
+fn info_row(text: &str) -> gtk4::ListBoxRow {
+    let row = gtk4::ListBoxRow::new();
+    let lbl = Label::new(Some(text));
+    lbl.add_css_class("hit-muted");
+    lbl.set_margin_top(12);
+    lbl.set_margin_bottom(12);
+    row.set_child(Some(&lbl));
+    row.set_activatable(false);
+    row.set_selectable(false);
+    row
+}
+
+fn populate_list(list: &ListBox, result: std::io::Result<Response>) {
+    clear_list(list);
+    match result {
+        Ok(Response::Hits { hits }) => {
+            if hits.is_empty() {
+                list.append(&info_row("No results"));
+            } else {
+                for hit in &hits {
+                    list.append(&make_hit_row(hit));
+                }
+                if let Some(first) = list.row_at_index(0) {
+                    list.select_row(Some(&first));
+                }
+            }
+        }
+        Ok(Response::Error { message }) => {
+            list.append(&info_row(&format!("Error: {}", message)));
+        }
+        Err(e) => {
+            list.append(&info_row(&format!("breadmill not running: {}", e)));
+        }
+        _ => {}
+    }
+}
+
+// ---- Actions ----------------------------------------------------------------
+
+fn open_file(path: &str) {
+    let _ = Command::new("xdg-open")
+        .arg(path)
+        .stdin(std::process::Stdio::null())
+        .stdout(std::process::Stdio::null())
+        .stderr(std::process::Stdio::null())
+        .spawn();
+}
+
+fn open_folder(path: &str) {
+    let parent = std::path::Path::new(path)
+        .parent()
+        .map(|p| p.to_string_lossy().into_owned())
+        .unwrap_or_else(|| path.to_string());
+    let _ = Command::new("xdg-open")
+        .arg(&parent)
+        .stdin(std::process::Stdio::null())
+        .stdout(std::process::Stdio::null())
+        .stderr(std::process::Stdio::null())
+        .spawn();
+}
+
+// ---- UI ---------------------------------------------------------------------
+
+fn run_ui() {
+    let app = Application::builder()
+        .application_id("com.breadway.breadsearch")
+        .build();
+
+    let debounce_id: Rc<RefCell<Option<glib::SourceId>>> = Rc::new(RefCell::new(None));
+
+    app.connect_activate(move |app| {
+        bread_theme::gtk::apply_shared();
+        bread_theme::gtk::apply_app_css(|| build_css(&load_palette()));
+
+        {
+            let user_css_path = breadsearch_shared::config_dir().join("style.css");
+            let user_cell: RefCell<Option<CssProvider>> = RefCell::new(None);
+            bread_theme::gtk::apply_user_css(&user_css_path, &user_cell);
+        }
+
+        let window = ApplicationWindow::builder().application(app).build();
+        window.init_layer_shell();
+        window.set_namespace(Some("breadsearch"));
+        window.set_layer(Layer::Overlay);
+        window.set_keyboard_mode(KeyboardMode::Exclusive);
+        for edge in [Edge::Top, Edge::Bottom, Edge::Left, Edge::Right] {
+            window.set_anchor(edge, true);
+        }
+        window.set_exclusive_zone(0);
+
+        let close_all: Rc<dyn Fn()> = Rc::new({
+            let w = window.clone();
+            move || {
+                cleanup_pid();
+                w.close();
+            }
+        });
+
+        let vbox = GBox::new(Orientation::Vertical, 0);
+        vbox.add_css_class("launcher-bg");
+        vbox.set_halign(gtk4::Align::Center);
+        vbox.set_valign(gtk4::Align::Start);
+        vbox.set_margin_top(120);
+        vbox.set_size_request(640, -1);
+
+        let search = SearchEntry::new();
+        search.set_placeholder_text(Some("breadsearch — find anything by meaning"));
+        vbox.append(&search);
+
+        let scroll = ScrolledWindow::new();
+        scroll.set_policy(PolicyType::Never, PolicyType::Automatic);
+        scroll.set_max_content_height(520);
+        scroll.set_propagate_natural_height(true);
+
+        let list = ListBox::new();
+        list.set_selection_mode(SelectionMode::Browse);
+        list.append(&info_row("Type to search across your documents…"));
+
+        scroll.set_child(Some(&list));
+        vbox.append(&scroll);
+        window.set_child(Some(&vbox));
+
+        // Search with 150ms debounce + off-UI-thread query
+        let list_s = list.clone();
+        let debounce = Rc::clone(&debounce_id);
+
+        search.connect_changed(move |entry| {
+            let query = entry.text().to_string();
+
+            if let Some(id) = debounce.borrow_mut().take() {
+                id.remove();
+            }
+
+            if query.is_empty() {
+                clear_list(&list_s);
+                list_s.append(&info_row("Type to search across your documents…"));
+                return;
+            }
+
+            let list_clone = list_s.clone();
+            let debounce_clone = Rc::clone(&debounce);
+
+            let id = glib::timeout_add_local(std::time::Duration::from_millis(150), move || {
+                debounce_clone.borrow_mut().take();
+
+                let q = query.clone();
+                let (tx, rx) = mpsc::sync_channel::<std::io::Result<Response>>(1);
+
+                std::thread::spawn(move || {
+                    let req = Request::Query { query: q, limit: 10 };
+                    let _ = tx.send(breadsearch_shared::send_request(&req));
+                });
+
+                // Poll via idle_add_local until the thread delivers its result.
