notelight

Private semantic search over your own files, for you and your AI agents. A Rust retrieval engine with a Python interface, a CLI, and an MCP server, with retrieval quality you can measure.

Status: 0.1.0, alpha. You can index a folder of notes, search it from the command line or from Claude, Cursor and other AI agents over MCP, and measure retrieval quality on public benchmarks (results). A localhost web UI and PDF/DOCX support are next (0.2). See the roadmap and the changelog.

Why

Tools that let you "chat with your files" usually need a server, an account, or an upload. notelight keeps everything on your machine:

• Hybrid retrieval. BM25 plus quantized embeddings, fused with Reciprocal Rank Fusion, so both exact names and paraphrased questions work. Quote a phrase ("lock file") to put exact matches first.
• Agent-ready. An MCP server lets Claude, Copilot or Cursor search your notes and cite path:line.
• Measured. notelight eval reports recall@10 and nDCG@10 on BEIR datasets for every quantization setting, not just anecdotes.
• Private. No telemetry, and no network after a one-time model download.

Usage

Install it from PyPI (Python 3.10+), ideally into a virtual environment or with pipx install notelight:

pip install notelight

Not on PyPI yet: 0.1.0 is built and tested but not published. Until it is, install from GitHub with pip install git+https://github.com/searope/notelight (needs Rust, see below).

There are prebuilt wheels for Linux (x86_64 and aarch64, glibc 2.28+), Windows (x64) and macOS (Apple silicon and Intel), so you do not need Rust. Two limits come from ONNX Runtime, which runs the embedding model: on Intel Macs use Python 3.10 to 3.13, and Alpine and other musl-based Linux distributions are not supported. On any other platform pip builds from the source distribution, which needs Rust 1.88+ (and the MSVC C++ build tools on Windows). To install the latest unreleased code, use pip install git+https://github.com/searope/notelight (also needs Rust), or see Development.

Index a folder of Markdown and text files, then search it. This is a real run over this repository's docs/ folder on a laptop CPU:

$ notelight index docs
Downloading onnx/model.onnx (133 MB, one time)...
Embedding 62 new chunks...
D:\Projects\notelight\docs: 5 files (5 added, 0 changed, 0 removed), 62 chunks, 62 embedded, generation 1, 4.9s

$ notelight search "what happens when two processes index at once" -k 2
 1. design-notes.md:153-161  Design notes > Milestone 2 decisions > One writer, enforced with a lock file
    ### One writer, enforced with a lock file The core assumes a single writer: `commit` sweeps up leftovers from earlier failed commits, which would be…
 2. learning/m1-rust-walkthrough.md:82-88  Milestone 1: Rust walkthrough > 5. Validate once, then index freely (`Bm25::validate`)
    ## 5. Validate once, then index freely (`Bm25::validate`) `from_bytes` checks every offset table, every term and every posting…

Run notelight index again after editing your notes. Only new or changed chunks are embedded. On an unchanged folder the model is never loaded, so the re-run takes about as long as starting the CLI (0.5 s for the folder above).

Options:

• --embedder bge-small|hashing|none: bge-small (the default for a new index) is BAAI/bge-small-en-v1.5, pinned to one revision and verified by SHA-256. none builds a keyword-only (BM25) index with no model. Without the flag, re-indexing keeps the embedder the index was built with, and search uses it automatically and refuses a different one.
• --offline: never download; fail if the model is not cached.
• Files: .md, .markdown and .txt, in UTF-8 or Windows-1252, plus .pdf and .docx with the optional extras (below). Hidden folders (.git, .obsidian, ...), node_modules, target, build and similar are skipped, as is anything matched by a .notelightignore file (.gitignore syntax) in the folder root.
• Indexes are stored in your user data directory, never in the indexed folder. Set NOTELIGHT_HOME to keep them elsewhere, or NOTELIGHT_MODEL_CACHE to share one model download.

From Python:

from notelight import OnnxEmbedder, Searcher, sync

embedder = OnnxEmbedder()
sync("~/notes", embedder)
for hit in Searcher("~/notes", embedder).search("release hotfixes", k=5):
    print(hit.location, hit.heading_path)

PDF and DOCX

PDF and DOCX support is optional, so the base install stays small:

pip install "notelight[pdf]"        # pypdf
pip install "notelight[docx]"       # python-docx
pip install "notelight[documents]"  # both

Without the extra, these files are listed as skipped with the reason (install notelight[pdf]), and the next notelight index after installing it picks them up. Encrypted (password-protected), scanned or image-only and damaged PDFs are skipped with a reason; notelight does no OCR.

Citations work as for text files, with line numbers counted in the extracted text, which read_context serves to agents. PDF hits also name their pages, and DOCX headings (Heading 1-6) become heading paths:

 1. papers/report.pdf:41-52 (p. 3)
    Glaciers retreat as summers warm…
 2. trips/alps.docx:9-15  Mountains > Huts
    Refuge | 2800 m…

Use with Claude / Cursor

notelight mcp is a Model Context Protocol server (built on the official Python SDK, over stdio). It gives an agent four read-only tools:

Index first (notelight index <folder>); the server only reads. It never takes the indexer's lock, so you can re-index while it runs, and it picks up the new generation on the next call. read_context only reads files that are in the index: paths with .., files outside the folder, and hidden or ignored files are refused. With no --folder, every indexed folder is served; pass --folder (repeatable) to expose only some of them.

