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).
41 lines
1.5 KiB
Markdown
41 lines
1.5 KiB
Markdown
# ADR-001: Python with uv
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## Status
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Accepted
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## Context
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The project needs a programming language and build toolchain. The PoC was
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written in Python using PyTorch, sklearn, and transformers. A Rust port using
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burn/cubecl was attempted but failed — the ML framework ecosystem in Rust is
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not yet mature enough for this type of work.
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The project needs a fast path to a usable system. The PoC already works in
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Python. Modern Python packaging (uv, pyproject.toml, src layout) provides a
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professional project structure that was not available even a few years ago.
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## Decision
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Use Python 3.10+ with uv as the package manager and build tool. Use uv_build
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as the build backend. Use src/ layout for the package.
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## Consequences
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**Positive**:
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- Fast path to working system — PoC code is already Python
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- Rich ML ecosystem (PyTorch, transformers, sklearn, safetensors)
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- uv provides 10-100x faster dependency management than pip
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- Modern packaging standards (pyproject.toml, PEP 735 dependency groups)
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- Easy distribution via PyPI with `pip install alknet-firewall[torch]`
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- Type checking via mypy provides strong correctness guarantees
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**Negative**:
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- Python is slower than Rust for non-ML code (SVD projection, data wrangling)
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- PyTorch is a large optional dependency (200MB-2.5GB)
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- Rust port remains a future goal (Phase 3, speculative)
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## References
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- [modern-python-project-setup.md](../research/modern-python-project-setup.md)
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- [python-ml-packaging.md](../research/python-ml-packaging.md) |