+                // Unix socket round-trips are sub-millisecond so this fires once.
+                let rx = Rc::new(rx);
+                let list_t = list_clone.clone();
+
+                glib::idle_add_local(move || {
+                    match rx.try_recv() {
+                        Ok(result) => {
+                            populate_list(&list_t, result);
+                            glib::ControlFlow::Break
+                        }
+                        Err(mpsc::TryRecvError::Empty) => glib::ControlFlow::Continue,
+                        Err(mpsc::TryRecvError::Disconnected) => glib::ControlFlow::Break,
+                    }
+                });
+
+                glib::ControlFlow::Break
+            });
+
+            *debounce.borrow_mut() = Some(id);
+        });
+
+        // Keyboard handling
+        let key_ctrl = EventControllerKey::new();
+        key_ctrl.set_propagation_phase(gtk4::PropagationPhase::Capture);
+        let close_k = Rc::clone(&close_all);
+        let list_k = list.clone();
+
+        key_ctrl.connect_key_pressed(move |_, key, _, mods| {
+            use gtk4::gdk::Key;
+            match key {
+                Key::Escape => {
+                    close_k();
+                    glib::Propagation::Stop
+                }
+                Key::Return | Key::KP_Enter => {
+                    if let Some(row) = list_k.selected_row() {
+                        if let Some(path) = row_path(&row) {
+                            if mods.contains(gtk4::gdk::ModifierType::CONTROL_MASK) {
+                                open_folder(&path);
+                            } else {
+                                open_file(&path);
+                            }
+                            close_k();
+                        }
+                    }
+                    glib::Propagation::Stop
+                }
+                Key::Down => {
+                    let cur = list_k.selected_row().map(|r| r.index()).unwrap_or(-1);
+                    let mut i = cur + 1;
+                    loop {
+                        match list_k.row_at_index(i) {
+                            Some(r) if r.is_selectable() => {
+                                list_k.select_row(Some(&r));
+                                break;
+                            }
+                            Some(_) => i += 1,
+                            None => break,
+                        }
+                    }
+                    glib::Propagation::Stop
+                }
+                Key::Up => {
+                    let cur = list_k.selected_row().map(|r| r.index()).unwrap_or(0);
+                    let mut i = cur - 1;
+                    loop {
+                        if i < 0 {
+                            break;
+                        }
+                        match list_k.row_at_index(i) {
+                            Some(r) if r.is_selectable() => {
+                                list_k.select_row(Some(&r));
+                                break;
+                            }
+                            Some(_) => i -= 1,
+                            None => break,
+                        }
+                    }
+                    glib::Propagation::Stop
+                }
+                _ => glib::Propagation::Proceed,
+            }
+        });
+        window.add_controller(key_ctrl);
+
+        // Row click opens file
+        let close_a = Rc::clone(&close_all);
+        list.connect_row_activated(move |_, row| {
+            if let Some(path) = row_path(row) {
+                open_file(&path);
+                close_a();
+            }
+        });
+
+        // Click outside launcher panel → close
+        let close_outside = Rc::clone(&close_all);
+        let vbox_ref = vbox.clone();
+        let win_ref = window.clone();
+        let outside_click = gtk4::GestureClick::new();
+        outside_click.connect_pressed(move |_, _, x, y| {
+            if let Some(b) = vbox_ref.compute_bounds(&win_ref) {
+                if x < b.x() as f64
+                    || x > (b.x() + b.width()) as f64
+                    || y < b.y() as f64
+                    || y > (b.y() + b.height()) as f64
+                {
+                    close_outside();
+                }
+            }
+        });
+        window.add_controller(outside_click);
+
+        window.connect_destroy(|_| cleanup_pid());
+        window.present();
+        search.grab_focus();
+    });
+
+    app.run();
+}
+
+// ---- Main -------------------------------------------------------------------
+
+fn main() {
+    if !toggle_or_continue() {
+        return;
+    }
+    run_ui();
+}
diff --git a/config.example.toml b/config.example.toml
new file mode 100644
index 0000000..b4fc17a
--- /dev/null
+++ b/config.example.toml
@@ -0,0 +1,41 @@
+# breadsearch configuration
+# Copy to ~/.config/breadsearch/config.toml
+
+[index]
+# Directories to crawl for documents
+roots = [
+    "~/Documents",
+    "~/Projects",
+    "~/.config/breadpad",
+]
+# File extensions to index
+extensions = ["md", "txt", "org", "pdf", "odt", "docx"]
+# Skip files larger than this (MB)
+max_file_mb = 10.0
+
+[search]
+# Default number of results
+limit = 10
+# Max characters in snippet
+snippet_len = 200
+
+[model]
+# Embedding model name (informational; breadmill uses the ONNX files it fetches)
+name = "nomic-embed-text-v1.5"
+# Embedding dimension
+dim = 768
+# Compute backend: "cpu" (default), "npu" (AMD XDNA via VitisAI EP), or
+# "rocm" (AMD iGPU/dGPU). NPU requires breadmill compiled with --features npu
+# and the AMD Ryzen AI SDK installed; ROCm requires --features rocm.
