pyproject.toml

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

[project]
name = "py-rag-engine"
version = "0.1.0"
description = "RAG engine with document ingestion (PDF, Markdown)"
readme = "README.md"
license = { file = "LICENSE" }
authors = [
    { name = "Your Name", email = "you@example.com" },
]
requires-python = ">=3.10"
dependencies = [
    "langchain-text-splitters>=0.2.0",
    "pypdf>=4.0.0",
    "numpy>=1.24.0",
    "pgvector>=0.4.0",
    "psycopg[binary]>=3.2.0",
    "SQLAlchemy>=2.0.0",
    "pydantic>=2.6.0",
]

[project.optional-dependencies]
api = [
    "fastapi>=0.110.0",
    "uvicorn[standard]>=0.27.0",
    "python-multipart>=0.0.9",
]
embeddings = [
    "openai>=1.0.0",
    "sentence-transformers>=2.2.0",
]
rerank = [
    "flashrank>=0.2.10",
]
eval = [
    "sentence-transformers>=2.2.0",
    # Optional: the script ships a manual RAGAS-equivalent implementation that
    # works against LM Studio out of the box. Set EVAL_USE_OFFICIAL_RAGAS=1 to
    # try the official library first, with automatic fallback to manual.
    "ragas>=0.2",
    "langchain-openai>=0.1",
    "datasets>=2.0",
    "pandas>=2.0",
]

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

[dependency-groups]
dev = ["pytest>=8.0.0"]

[tool.pytest.ini_options]
testpaths = ["tests"]
pythonpath = ["src"]

[tool.ruff]
line-length = 100
target-version = "py310"