The configs below need the full path to the notelight executable, because desktop apps do not see your shell's PATH or virtual environment. Find it with (Get-Command notelight).Source in PowerShell or which notelight.

Claude Desktop: edit claude_desktop_config.json (Settings → Developer → Edit Config; on Windows it is %APPDATA%\Claude\claude_desktop_config.json), then restart Claude:

{
  "mcpServers": {
    "notelight": {
      "command": "C:\\Users\\you\\.venvs\\notelight\\Scripts\\notelight.exe",
      "args": ["mcp", "--folder", "C:\\Users\\you\\Notes"]
    }
  }
}

Claude Code: add a .mcp.json to your project root (shared with everyone who opens the project), or run claude mcp add notelight -- notelight mcp for yourself only:

{
  "mcpServers": {
    "notelight": {
      "type": "stdio",
      "command": "notelight",
      "args": ["mcp"]
    }
  }
}

Cursor: the same mcpServers block goes in .cursor/mcp.json (one project) or ~/.cursor/mcp.json (all projects).

Then ask, for example: "Search my notes for how we release hotfixes and cite the files." The agent calls search, reads around the best hits with read_context, and answers with locations like runbooks/release.md:40-58.

The tool calls and results in this GIF come from a real session: the script indexes this repository's docs/ folder with bge-small, starts notelight mcp over stdio, calls the tools, and draws exactly what comes back. To regenerate it after changing search code (the model must be cached):

pip install pillow
python scripts/make_demo_gif.py --query "what happens if I re-index while the agent is searching"

Web UI

notelight serve opens the same search in a browser, for people rather than agents:

pip install "notelight[web]"
notelight serve --open             # http://127.0.0.1:8765, every indexed folder

Type to search (results update as you type), filter by folder, and click a result, or move with the arrow keys or j/k and press Enter, to read the lines around it. Each folder shows whether its index is up to date. The page is one HTML file with plain JavaScript, no build step and no CDN, in light and dark themes. It works at phone width too.

The server listens on 127.0.0.1 only, never changes an index, and reads only files the index covers, through the same checks as the MCP server's read_context. It refuses requests whose Host header is not a loopback name, which blocks DNS-rebinding attacks from other web pages.

Architecture

            ┌────────────── notelight-core (Rust) ─────────────┐
            │ chunking · BM25 · ternary + int8 search · RRF    │
            │ memory-mapped, versioned index format            │
            └───────┬──────────────────────────────┬───────────┘
              PyO3 / maturin                 wasm-bindgen (v2)
                    │                              │
   notelight (Python package)              browser app (v2)
   embeddings via ONNX Runtime
   CLI · MCP server · eval harness · localhost UI

The core crate has no Python, ML or async dependencies. That keeps it small, easy to test, and portable to WebAssembly. Embeddings run in Python, where the ML tooling is.

Performance (Rust core)

Measured with cargo bench -p notelight-core on synthetic data, on one thread of an Intel Core i9-10900K (Windows 11, default x86-64 target, so no AVX). These are indicative numbers, not a tuned benchmark:

The default two-stage search is about 5× faster than scanning every int8 vector, because the ternary pass compares 96 bytes per vector (sign bits and mask) with popcount, and only 200 candidates are re-scored. Binary candidates halve the scan but lose up to 0.01 nDCG@10 (below). The int8 dot product keeps 16 partial sums so the compiler can use SIMD; see the note on vectorizing it.

Retrieval quality

nDCG@10 on three BEIR test sets with the default bge-small embedder, measured with notelight eval (one command, datasets pinned by SHA-256; see docs/results.md for recall@10, latency and the setup):

The default vector search, ternary candidates re-scored with int8, matches exhaustive int8 to three decimals and is within 0.002 of exact float32 search; binary candidates lose up to 0.01. Hybrid fusion helps where keywords are strong (NFCorpus) and hurts where they are weak (FiQA). Against a NumPy implementation of the same quantized searches, the Rust core returns identical top-10 lists; its two-stage searches are 7 to 19x faster (10 to 19x for the ternary default), while NumPy's multi-threaded BLAS still wins the exhaustive int8 scan by 1.3 to 1.7x (it was 5x before the int8 dot product was vectorized). Details and caveats are in docs/results.md.

notelight eval                     # downloads 23 MB of data once; FiQA takes ~40 min to embed on a CPU

Development

Requirements: Rust (stable) and Python 3.10+.

git clone https://github.com/searope/notelight
cd notelight
python -m venv .venv
# Windows: .venv\Scripts\activate   |   macOS/Linux: source .venv/bin/activate
pip install -e ".[dev,web]"    # builds the Rust extension via maturin; web: web UI + its tests
pytest                         # Python tests (no network; uses a fake embedder)
cargo test -p notelight-core   # Rust unit, integration and property tests
cargo bench -p notelight-core  # Rust benchmarks
notelight version

After changing Rust code, rebuild with maturin develop --release.

Roadmap

Documentation

• Design notes: why it is built this way
• Index format: the on-disk layout, byte by byte
• Results: retrieval quality and latency on BEIR, with the Rust-vs-NumPy comparison
• Rust walkthroughs for readers coming from C# or Python: milestone 0, milestone 1, milestone 2, milestone 3 (plus vectorizing the int8 dot product), milestone 4

License

Dual-licensed under MIT or Apache-2.0, at your option.