Kjell Tore Guttormsen
a9c4ccd8c7
Build order steps 1-2 of docs/PLAN.md: - pyproject.toml (llm-ingestion-guard, stdlib-only core, extras [ml]/[judge]/[dev]), LICENSE (MIT) - report: Finding/Report/Severity/Source shared type (pure data) - sanitize: carrier stripping (zero-width, BIDI, Unicode-tag, HTML comment, data: URI) with the byte-identical / removes-only invariant - docs/PLAN.md: v1 implementation plan (positioning A, gap-expanded scope, llm-security reuse map) 15 tests passing. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01K8GmKRCdsPjWYAKWsNgeQS
34 lines
1.1 KiB
TOML
34 lines
1.1 KiB
TOML
[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "llm-ingestion-guard"
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version = "0.1.0"
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description = "A minimal, dependency-light defensive layer for LLM ingestion pipelines — the write-time siblings of query-time chatbot guardrails."
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readme = "README.md"
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requires-python = ">=3.10"
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license = { file = "LICENSE" }
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authors = [{ name = "Kjell Tore Guttormsen" }]
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keywords = ["llm", "security", "prompt-injection", "rag", "ingestion", "guardrails", "write-time"]
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"License :: OSI Approved :: MIT License",
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"Programming Language :: Python :: 3",
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"Topic :: Security",
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]
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dependencies = [] # stdlib-only core — see design principle 1
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[project.optional-dependencies]
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ml = [] # pluggable embedding/classifier detectors (placeholder)
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judge = [] # LLM-judge / source-grounding implementation (placeholder)
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dev = ["pytest>=8"]
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[tool.hatch.build.targets.wheel]
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packages = ["src/llm_ingestion_guard"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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pythonpath = ["src"]
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addopts = "-q"
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