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 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| liumi_vectorcore-1.0.1-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| liumi_vectorcore-1.0.1-cp39-abi3-macosx_10_15_universal2.whl | CPython 3.9 | abi3 | macOS 10.15+ universal2 (ARM64, x86-64) | Details |
Total release size: 334.5 kB
Release files / liumi_vectorcore-1.0.1-cp39-abi3-win_amd64.whl
| Download URL | liumi_vectorcore-1.0.1-cp39-abi3-win_amd64.whl |
|---|---|
| Size | 90.2 kB |
| Tags | CPython 3.9 Windows x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
a1c1fd623804bc188096aa85fa9704aa1e41dea2ca06183b0f2b3b97a34c521b
|
|
BLAKE2b-256 checksum How to use checksums |
f1c4cb9d28452d2346a7245c32112da87eb44981eadbef6be8eae35393841c1a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / liumi_vectorcore-1.0.1-cp39-abi3-macosx_10_15_universal2.whl
| Download URL | liumi_vectorcore-1.0.1-cp39-abi3-macosx_10_15_universal2.whl |
|---|---|
| Size | 244.2 kB |
| Tags | CPython 3.9 abi3 macOS 10.15+ universal2 (ARM64, x86-64) |
|
SHA-256 checksum How to use checksums |
c27514565ee7129fe91aa2d0c1d350864032259c42582a18335bbd74bf40ac15
|
|
BLAKE2b-256 checksum How to use checksums |
c67062c3166fd1d9076c09cdd1a9fce700517daa21a303f276f45ccf4ba6f4aa
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.6
|