knowbase/pyproject.toml at master · v0ropaev/knowbase · GitHub

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[project]
name = "knowbase"
version = "0.8.0"
description = "Versioned, provenance-grounded knowledge layer over a codebase"
readme = "README.md"
requires-python = ">=3.12"
license = { text = "AGPL-3.0-or-later" }
authors = [{ name = "Dmitry Voropaev" }]
dependencies = [
    "tree-sitter>=0.25,<0.26",
    "grimp>=3,<4",
    "sqlalchemy>=2.0,<3",
    "psycopg[binary]>=3.1,<4",
    "alembic>=1.13",
    "pygit2>=1.14",
    "typer>=0.12",
    "pydantic>=2.6,<3",
    "tree-sitter-python>=0.25.0",
    "sqlparse>=0.5.5",
    "fastmcp>=3.3,<4",
    "pgvector>=0.4,<0.5",
    "pyyaml>=6",
]

[project.optional-dependencies]
dev = [
    "pytest>=8",
    "ruff>=0.6",
    "mypy>=1.10",
    # fastapi powers the eval oracle (kb introspect) and the Tier-1 API gate only; the serving
    # extractor is tree-sitter-static and never imports it.
    "fastapi>=0.110",
    "pytest-asyncio>=0.23",
    # griffe is the static, in-process independent oracle for the Tier-1 library-surface gate only;
    # the serving extractor is tree-sitter-static and never imports it.
    "griffe>=1.5,<3",
]
# Embedding backend for `kb embed` / search_knowledge / the RAG baseline. Heavy (torch) — kept out
# of the default install so `kb index` stays light; CI uses `uv sync --extra dev --extra embed`.
embed = [
    "sentence-transformers>=3,<6",
]
# LLM backend for the optional, nightly, NON-gating LLM-judged A/B (kb.llm + tier3_llm_judge_test).
# Not needed for index/serve/embed; the nightly workflow uses `uv sync --extra dev --extra embed
# --extra llm`. Also makes the existing OpenAI embedding adapter installable.
llm = [
    "anthropic>=0.40",
    "openai>=1.40",
]

[project.scripts]
kb = "kb.daemon.cli:app"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src/kb"]

# Pull torch from the CPU-only wheel index: the default Linux torch wheel bundles CUDA (~2GB+),
# which is useless here (embeddings run on CPU) and would bloat CI. macOS/linux CPU wheels both live
# here, so this keeps local and CI consistent.
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[tool.uv.sources]
torch = { index = "pytorch-cpu" }

[tool.pytest.ini_options]
testpaths = ["src/kb/eval"]
python_files = ["*_test.py", "test_*.py"]
asyncio_mode = "auto"

[tool.ruff]
target-version = "py312"
line-length = 100
src = ["src"]

[tool.ruff.lint]
select = ["E", "F", "W", "I", "N", "UP", "B", "C4", "RUF"]

[tool.mypy]
python_version = "3.12"
packages = ["kb"]
mypy_path = "src"
strict = true

# The eval package is tests + their harness; it is validated by running, not by strict typing.
[[tool.mypy.overrides]]
module = ["kb.eval.*"]
ignore_errors = true

[[tool.mypy.overrides]]
module = [
    "pygit2.*", "grimp.*", "sqlparse.*", "fastapi.*", "starlette.*", "fastmcp.*", "mcp.*",
    "sentence_transformers.*", "pgvector.*", "openai.*", "anthropic.*", "yaml.*",
]
ignore_missing_imports = true

# fastmcp's @mcp.tool decorator is untyped (the package is ignore_missing_imports above), so the
# strict disallow_untyped_decorators check can't see through it. The tool bodies stay fully typed.
[[tool.mypy.overrides]]
module = ["kb.mcp.server"]
disallow_untyped_decorators = false