+# Override at runtime with: breadmill --npu / --rocm
+backend = "cpu"
+
+[power]
+# Master switch for indexing. When false, breadmill still answers queries
+# against the existing index but never embeds new/changed files.
+enabled = true
+# Embedding is the compute-heavy step. By default breadmill pauses indexing
+# while on battery and resumes automatically when AC power is reconnected.
+# Set true to index on battery too. Machines with no battery (desktops) are
+# always treated as on-power regardless of this setting.
+run_on_battery = false
diff --git a/docs/error-reports/2026-06-24-oom-minified-json-chunking.md b/docs/error-reports/2026-06-24-oom-minified-json-chunking.md
new file mode 100644
index 0000000..53b6db4
--- /dev/null
+++ b/docs/error-reports/2026-06-24-oom-minified-json-chunking.md
@@ -0,0 +1,238 @@
+# Error Report: breadmill OOM loop on minified JSON `.txt` file
+
+**Date:** 2026-06-24  
+**Reporter:** breadway  
+**Component:** `breadmill` (semantic search indexer)  
+**Version:** 0.1.0 (`/home/breadway/Projects/breadsearch/breadmill`)  
+**Severity:** Critical — repeated kernel OOM kills, ~28 GB RAM consumption, system instability
+
+---
+
+## Summary
+
+`breadmill` enters a crash loop when indexing a 705 KB minified JSON game save file exposed as `.txt`. Word-based chunking produces ~486–616 KB chunks that are passed unbounded to the ONNX embedding model. ONNX Runtime then attempts multi-terabyte allocations in attention `MatMul`, the process balloons to ~28 GB RSS, and the kernel OOM killer terminates it. `Restart=on-failure` causes an immediate restart and the cycle repeats.
+
+On 2026-06-24 this produced **40 embed errors** and **76 OOM kills** in journal logs within ~45 minutes.
+
+---
+
+## Environment
+
+| Item | Value |
+|------|-------|
+| Host | Lenovo Yoga Slim 7 14AKP10 (`83JY`) |
+| OS | BOS (Arch-based), kernel `7.0.12-arch1-1` |
+| RAM | 32 GB |
+| Swap | 4 GB zram |
+| Model | `nomic-embed-text-v1.5` (768-dim, ONNX via `ort` 2.0.0-rc.12) |
+| Service | `breadmill.service` (user systemd unit) |
+| Config | `~/.config/breadsearch/config.toml` |
+
+### Service unit
+
+```ini
+[Service]
+Type=simple
+ExecStart=%h/.cargo/bin/breadmill
+Restart=on-failure
+RestartSec=5
+# No MemoryMax set (unlimited)
+# OOMScoreAdjust=200 (default for user services — preferential OOM victim)
+```
+
+### Relevant config
+
+```toml
+[index]
+roots = ["~/Documents", "~/.config/breadpad"]
+extensions = ["md", "txt", "org", "pdf", "odt", "docx"]
+max_file_mb = 5.0
+```
+
+---
+
+## Triggering file
+
+**Path:**  
+`~/Documents/Gaming/Games/Spaceflight Simulator Game/Saving/Worlds/Bread/Persistent/Rockets.txt`
+
+| Property | Value |
+|----------|-------|
+| Size | 721,601 bytes (705 KB) |
+| Type | JSON array (11 rocket objects), stored as `.txt` |
+| Lines | **1** (single-line minified JSON) |
+| Whitespace-delimited "words" | 601 |
+| Longest "word" | 68,068 characters |
+
+The file is under `max_file_mb = 5.0` and has extension `.txt`, so it is indexed.
+
+---
+
+## Root cause
+
+### 1. Word-based chunking breaks on minified JSON
+
+`chunk::chunk_text()` splits on whitespace only (`chunk.rs`). For minified JSON, most structure is packed into very long tokens with few spaces.
