mnemos-embedkit 0.1.0a1

Open embedding devkit — same API across NPU / GPU / CPU silicon (Cix, NVIDIA, AMD, Intel, Apple, Rockchip, MediaTek)

📍 Moved to GitLab

The canonical, authoritative home of this project is GitLab — always:

👉 https://gitlab.com/ncz-os/mnemos-embedkit

This GitHub repository is a frozen, read-only mirror. All development, issues, and releases happen on GitLab. Please open issues and merge requests there. The full history of this stub is preserved on GitLab.

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mnemos-embedkit

Open embedding devkit. Same API, every silicon.

embedkit lets you embed text once and run it on whatever hardware your box has — Cix Sky1 NPU, Apple Silicon Metal/MLX, NVIDIA CUDA/TensorRT, AMD ROCm/XDNA, Intel iGPU/NPU via OpenVINO, MediaTek APU, Rockchip RKNN, or just the CPU. The kit detects what's installed and picks the fastest adapter at runtime. No vendor preference.

Quick start

import embedkit

eng = embedkit.Engine.auto()              # picks the fastest adapter on this host
vec = eng.embed("Hello world")            # -> List[float]
vecs = eng.embed_batch(["a", "b", "c"])   # -> List[List[float]]

eng.info()
# {"adapter": "cix-npu", "model": "bge-small-zh-v1.5_256.cix",
#  "embed_dim": 512, "max_tokens": 256, "throughput_baseline": 55.0}

Explicit adapter pick:

eng = embedkit.Engine(adapter="cix-npu",      model="bge-small-zh-v1.5")
eng = embedkit.Engine(adapter="nvidia-cuda",  model="nomic-embed-text-v1.5")
eng = embedkit.Engine(adapter="amd-rocm",     model="bge-large-en-v1.5")
eng = embedkit.Engine(adapter="apple-mlx",    model="mxbai-embed-large-v1")
eng = embedkit.Engine(adapter="cpu-llamacpp", model="bge-small-zh-v1.5")

What the kit is

A pure-Python adapter layer over vendor-specific embedding runtimes, plus a uniform Engine.embed* API and a canonical bench harness. The kit does not bundle drivers or kernel modules. It detects what the host OS already provides and binds to it:

Host has	Kit picks via
cix-noe-umd 2.0.2 + libnoe (NCZ Magnetar / cixtech apt)	npu-cix adapter
onnxruntime-gpu (CUDA driver from Linux distro)	nvidia-cuda adapter
tensorrt python (NVIDIA tar/apt)	nvidia-trt adapter
onnxruntime-rocm (AMD ROCm dkms)	amd-rocm adapter
onnxruntime-vitisai (XDNA driver)	amd-xdna adapter
openvino (Intel CPU/iGPU/NPU)	intel-igpu / intel-npu adapter
mlx (Apple Silicon, macOS only)	apple-mlx adapter
llama-cpp-python with Metal	cpu-llamacpp adapter (auto-detects Metal at runtime)
llama-cpp-python with -DGGML_VULKAN=1	gpu-vulkan adapter
rknn-toolkit2 (Rockchip RK3588 / RK3576)	rockchip-rknn adapter
mtk-genio-apu (MediaTek Genio)	mediatek-apu adapter
nothing else	cpu-llamacpp (CPU baseline, ships GGUF)

Install

# Pick the form-factor bundle that matches your host:
pip install embedkit[all-cpu]                 # baseline, CPU only
pip install embedkit[all-x86-cuda]            # CPU + NVIDIA CUDA
pip install embedkit[all-x86-rocm]            # CPU + AMD ROCm + XDNA
pip install embedkit[all-x86-intel]           # CPU + Intel iGPU + NPU via OpenVINO
pip install embedkit[all-arm-cix]             # CPU + Cix NPU + Mali Vulkan
pip install embedkit[all-arm-rockchip]        # CPU + Rockchip RKNN + Mali Vulkan
pip install embedkit[all-apple]               # CPU + Apple MLX + Metal
pip install embedkit[all]                     # everything

The kit pulls vendor python bindings from PyPI. Vendor drivers are managed by your OS package manager (cix-noe-umd via apt, nvidia-driver via ubuntu-drivers, rocm-dkms via amdgpu-install, intel-npu-driver via apt, etc.).

Reference bench

The canonical multi-platform bench is in benches/. Run on your host:

embedkit-bench --corpus benches/corpora/mnemos-8038.json --engines auto

See benches/results.md for the cross-platform numbers we have today (Cix Sky1 NPU, Apple Silicon Metal, NVIDIA CUDA, x86 + ARM CPU, Pi 5, Pi 4).

Reference implementation consumer

ncz-os/mnemos (the canonical MNEMOS memory layer) is the reference embedkit consumer. The plan is to migrate MNEMOS's embedding helper to call embedkit.Engine(...) directly. See docs/mnemos-integration.md.

License

Apache-2.0.

Status

Bootstrap. Design + cross-platform bench data exist. Adapter implementations are queued (Codex handoff prompt at docs/CODEX-ADAPTER-HANDOFF.md).

See docs/DESIGN.md for the full architecture.

Build infrastructure & partners

Continuous integration and package distribution for this project are generously supported by our open-source infrastructure partners:

• GitLab — canonical source hosting and CI pipelines (format / lint / test gates), via the GitLab for Open Source program.
• Buildkite — CI/CD orchestration with hosted macOS and Linux agents, and our APT package registry host (packages.buildkite.com/ncz-os/ncz), via the Buildkite Open Source program.

Thank you to both for backing open-source software.