mempalace/pyproject.toml

[project]
name = "mempalace"
version = "3.3.4"
description = "Give your AI a memory — mine projects and conversations into a searchable palace. No API key required."
readme = "README.md"
requires-python = ">=3.9"
license = "MIT"
authors = [
    {name = "milla-jovovich"},
]
keywords = [
    "ai", "memory", "llm", "rag", "chromadb", "mcp",
    "vector-database", "claude", "chatgpt", "embeddings",
]
classifiers = [
    "Development Status :: 4 - Beta",
    "Environment :: Console",
    "Intended Audience :: Developers",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.9",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Programming Language :: Python :: 3.14",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Utilities",
]
dependencies = [
    "chromadb>=1.5.4,<2",
    "pyyaml>=6.0,<7",
    "tomli>=2.0.0; python_version < '3.11'",
]

[project.urls]
Homepage = "https://github.com/MemPalace/mempalace"
Repository = "https://github.com/MemPalace/mempalace"
"Bug Tracker" = "https://github.com/MemPalace/mempalace/issues"

[project.scripts]
mempalace = "mempalace.cli:main"
mempalace-mcp = "mempalace.mcp_server:main"

[project.entry-points."mempalace.backends"]
chroma = "mempalace.backends.chroma:ChromaBackend"

# RFC 002 source-adapter entry-point group. Core publishes no first-party
# adapters under this group yet; ``miner.py`` and ``convo_miner.py`` migrate
# onto ``BaseSourceAdapter`` in a follow-up PR. Third-party adapter packages
# (``mempalace-source-cursor``, ``mempalace-source-git``, …) register here.
[project.entry-points."mempalace.sources"]

[project.optional-dependencies]
dev = ["pytest>=7.0", "pytest-cov>=4.0", "ruff>=0.4.0", "psutil>=5.9"]
spellcheck = ["autocorrect>=2.0"]
# Hardware acceleration for the ONNX embedding model. Install exactly one:
#   pip install mempalace[gpu]       — NVIDIA CUDA
#   pip install mempalace[dml]       — DirectML (Windows AMD/Intel/NVIDIA)
#   pip install mempalace[coreml]    — macOS Neural Engine
# After install, set MEMPALACE_EMBEDDING_DEVICE=cuda|dml|coreml (or "auto").
gpu = ["onnxruntime-gpu>=1.16"]
dml = ["onnxruntime-directml>=1.16"]
coreml = ["onnxruntime>=1.16"]

[dependency-groups]
dev = ["pytest>=7.0", "pytest-cov>=4.0", "ruff>=0.4.0", "psutil>=5.9"]

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

[tool.hatch.build.targets.wheel]
packages = ["mempalace"]

[tool.ruff]
line-length = 100
target-version = "py39"
extend-exclude = ["benchmarks"]

[tool.ruff.lint]
select = ["E", "F", "W", "C901"]
ignore = ["E501"]

[tool.ruff.lint.mccabe]
max-complexity = 25

[tool.ruff.format]
quote-style = "double"

[tool.pytest.ini_options]
testpaths = ["tests"]
pythonpath = ["."]
addopts = "-m 'not benchmark and not slow and not stress'"
markers = [
    "benchmark: scale/performance benchmark tests",
    "slow: tests that take more than 30 seconds",
    "stress: destructive scale tests (100K+ drawers)",
]

[tool.coverage.run]
source = ["mempalace"]

[tool.coverage.report]
fail_under = 85
show_missing = true
exclude_lines = [
    "if __name__",
    "pragma: no cover",
]