+
+Chunking with `words_per_chunk=400`, `overlap_words=80` (`indexer.rs:268`) yields:
+
+| Chunk | Word range | Character length |
+|-------|------------|------------------|
+| 0 | 0–399 | **486,347** |
+| 1 | 320–600 | **616,582** |
+
+A "400-word chunk" is not ~400 natural-language words; it is hundreds of kilobytes of dense JSON.
+
+### 2. No token/character limit before embedding
+
+`embed::OrtEmbedder::embed_with_prefix()` tokenizes the full chunk and runs ONNX inference with no `max_length` truncation (`embed.rs`). The nomic model supports ~8192 tokens; these chunks are orders of magnitude larger when tokenized.
+
+### 3. ONNX attention allocation explodes
+
+Embedding fails inside ONNX Runtime:
+
+```
+Non-zero status code returned while running FusedMatMul node.
+Name: '/encoder/layers.0/attn/MatMul/MatMulScaleFusion/'
+Status Message: ... BFCArena::AllocateRawInternal ...
+Failed to allocate memory for requested buffer of size 5525780084992
+```
+
+Requested buffer: **~5.1 TiB** (5,525,780,084,992 bytes).
+
+Kernel also logs repeated allocation attempts before OOM:
+
+```
+__vm_enough_memory: pid: NNNN, comm: breadmill,
+  bytes: 8796093026304 not enough memory for the allocation   (~8.0 TiB)
+  bytes: 7916483514368 not enough memory for the allocation   (~7.2 TiB)
+  bytes: 7124834848768 not enough memory for the allocation   (~6.5 TiB)
+```
+
+### 4. Restart loop amplifies damage
+
+`Restart=on-failure` + `RestartSec=5` restarts breadmill immediately after each OOM kill. Each restart reloads the model, rescans `~/Documents`, hits the same file, and OOMs again. Peak memory per attempt: **~27–28 GB RSS** (`anon-rss:27867432kB`).
+
+---
+
+## Log excerpts
+
+### Embed error (repeats on every restart)
+
+```
+breadmill: embed error for .../Rockets.txt: Non-zero status code returned while running FusedMatMul node.
+Name:'/encoder/layers.0/attn/MatMul/MatMulScaleFusion/' Status Message:
+  ... Failed to allocate memory for requested buffer of size 5525780084992
+```
+
+### OOM kill
+
+```
+oom-kill: ... task_memcg=.../breadmill.service, task=breadmill, pid=4515, uid=1000
+Out of memory: Killed process 4515 (breadmill)
+  total-vm:64081848kB, anon-rss:27867432kB, ... oom_score_adj:200
+systemd[1649]: breadmill.service: Failed with result 'oom-kill'.
+systemd[1649]: breadmill.service: Consumed ... 27.9G memory peak, 2.1G memory swap peak.
+systemd[1649]: breadmill.service: Scheduled restart job, restart counter is at 8.
+```
+
+### Secondary noise (non-fatal)
+
+```
+breadmill: extract .../Novacana Info.txt: stream did not contain valid UTF-8
+```
+
+---
+
+## Reproduction
+
+1. Place a single-line minified JSON file (>400 KB, `.txt` extension) under a configured index root.
+2. Start `breadmill serve` (or `systemctl --user start breadmill.service`).
+3. Wait for initial scan to reach the file.
+4. Observe embed errors, climbing RSS, and OOM kills in `journalctl --user -u breadmill.service -f`.
+
+**Minimal reproducer characteristics:**
+
+- Extension in `extensions` list (e.g. `txt`)
+- File size `< max_file_mb`
+- Content is minified JSON or other whitespace-sparse text on one line
+- Results in chunks >> model `max_seq_len` when tokenized
+
+---
+
+## Impact
+
+- **breadmill** unusable while the file is present in index roots
+- **System-wide** memory pressure: swap exhaustion, unrelated process kills, journal/cache pressure
+- Can be mistaken for unrelated storage or suspend issues when swap write errors appear in kernel log
+
+---
+
+## Suggested fixes
+
+### Required (correctness)
+
+1. **Truncate before tokenization** — cap input to model `max_seq_len` (8192 tokens for nomic-embed-text-v1.5) in `embed.rs`, with explicit logging when truncation occurs.
+
+2. **Character-based chunk limits** — add `max_chunk_chars` (e.g. 8_000–32_000) independent of word count; split oversized chunks before embedding.
+
+3. **Sanity-check chunk size** — refuse to embed chunks above a byte/token threshold; log and skip rather than calling ONNX.
+
+### Recommended (resilience)
+
+4. **Per-chunk error isolation** — on embed failure for one chunk, skip that chunk but do not retry the entire file in a tight loop; mark file as `failed` in SQLite.
