pyproject.toml

[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "rag-mcp-qdrant"
version = "0.1.0"
description = "A minimal RAG service with MCP tools for document ingestion and semantic search using Qdrant"
readme = "README.md"
requires-python = ">=3.10"
license = {text = "MIT"}
authors = [
    {name = "Your Name", email = "[email protected]"}
]
keywords = ["mcp", "rag", "vector-search", "embeddings", "qdrant"]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
]

dependencies = [
    "fastmcp>=2.0.0",
    "huggingface-hub>=0.20.0",
    "langchain-huggingface>=1.0.0",
    "langchain-text-splitters>=0.3.0",
    "mcp>=1.0.0",
    "pydantic>=2.0.0",
    "python-dotenv>=1.0.0",
    "qdrant-client>=1.7.0",
    "sentence-transformers>=3.0.0",
    "torch>=2.0.0; platform_machine != 'x86_64'",
    "torch==2.5.1+cpu; platform_machine == 'x86_64'",
    "transformers>=4.30.0",
]

[tool.uv.sources]
torch = [
    { index = "pytorch-cpu", marker = "platform_machine == 'x86_64'" },
]

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[project.optional-dependencies]
dev = [
    "pytest",
    "pytest-asyncio",
    "black",
    "ruff",
]

# Vector store backends
chroma = [
    "chromadb>=0.4.0",
]

milvus = [
    "pymilvus>=2.3.0",
]

weaviate = [
    "weaviate-client>=3.25.0",
]

[project.scripts]
rag-mcp-qdrant = "mcp_server.main:main"

[project.urls]
Homepage = "https://github.com/yourusername/rag-mcp"
Repository = "https://github.com/yourusername/rag-mcp"
Issues = "https://github.com/yourusername/rag-mcp/issues"

[tool.setuptools]
packages = ["mcp_server", "rag_core"]

[tool.setuptools.package-data]
"*" = ["*.txt", "*.md"]