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Liumi VectorCore 🚀

Sub‑2 ms SIMD‑accelerated INT8 vector search – zero‑copy .lvc format – runs entirely on‑device.


🎯 Why VectorCore?

Feature Benefit
SQ8 INT8 scalar quantisation (MSE‑optimal scaling) 4× memory reduction vs FP32, no accuracy loss for nearest‑neighbour search
AVX2 (x86‑64) + NEON (aarch64) runtime dispatch Same binary, optimal throughput on any modern CPU
Zero‑copy .lvc file format mmap‑load an index in < 1 ms, no deserialization overhead
Offline cryptographic licensing No phone‑home, no runtime licence server – just drop a key file
Python ≥ 3.9, single pip install cp39‑abi3 wheel → one binary works on 3.9‑3.13+

📦 Installation

pip install liumi-vectorcore   # pulls the cp39‑abi3 wheel from PyPI

Note: The wheel contains only the stripped native library (vector_core.dll / .so / .dylib) and typed Python stubs – no source code.


🚀 Quick‑Start (Community Tier – ≤ 5 000 vectors)

from lumi_vectorcore import VectorCore, SearchResult

vc = VectorCore(dim=128)                 # 128‑dim INT8 vectors
vecs = [bytes([i % 256 for i in range(128)]) for _ in range(3000)]
vc.insert(vecs)                          # < 5 ms total

query = bytes([10] * 128)
hits: list[SearchResult] = vc.search(query, k=10)
for h in hits:
    print(h.id, h.distance)

🔓 Unlock Commercial Tier (unlimited vectors, multi‑threaded)

vc = VectorCore(dim=256, license_key="BASE64_KEY_FROM_LIUMI")
vc.insert(huge_corpus)   # millions of vectors, parallelised internally

The key is verified offline – no network call ever.


📄 Persistence – Zero‑Copy .lvc

# Save
blob = vc.save_lvc()
with open("my_index.lvc", "wb") as f:
    f.write(blob)

# Load (mmap‑backed, instant)
with open("my_index.lvc", "rb") as f:
    vc2 = VectorCore.from_bytes(f.read())

📜 License

See LICENSE.txt – proprietary commercial EULA with a free Community tier.


🤝 Community vs Commercial

Tier Vectors / Index Threads License
Community ≤ 5 000 1 Free, no key required
Commercial Unlimited All cores Offline cryptographic key (purchased)

📈 Benchmarks (single‑core, Intel i7‑12700K)

Vectors Dim Insert (ms) Search k=10 (ms)
10 000 128 3.2 1.6
100 000 256 28 1.9

All numbers measured with time.perf_counter on a cold mmap load.


🙋 Support / Contact

  • Issues: GitHub → LiumiCorp/VectorCore (public tracker)
  • Commercial enquiries: sales@liumi.io
  • Security: security@liumi.io (GPG‑encrypted)

Liumi Corporation – “AI on the edge, at the speed of silicon.”

Metadata

Release files for liumi-vectorcore 1.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for liumi-vectorcore 1.0.2
File Interpreter ABI Platform
liumi_vectorcore-1.0.2-cp39-abi3-win_amd64.whl CPython 3.9 abi3 Windows x86-64 Details
liumi_vectorcore-1.0.2-cp39-abi3-macosx_10_15_universal2.whl CPython 3.9 abi3 macOS 10.15+ universal2 (ARM64, x86-64) Details

Total release size: 332.3 kB

Release files / liumi_vectorcore-1.0.2-cp39-abi3-win_amd64.whl

Download URL liumi_vectorcore-1.0.2-cp39-abi3-win_amd64.whl
Size 90.2 kB
Tags CPython 3.9 Windows x86-64 abi3
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Release files / liumi_vectorcore-1.0.2-cp39-abi3-macosx_10_15_universal2.whl

Download URL liumi_vectorcore-1.0.2-cp39-abi3-macosx_10_15_universal2.whl
Size 242.1 kB
Tags CPython 3.9 abi3 macOS 10.15+ universal2 (ARM64, x86-64)
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1.0.3

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1.0.1

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1.0.0

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