+
+5. **Service memory limit** — set `MemoryMax=4G` (or similar) on `breadmill.service` so a runaway embed cannot take the whole machine.
+
+6. **Lower `OOMScoreAdjust`** — use `0` or negative so breadmill is not preferentially killed while still allowing limits.
+
+7. **Backoff on repeated OOM** — `RestartSec=exponential` or stop after N OOM kills per file.
+
+### Optional (UX)
+
+8. **Exclude patterns** — config option for glob excludes (e.g. `**/Saving/**`, `**/*.json` even if renamed `.txt`).
+
+9. **Detect minified JSON** — if `serde_json::from_str` succeeds on `.txt`, skip or pretty-print/chunk differently.
+
+10. **`--version` flag** — aids bug reports (currently `unknown command: --version`).
+
+---
+
+## Workaround (immediate)
+
+Exclude the game save directory from index roots in `~/.config/breadsearch/config.toml`:
+
+```toml
+[index]
+roots = [
+    "~/Documents",
+    "~/.config/breadpad",
+]
+# Then add an exclude mechanism when available, OR temporarily narrow roots:
+# roots = ["~/Documents/Creative", "~/.config/breadpad"]
+```
+
+Or stop the service until a fix is deployed:
+
+```bash
+systemctl --user stop breadmill.service
+```
+
+---
+
+## Files involved
+
+| File | Role |
+|------|------|
+| `breadmill/src/chunk.rs` | Whitespace word chunking — no char/token cap |
+| `breadmill/src/indexer.rs:268` | `chunk_text(&text, 400, 80)` — hardcoded params |
+| `breadmill/src/embed.rs` | No truncation before `tokenizer.encode()` / ONNX run |
+| `breadmill/src/extract.rs` | Treats `.txt` as raw UTF-8 (JSON passes through) |
+| `~/.config/breadsearch/config.toml` | `max_file_mb` only; no chunk/token limits |
+
+---
+
+## Related
+
+- ONNX node: `/encoder/layers.0/attn/MatMul/MatMulScaleFusion/`
+- Model: [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) (max sequence length 8192)
diff --git a/original-info-signature.txt b/original-info-signature.txt
new file mode 100644
index 0000000..3bfd606
--- /dev/null
+++ b/original-info-signature.txt
@@ -0,0 +1 @@
+6c422027bd642c5e1f409e020b7b7a9b
\ No newline at end of file
diff --git a/original-model-signature.txt b/original-model-signature.txt
new file mode 100644
index 0000000..200c9db
--- /dev/null
+++ b/original-model-signature.txt
@@ -0,0 +1 @@
+e9339c75bca7719f67ac377f03e28b83
\ No newline at end of file
diff --git a/packaging/breadmill.service b/packaging/breadmill.service
new file mode 100644
index 0000000..2b4d65e
--- /dev/null
+++ b/packaging/breadmill.service
@@ -0,0 +1,15 @@
+[Unit]
+Description=Breadmill semantic search indexer
+Documentation=https://github.com/breadway/breadsearch
+After=default.target
+
+[Service]
+Type=simple
+ExecStart=%h/.cargo/bin/breadmill
+Restart=on-failure
+RestartSec=5
+StandardOutput=journal
+StandardError=journal
+
+[Install]
+WantedBy=default.target
diff --git a/quantize_nomic.py b/quantize_nomic.py
new file mode 100644
index 0000000..e98e535
--- /dev/null
+++ b/quantize_nomic.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""
+Quantize nomic-embed-text-v1.5 with AMD Quark for NPU (XDNA) inference.
+Targets int8 QDQ format with enable_npu_transformer=True so VAIML can map
+attention MatMul ops to the NPU.
