glm-5.1
cf464c2296
Phase 0→1 setup for alknet-firewall — a behavioral signal detection library that screens untrusted LLM inputs using small model activations. Architecture docs (5 specs, 10 ADRs, 7 open questions): - overview: vision, scope, dependencies, package structure - firewall: core API, alarm protocol, score composition, error handling - codebook: SVD basis, spline distributions, calibration, tensor format - model: activation extraction, model-agnostic interface, lazy loading - configuration: thresholds, model selection, detection tuning Research reports: - modern-python-project-setup: uv, pyproject.toml, src layout, ruff, CI - python-ml-packaging: optional PyTorch, HF Hub download, safetensors - llm-input-safety-landscape: threat taxonomy, defenses, academic evidence Agent role adaptations for Python project (replaced Rust conventions).
1.5 KiB
1.5 KiB
ADR-001: Python with uv
Status
Accepted
Context
The project needs a programming language and build toolchain. The PoC was written in Python using PyTorch, sklearn, and transformers. A Rust port using burn/cubecl was attempted but failed — the ML framework ecosystem in Rust is not yet mature enough for this type of work.
The project needs a fast path to a usable system. The PoC already works in Python. Modern Python packaging (uv, pyproject.toml, src layout) provides a professional project structure that was not available even a few years ago.
Decision
Use Python 3.10+ with uv as the package manager and build tool. Use uv_build as the build backend. Use src/ layout for the package.
Consequences
Positive:
- Fast path to working system — PoC code is already Python
- Rich ML ecosystem (PyTorch, transformers, sklearn, safetensors)
- uv provides 10-100x faster dependency management than pip
- Modern packaging standards (pyproject.toml, PEP 735 dependency groups)
- Easy distribution via PyPI with
pip install alknet-firewall[torch] - Type checking via mypy provides strong correctness guarantees
Negative:
- Python is slower than Rust for non-ML code (SVD projection, data wrangling)
- PyTorch is a large optional dependency (200MB-2.5GB)
- Rust port remains a future goal (Phase 3, speculative)