[project]
name = "yuanqi"
version = "0.1.0"
description = "元炁 · 魂镜本地推理引擎 — LLM / Embedding / Reranker / Multimodal（纯 Python，直接推理）"
authors = [{ name = "chenxin", email = "2685475410@qq.com" }]
license = "MIT"
requires-python = ">=3.12"
dependencies = [
    "fastapi>=0.139.0",
    "loguru>=0.7.3",
    "numba>=0.66.0",
    "pydantic>=2.13.4",
    "pydantic-settings>=2.14.2",
    "python-multipart>=0.0.12",
    "sentencepiece>=0.2.2",
    "torch>=2.11.0",
    "torchaudio>=2.11.0",
    "transformers>=5.13.1",
    "uvicorn[standard]>=0.51.0",
]

[project.optional-dependencies]
llm = ["llama-cpp-python>=0.3.34"]
embedding = ["FlagEmbedding>=1.4.0"]
asr = ["funasr>=1.2.0"]
modelscope = ["modelscope>=1.16.0"]
all = ["yuanqi[llm,embedding,asr,modelscope]"]
dev = [
    "pre-commit>=4.6.0",
    "pytest>=9.1.1",
    "pytest-asyncio>=1.4.0",
    "ruff>=0.15.21",
]
lint = ["black>=26.5.1", "mypy>=2.3.0", "ruff>=0.15.21"]
test = ["pytest>=9.1.1", "pytest-asyncio>=1.4.0", "pytest-cov>=7.1.0"]
dev-all = ["yuanqi[dev,lint,test]"]

# =============================================
# uv 配置 - 跨平台 PyTorch 自动适配
# =============================================

# 默认使用中科大镜像（所有平台通用）当前服务器对中科大源访问快,其他源可以按照下面方法测试来换源
# curl -o /dev/null -s -w '清华源: %{time_total}s\n' https://pypi.tuna.tsinghua.edu.cn/simple/
# curl -o /dev/null -s -w '阿里云: %{time_total}s\n' https://mirrors.aliyun.com/pypi/simple/
# curl -o /dev/null -s -w '南大源: %{time_total}s\n' https://mirror.nju.edu.cn/pypi/web/simple/
# curl -o /dev/null -s -w 'PyPI官方: %{time_total}s\n' https://pypi.org/simple/
# curl -o /dev/null -s -w '中科大源: %{time_total}s\n' https://pypi.mirrors.ustc.edu.cn/simple/
[[tool.uv.index]]
name = "ustc"
url = "https://pypi.mirrors.ustc.edu.cn/simple/"
default = true

# PyTorch CUDA 12.8 专用源（仅 Linux/Windows 使用）
# 目前发现仅南京大学和阿里云(https://mirrors.aliyun.com/pytorch-wheels/cu128/)可用;中科大,清华源等都找不到
[[tool.uv.index]]
name = "pytorch-cu128"
url = "https://mirrors.nju.edu.cn/pytorch/whl/cu128/"
explicit = true

# === 包来源映射：根据操作系统自动选择 ===
[tool.uv.sources]
# Linux/Windows：使用 CUDA 12.8 版本
torch = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
torchvision = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
torchaudio = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
yuanqi = { workspace = true }

# macOS：从默认源（中科大）自动获取 CPU/MPS 版本
# 不需要额外配置，会自动回退到 default = true 的源
# macOS 验证（应该使用 MPS）
# python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'MPS available: {torch.backends.mps.is_available()}'); print(f'CUDA available: {torch.cuda.is_available()}')"
# Linux/Windows 验证（应该使用 CUDA）
# python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda if torch.cuda.is_available() else \"N/A\"}')"

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

# 告诉 hatchling 包在 src/ 目录下
[tool.hatch.build.targets.wheel]
packages = ["src"]

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

[tool.ruff.lint]
select = ["E", "F", "I", "N", "W"]

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]