+"""
+
+import json
+import numpy as np
+import onnxruntime
+from pathlib import Path
+
+MODEL_PATH = Path.home() / ".cache/breadsearch/models/model.onnx"
+TOKENIZER_PATH = Path.home() / ".cache/breadsearch/models/tokenizer.json"
+OUTPUT_PATH = Path.home() / ".cache/breadsearch/models/model_quantized.onnx"
+
+CALIB_SENTENCES = [
+    "search_document: The quick brown fox jumps over the lazy dog.",
+    "search_document: Machine learning is a branch of artificial intelligence.",
+    "search_document: The Eiffel Tower is located in Paris, France.",
+    "search_document: Python is a high-level programming language.",
+    "search_document: The speed of light is approximately 299,792,458 meters per second.",
+    "search_document: Climate change is one of the greatest challenges facing humanity.",
+    "search_document: Quantum computing leverages quantum mechanical phenomena.",
+    "search_document: The human genome contains approximately 3 billion base pairs.",
+    "search_document: Rust is a systems programming language focused on safety.",
+    "search_document: Neural networks are inspired by the structure of the brain.",
+    "search_document: The Milky Way galaxy contains over 100 billion stars.",
+    "search_document: Cryptography is the practice of secure communication.",
+    "search_document: The Linux kernel was first released in 1991 by Linus Torvalds.",
+    "search_document: Databases store and retrieve structured data efficiently.",
+    "search_document: The TCP/IP protocol suite is the backbone of the internet.",
+    "search_document: Embedded systems run on resource-constrained hardware.",
+    "search_document: The ONNX format provides a standard for machine learning models.",
+    "search_document: Transformer models use attention mechanisms for sequence tasks.",
+    "search_document: File systems organize and manage data storage on disks.",
+    "search_document: Async programming allows concurrent execution without threads.",
+    "search_query: What is machine learning?",
+    "search_query: How does attention work in transformers?",
+    "search_query: Where is the Eiffel Tower?",
+    "search_query: What programming language should I learn first?",
+    "search_query: How fast is the speed of light?",
+]
+
+MAX_SEQ_LEN = 512
+
+def load_tokenizer(path: Path):
+    with open(path) as f:
+        return json.load(f)
+
+def simple_tokenize(tok_data: dict, text: str, max_len: int) -> dict[str, np.ndarray]:
+    vocab = tok_data["model"]["vocab"]
+    unk_id = vocab.get("[UNK]", 100)
+    cls_id = vocab.get("[CLS]", 101)
+    sep_id = vocab.get("[SEP]", 102)
+    pad_id = vocab.get("[PAD]", 0)
+
+    words = text.lower().split()
+    token_ids = [cls_id]
+    for w in words:
+        token_ids.append(vocab.get(w, unk_id))
+    token_ids.append(sep_id)
+
+    if len(token_ids) > max_len:
+        token_ids = token_ids[:max_len - 1] + [sep_id]
+
+    seq_len = len(token_ids)
+    padded = token_ids + [pad_id] * (max_len - seq_len)
+    mask = [1] * seq_len + [0] * (max_len - seq_len)
+    type_ids = [0] * max_len
+
+    return {
+        "input_ids": np.array([padded], dtype=np.int64),
+        "attention_mask": np.array([mask], dtype=np.int64),
+        "token_type_ids": np.array([type_ids], dtype=np.int64),
+    }
+
+
+class NomicCalibrationReader:
+    def __init__(self, sentences, tok_data, max_len=MAX_SEQ_LEN):
+        self.inputs = [simple_tokenize(tok_data, s, max_len) for s in sentences]
+        self.idx = 0
+
+    def get_next(self):
+        if self.idx >= len(self.inputs):
+            return None
+        sample = self.inputs[self.idx]
+        self.idx += 1
+        return sample
+
+    def rewind(self):
+        self.idx = 0
+
+
+def main():
+    print(f"Loading tokenizer from {TOKENIZER_PATH}")
+    tok_data = load_tokenizer(TOKENIZER_PATH)
+
+    calib_reader = NomicCalibrationReader(CALIB_SENTENCES, tok_data)
+
+    # Verify model input names match what we provide
+    sess = onnxruntime.InferenceSession(str(MODEL_PATH), providers=["CPUExecutionProvider"])
+    input_names = [inp.name for inp in sess.get_inputs()]
+    print(f"Model inputs: {input_names}")
+    del sess
+
+    from quark.onnx import ModelQuantizer, QuantizationConfig
+    from quark.onnx.quantization.config.config import Config
+    from onnxruntime.quantization import CalibrationMethod, QuantFormat, QuantType
+
+    quant_config = QuantizationConfig(
+        calibrate_method=CalibrationMethod.MinMax,
+        quant_format=QuantFormat.QDQ,
+        activation_type=QuantType.QInt8,
+        weight_type=QuantType.QInt8,
+        per_channel=False,
+        reduce_range=False,
+        optimize_model=True,
+        enable_npu_transformer=True,
+        include_cle=True,
+        print_summary=True,
+        extra_options={
+            "ActivationSymmetric": True,
+            "WeightSymmetric": True,
+        },
+    )
+
+    config = Config(global_quant_config=quant_config)
+    quantizer = ModelQuantizer(config)
+    print(f"Quantizing {MODEL_PATH} → {OUTPUT_PATH}")
+    print("This may take several minutes...")
