pyproject.toml

[project]
name = "grounded"
version = "0.1.0"
description = "An open, evaluated, domain-specialized RAG platform — energy profile first."
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    # --- light runtime: CLI + acquisition + discovery (no ML stack) ---
    "typer>=0.12",
    "pyyaml>=6.0",
    "python-dotenv>=1.0",
    "httpx>=0.27",
    "ddgs>=6.0",
]

[project.optional-dependencies]
dev = ["pytest>=8.0"]
# Web API layer (Slice 5) — install with `uv sync --extra web`. Deliberately kept
# out of core AND out of the ml stack: the API is a thin HTTP shell that imports
# the engine lazily, so fastapi/uvicorn never enter the light or ml lanes.
web = [
    "fastapi>=0.115",
    "uvicorn[standard]>=0.30",
]
# The ML / RAG stack — install with `uv sync --extra ml` (or `uv pip install -e '.[ml]'`).
# Kept out of the light lane so the CLI + acquisition stay torch-free.
ml = [
    "pymupdf>=1.24",              # PDF parsing (Slice 1b)
    "sentence-transformers>=3.0", # local embeddings (Slice 1b)
    "torch>=2.2",                 # CPU build on this box; pulled by sentence-transformers
    "chromadb>=0.5",              # vector store (Slice 1b)
    "rank-bm25>=0.2",             # hybrid retrieval (Slice 2)
    "llama-index>=0.12",          # framework (Slices 2–3)
    "llama-index-vector-stores-chroma>=0.4",
    "llama-index-embeddings-huggingface>=0.4",
    "llama-index-llms-litellm>=0.3",
    "litellm>=1.44",              # LLM gateway (Slice 2)
    "ragas>=0.2",                 # evaluation (Slice 4)
    "langchain-litellm>=0.1",     # RAGAS judge via LiteLLM → Groq (Slice 4)
    "langchain-huggingface>=0.1", # RAGAS embeddings wrapper (Slice 4)
]

[project.scripts]
grounded = "grounded.cli:app"

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

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