93 lines
3.6 KiB
TOML
93 lines
3.6 KiB
TOML
[project]
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name = "yuanqi"
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version = "0.1.0"
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description = "元炁 · 魂镜本地推理引擎 — LLM / Embedding / Reranker / Multimodal（纯 Python，直接推理）"
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authors = [{ name = "chenxin", email = "2685475410@qq.com" }]
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license = "MIT"
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requires-python = ">=3.12"
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dependencies = [
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"fastapi>=0.139.0",
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"loguru>=0.7.3",
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"numba>=0.66.0",
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"pydantic>=2.13.4",
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"pydantic-settings>=2.14.2",
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"python-multipart>=0.0.12",
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"sentencepiece>=0.2.2",
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"torch>=2.11.0",
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"torchaudio>=2.11.0",
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"transformers>=5.13.1",
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"uvicorn[standard]>=0.51.0",
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]
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[project.optional-dependencies]
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llm = ["llama-cpp-python>=0.3.34"]
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embedding = ["FlagEmbedding>=1.4.0"]
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asr = ["funasr>=1.2.0"]
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modelscope = ["modelscope>=1.16.0"]
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all = ["yuanqi[llm,embedding,asr,modelscope]"]
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dev = [
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"pre-commit>=4.6.0",
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"pytest>=9.1.1",
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"pytest-asyncio>=1.4.0",
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"ruff>=0.15.21",
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]
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lint = ["black>=26.5.1", "mypy>=2.3.0", "ruff>=0.15.21"]
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test = ["pytest>=9.1.1", "pytest-asyncio>=1.4.0", "pytest-cov>=7.1.0"]
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dev-all = ["yuanqi[dev,lint,test]"]
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# =============================================
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# uv 配置 - 跨平台 PyTorch 自动适配
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# =============================================
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# 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
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# curl -o /dev/null -s -w '清华源: %{time_total}s\n' https://pypi.tuna.tsinghua.edu.cn/simple/
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# curl -o /dev/null -s -w '阿里云: %{time_total}s\n' https://mirrors.aliyun.com/pypi/simple/
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# curl -o /dev/null -s -w '南大源: %{time_total}s\n' https://mirror.nju.edu.cn/pypi/web/simple/
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# curl -o /dev/null -s -w 'PyPI官方: %{time_total}s\n' https://pypi.org/simple/
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# curl -o /dev/null -s -w '中科大源: %{time_total}s\n' https://pypi.mirrors.ustc.edu.cn/simple/
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[[tool.uv.index]]
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name = "ustc"
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url = "https://pypi.mirrors.ustc.edu.cn/simple/"
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default = true
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# PyTorch CUDA 12.8 专用源（仅 Linux/Windows 使用）
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# 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
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[[tool.uv.index]]
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name = "pytorch-cu128"
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url = "https://mirrors.nju.edu.cn/pytorch/whl/cu128/"
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explicit = true
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# === 包来源映射：根据操作系统自动选择 ===
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[tool.uv.sources]
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# Linux/Windows：使用 CUDA 12.8 版本
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torch = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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torchvision = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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torchaudio = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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yuanqi = { workspace = true }
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# macOS：从默认源（中科大）自动获取 CPU/MPS 版本
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# 不需要额外配置，会自动回退到 default = true 的源
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# macOS 验证（应该使用 MPS）
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# 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()}')"
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# Linux/Windows 验证（应该使用 CUDA）
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# 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\"}')"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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# 告诉 hatchling 包在 src/ 目录下
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[tool.hatch.build.targets.wheel]
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packages = ["src"]
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[tool.ruff]
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line-length = 100
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target-version = "py312"
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[tool.ruff.lint]
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select = ["E", "F", "I", "N", "W"]
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[tool.pytest.ini_options]
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asyncio_mode = "auto"
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testpaths = ["tests"]
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