+
+    quantizer.quantize_model(
+        model_input=str(MODEL_PATH),
+        model_output=str(OUTPUT_PATH),
+        calibration_data_reader=calib_reader,
+    )
+
+    print(f"\nQuantized model saved to {OUTPUT_PATH}")
+    print(f"Size: {OUTPUT_PATH.stat().st_size / 1024 / 1024:.1f} MB")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/quantized_info.csv b/quantized_info.csv
new file mode 100644
index 0000000..f5fcc24
--- /dev/null
+++ b/quantized_info.csv
@@ -0,0 +1,151 @@
+/home/breadway/.cache/breadsearch/models/model.onnx quantization quantization info,2026-06-24 18:14:20
+quantization stage,time consumed(s),sub stage,time consumed(s)
+pre process,5.43
+,,calibration: collect data (onnx inference + numpy statistics),16.60
+,,calibration: compute data,0.01
+calibration (collect data + compute data),19.75
+static quantization,0.81
+post process(including finetuning),0.00
+e2e,40.621040006999465
+
+Node Name,Op Type,Activation,Weights,Bias
+/encoder/layers.0/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.0/attn/Reshape,Reshape,INT8,,
+/encoder/layers.0/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.0/Add,Add,INT8,,
+/encoder/layers.0/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.0/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.0/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.0/mlp/Mul,Mul,INT8,,
+/encoder/layers.0/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.0/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.0/Add_1,Add,INT8,,
+/encoder/layers.1/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.1/attn/Reshape,Reshape,INT8,,
+/encoder/layers.1/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.1/Add,Add,INT8,,
+/encoder/layers.1/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.1/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.1/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.1/mlp/Mul,Mul,INT8,,
+/encoder/layers.1/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.1/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.1/Add_1,Add,INT8,,
+/encoder/layers.2/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.2/attn/Reshape,Reshape,INT8,,
+/encoder/layers.2/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.2/Add,Add,INT8,,
+/encoder/layers.2/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.2/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.2/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.2/mlp/Mul,Mul,INT8,,
+/encoder/layers.2/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.2/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.2/Add_1,Add,INT8,,
+/encoder/layers.3/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.3/attn/Reshape,Reshape,INT8,,
+/encoder/layers.3/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.3/Add,Add,INT8,,
+/encoder/layers.3/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.3/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.3/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.3/mlp/Mul,Mul,INT8,,
+/encoder/layers.3/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.3/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.3/Add_1,Add,INT8,,
+/encoder/layers.4/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.4/attn/Reshape,Reshape,INT8,,
+/encoder/layers.4/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.4/Add,Add,INT8,,
+/encoder/layers.4/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.4/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.4/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.4/mlp/Mul,Mul,INT8,,
+/encoder/layers.4/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.4/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.4/Add_1,Add,INT8,,
+/encoder/layers.5/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.5/attn/Reshape,Reshape,INT8,,
+/encoder/layers.5/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.5/Add,Add,INT8,,
+/encoder/layers.5/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.5/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.5/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.5/mlp/Mul,Mul,INT8,,
+/encoder/layers.5/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.5/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.5/Add_1,Add,INT8,,
+/encoder/layers.6/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.6/attn/Reshape,Reshape,INT8,,
+/encoder/layers.6/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.6/Add,Add,INT8,,
+/encoder/layers.6/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.6/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.6/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.6/mlp/Mul,Mul,INT8,,
+/encoder/layers.6/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.6/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.6/Add_1,Add,INT8,,
+/encoder/layers.7/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.7/attn/Reshape,Reshape,INT8,,
+/encoder/layers.7/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.7/Add,Add,INT8,,
+/encoder/layers.7/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.7/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.7/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.7/mlp/Mul,Mul,INT8,,
+/encoder/layers.7/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.7/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.7/Add_1,Add,INT8,,
+/encoder/layers.8/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.8/attn/Reshape,Reshape,INT8,,
+/encoder/layers.8/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.8/Add,Add,INT8,,
+/encoder/layers.8/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.8/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.8/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.8/mlp/Mul,Mul,INT8,,
+/encoder/layers.8/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.8/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.8/Add_1,Add,INT8,,
+/encoder/layers.9/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.9/attn/Reshape,Reshape,INT8,,
+/encoder/layers.9/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.9/Add,Add,INT8,,
+/encoder/layers.9/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.9/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.9/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.9/mlp/Mul,Mul,INT8,,
+/encoder/layers.9/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.9/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.9/Add_1,Add,INT8,,
+/encoder/layers.10/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.10/attn/Reshape,Reshape,INT8,,
+/encoder/layers.10/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.10/Add,Add,INT8,,
+/encoder/layers.10/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.10/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.10/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.10/mlp/Mul,Mul,INT8,,
+/encoder/layers.10/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.10/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.10/Add_1,Add,INT8,,
+/encoder/layers.11/attn/Wqkv/MatMul,MatMul,INT8,INT8,
+/encoder/layers.11/attn/Reshape,Reshape,INT8,,
+/encoder/layers.11/attn/out_proj/MatMul,MatMul,INT8,INT8,
+/encoder/layers.11/Add,Add,INT8,,
+/encoder/layers.11/mlp/fc12/MatMul,MatMul,INT8,INT8,
+/encoder/layers.11/mlp/fc11/MatMul,MatMul,INT8,INT8,
+/encoder/layers.11/mlp/Sigmoid,Sigmoid,INT8,,
+/encoder/layers.11/mlp/Mul,Mul,INT8,,
+/encoder/layers.11/mlp/Mul_1,Mul,INT8,,
+/encoder/layers.11/mlp/fc2/MatMul,MatMul,INT8,INT8,
+/encoder/layers.11/Add_1,Add,INT8,,
+
+Op Type,Activation,Weights,Bias
+MatMul,INT8(60),INT8(60),
+Reshape,INT8(12),,
+Add,INT8(24),,
+Sigmoid,INT8(12),,
+Mul,INT8(24),,
+
diff --git a/test_npu_quantized.py b/test_npu_quantized.py
new file mode 100644
index 0000000..0f122af
--- /dev/null
+++ b/test_npu_quantized.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+Test VitisAI EP with the quantized nomic-embed-text-v1.5 model.
