PipeOwl-1.2(Geometric Embedding)

A transformer-free semantic retrieval engine.

PipeOwl performs deterministic vocabulary scoring over a static embedding field:

score = α⋅base + β⋅Δfield

where:

• base = cosine similarity in embedding space
• Δfield = static scalar field bias

Features:

• O(n) over vocabulary.
• No attention.
• No transformer weights.

Patch Note

1.1

• fix OOV
• symbolic fallback
• english fallback
• japanese fallback
• PipeOwlConfig improvement
• Tokenizer: max_len cap
• load_assets: contiguous + row-normalize
• small benchmark

1.2

• safetensors support

Architecture

• Static embedding table (V × D)
• Aligned vocabulary index
• Optional scalar bias field
• Linear scoring
• Pluggable decoder stage
• Targeted for CPU environments and low-latency systems (e.g. IME).
• Single static field (~635MB), no runtime model weights.

Attribution

The base embedding vectors were generated using BGE-M3 (Apache-2.0) via inference. This repository does not redistribute any original BGE weights.

Quickstart

pip install numpy safetensors
python quickstart.py

See full experimental notes here:

https://hackmd.io/@galaxy4552/SJ5DatsuZx

Repository Structure

pipeowl1.2/
 ├ README.md
 ├ config.json
 ├ LICENSE
 ├ quickstart.py
 ├ pipeowl.safetensors
 ├ vocabulary.json
 └ engine.py

PipeOwl 是一個基於靜態語義場的幾何檢索系統。

核心公式：

score = α⋅base + β⋅Δfield

其中：

• base = embedding cosine similarity
• delta = 靜態場偏移量
• α / β 為可調權重

提供一種 O(n) 的輕量語義計分方法， 適合低延遲環境（如輸入法）。

LICENSE

MIT