ROCm silently fell back to CPU: the code targeted ONNX Runtime's classic ROCMExecutionProvider, but distro ROCm-enabled ONNX Runtime builds (e.g. Arch's onnxruntime-rocm) are commonly compiled with --use_migraphx instead, and registration failures were invisible since breadmill never installed a tracing subscriber. Switches the rocm feature to target MIGraphX, adds a default tracing subscriber so EP registration success/failure is always visible, and fixes a real crash where MIGraphX's output sequence padding could index the attention mask out of bounds during mean-pooling. Also adds a CUDA backend (--cuda / backend = "cuda") mirroring the same ort execution-provider pattern, for NVIDIA hardware. Version bump: 0.1.0 -> 0.2.0.
180 lines
6.4 KiB
Markdown
180 lines
6.4 KiB
Markdown
# breadsearch
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Semantic document search for Bread OS. Type a concept — not a keyword — and get ranked hits from your documents.
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Two binaries in one Cargo workspace:
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- **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.
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- **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.
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## Build
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System dependencies: `gtk4`, `gtk4-layer-shell`, `librsvg`.
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```
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cargo build --release
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```
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The `ort` crate downloads a bundled ONNX Runtime at build time (no system `onnxruntime` needed for the default CPU build).
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Optional features:
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| Feature | What it adds |
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|---------|-------------|
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| `npu` | AMD XDNA NPU via VitisAI ONNX Runtime EP (requires Ryzen AI SDK) |
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| `rocm` | AMD iGPU via the MIGraphX ONNX Runtime EP (ROCm-backed) |
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| `cuda` | NVIDIA GPU via the CUDA ONNX Runtime EP |
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```
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# NPU build
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cargo build --release -p breadmill --features npu
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# ROCm (AMD iGPU/dGPU) build
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cargo build --release -p breadmill --features rocm
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# CUDA (NVIDIA GPU) build
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cargo build --release -p breadmill --features cuda
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```
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`rocm`/`cuda`/`npu` all use `ort`'s `load-dynamic` mode: at runtime, breadmill
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dlopens whatever `libonnxruntime.so` the dynamic linker resolves (or
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`ORT_DYLIB_PATH` if set). GPU acceleration only works if that ONNX Runtime
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build actually has the matching execution provider compiled in — breadmill
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logs a clear `Successfully registered` / `not enabled in this build` line for
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this at startup (see [GPU backend notes](#gpu-backend-notes) below).
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## Setup
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**1. Fetch the embedding model** (~550 MB, downloaded once from Hugging Face):
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```
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breadmill fetch-model
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```
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Model files are stored in `~/.cache/breadsearch/models/`.
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**2. Enable the systemd user service:**
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```
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cp packaging/breadmill.service ~/.config/systemd/user/
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systemctl --user daemon-reload
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systemctl --user enable --now breadmill
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```
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Or run it directly: `breadmill serve` (or just `breadmill`).
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**3. Copy the example config** (optional — built-in defaults are used otherwise):
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```
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mkdir -p ~/.config/breadsearch
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cp config.example.toml ~/.config/breadsearch/config.toml
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```
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## breadsearch (GUI)
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```
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breadsearch
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```
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Invoking again while running closes the window (PID-toggle). Bind to a hotkey in your compositor config.
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Results show: filename/title, full path, and a snippet from the matching chunk. Score is cosine similarity as a percentage.
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User CSS overrides go in `~/.config/breadsearch/style.css`.
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## breadmill (daemon / CLI)
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```
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# Start the daemon (also the default when called with no arguments)
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breadmill serve
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# Force a full re-index from scratch
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breadmill reindex
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# Download model files
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breadmill fetch-model
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# Query from the terminal
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breadmill query "tax stuff"
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# Show daemon status (chunks indexed, pending, model ready)
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breadmill status
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# Backend flags (requires the matching Cargo feature)
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breadmill --npu
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breadmill --rocm
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breadmill --cuda
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```
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## Config
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`~/.config/breadsearch/config.toml` — all keys are optional; built-in defaults are shown.
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```toml
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[index]
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roots = ["~/Documents", "~/Projects", "~/.config/breadpad"]
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extensions = ["md", "txt", "org", "pdf", "odt", "docx"]
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excludes = [] # paths to skip (prefix match)
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max_file_mb = 10.0
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[search]
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limit = 10 # max results per query
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snippet_len = 200 # max characters in result snippet
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[model]
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name = "nomic-embed-text-v1.5"
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dim = 768
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backend = "cpu" # "cpu", "npu", "rocm", or "cuda"
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```
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`roots` and `excludes` support `~/` expansion. The index respects `.gitignore` files found during the walk.
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### NPU backend
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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:
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1. `$VAIP_CONFIG`
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2. `~/.config/breadsearch/vaip_config.json`
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3. `~/.local/share/ryzen-ai-1.7.1/voe-4.0-linux_x86_64/vaip_config.json`
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4. `/etc/vaip_config.json`
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5. `/opt/xilinx/vaip_config.json`
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### GPU backend notes
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Both `rocm` and `cuda` need a system ONNX Runtime that was actually built with
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the matching execution provider — the crate's own downloaded binary is CPU-only.
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Point `ORT_DYLIB_PATH` at one, or install a distro package that provides
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`libonnxruntime.so` with the EP baked in and let the dynamic linker find it.
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**ROCm (`--rocm` / `backend = "rocm"`)** targets ONNX Runtime's **MIGraphX**
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execution provider, not the classic `ROCMExecutionProvider`. Distro
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ROCm-enabled ONNX Runtime packages (e.g. Arch's `onnxruntime-rocm`) are
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commonly built with `--use_migraphx` rather than `--use_rocm`, so this is the
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EP that's actually available in practice; the classic ROCm EP needs a bespoke
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`--use_rocm` build most distros don't package. Startup logs a
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`Successfully registered `MIGraphXExecutionProvider`` line when it's really
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active — check for it if in doubt, since a failed GPU EP registration falls
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back to CPU silently at the ONNX Runtime level (breadmill's own log line is
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only a statement of intent, not a confirmation).
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MIGraphX JIT-compiles the model per distinct input sequence length and caches
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the compiled kernel to disk (each compile takes ~60–120s and produces a
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~500MB `.mxr` file). Set `ORT_MIGRAPHX_MODEL_CACHE_PATH=/path/to/cache` so
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that cost is paid once per shape instead of on every daemon restart. Because
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query text length varies, expect an occasional multi-second stall the first
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time a new token length is seen — fine for background document indexing,
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noticeable for interactive query embedding.
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**CUDA (`--cuda` / `backend = "cuda"`)** targets the standard
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`CUDAExecutionProvider` and needs a CUDA-enabled ONNX Runtime + a working
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CUDA/cuDNN install. Unverified on real NVIDIA hardware in this repo — only
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compile-checked, since development happened on an AMD-only machine.
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## Runtime paths
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| Purpose | Path |
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|---------|------|
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| Config | `~/.config/breadsearch/` |
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| Index (SQLite + HNSW) | `~/.local/state/breadsearch/` |
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| Model cache | `~/.cache/breadsearch/models/` |
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| Unix socket | `$XDG_RUNTIME_DIR/breadmill.sock` |
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