+Checks whether the NPU VAIML pass achieves meaningful GOPs coverage.
+"""
+
+import os
+import json
+import time
+import numpy as np
+from pathlib import Path
+
+MODEL_PATH = Path.home() / ".cache/breadsearch/models/model_quantized_static.onnx"
+CACHE_DIR = Path.home() / ".cache/breadsearch/npu/nomic-quantized-static"
+VAIP_CONFIG = Path.home() / ".config/breadsearch/vaip_config.json"
+RYZEN_AI_LIB = Path.home() / ".local/share/ryzen-ai-1.7.1/lib"
+
+# libvaiml.so must be discoverable
+os.environ["RYZEN_AI_INSTALLATION_PATH"] = str(RYZEN_AI_LIB)
+os.environ["LD_LIBRARY_PATH"] = str(RYZEN_AI_LIB) + ":" + os.environ.get("LD_LIBRARY_PATH", "")
+
+CACHE_DIR.mkdir(parents=True, exist_ok=True)
+
+if not VAIP_CONFIG.exists():
+    print(f"ERROR: vaip_config.json not found at {VAIP_CONFIG}")
+    print("Check breadmill embed.rs for the find_vaip_config() paths")
+    exit(1)
+
+print(f"Testing quantized model: {MODEL_PATH}")
+print(f"Cache dir: {CACHE_DIR}")
+print(f"VAIP config: {VAIP_CONFIG}")
+print(f"RYZEN_AI_INSTALLATION_PATH: {os.environ['RYZEN_AI_INSTALLATION_PATH']}")
+
+import onnxruntime as ort
+
+providers = [
+    ("VitisAIExecutionProvider", {
+        "config_file": str(VAIP_CONFIG),
+        "cacheDir": str(CACHE_DIR),
+        "cacheKey": "nomic-quantized-static",
+    }),
+    "CPUExecutionProvider",
+]
+
+print("\nCreating InferenceSession with VitisAI EP...")
+t0 = time.time()
+try:
+    sess = ort.InferenceSession(str(MODEL_PATH), providers=providers)
+    t1 = time.time()
+    print(f"Session created in {t1-t0:.1f}s")
+    print(f"Active providers: {sess.get_providers()}")
+except Exception as e:
+    print(f"ERROR creating session: {e}")
+    exit(1)
+
+# Check for the VAIML pass summary
+summary_path = CACHE_DIR / "nomic-quantized" / "preliminary-vaiml-pass-summary.txt"
+if not summary_path.exists():
+    # Try variations
+    for p in CACHE_DIR.rglob("preliminary-vaiml-pass-summary.txt"):
+        summary_path = p
+        break
+
+if summary_path.exists():
+    print(f"\n--- VAIML Pass Summary ---")
+    print(summary_path.read_text())
+else:
+    print(f"\nNo VAIML summary found at {summary_path}")
+    print("Files in cache dir:")
+    for f in CACHE_DIR.rglob("*"):
+        if f.is_file():
+            print(f"  {f}")
+
+# Run a quick inference test
+print("\nRunning inference test...")
+seq_len = 128
+dummy_ids = np.ones((1, seq_len), dtype=np.int64)
+dummy_mask = np.ones((1, seq_len), dtype=np.int64)
+dummy_types = np.zeros((1, seq_len), dtype=np.int64)
+
+input_names = [inp.name for inp in sess.get_inputs()]
+print(f"Input names: {input_names}")
+
+feed = {}
+for name in input_names:
+    if "type" in name:
+        feed[name] = dummy_types
+    elif "mask" in name:
+        feed[name] = dummy_mask
+    else:
+        feed[name] = dummy_ids
+
+t0 = time.time()
+outputs = sess.run(None, feed)
+t1 = time.time()
+print(f"Inference completed in {(t1-t0)*1000:.1f}ms")
+print(f"Output shape: {outputs[0].shape}")