pyproject.toml

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

[project]
name = "tenk-intelligence"
version = "0.1.0"
description = "Local-first financial-filings intelligence over SEC EDGAR: hybrid vector + GraphRAG with cited, multi-hop answers."
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [{ name = "Raed Lazreg" }]

# Core is intentionally light so CI (and the smoke test) install fast. Every heavy
# runtime dependency is lazily imported inside the module that needs it and lives in an
# extra below; `make install` pulls them all via the `all` extra.
dependencies = [
    "pydantic>=2.9",
    "pydantic-settings>=2.6",
    "python-dotenv>=1.0",
    "tenacity>=9.0",            # retry flaky network calls
    "typer>=0.13",             # CLI
    "rich>=13.9",
]

[project.optional-dependencies]
ingest = ["edgartools>=3.0", "docling>=2.0", "pandas>=2.2"]
index = ["fastembed>=0.4", "qdrant-client>=1.12"]
graph = ["neo4j>=5.24"]
llm = ["ollama>=0.4", "openai>=1.50"]
serve = ["fastapi>=0.115", "uvicorn[standard]>=0.32", "streamlit>=1.40"]
eval = ["ragas>=0.2", "datasets>=3.0"]
dev = ["pytest>=8.3", "ruff>=0.7"]
all = [
    "edgartools>=3.0", "docling>=2.0", "pandas>=2.2",
    "fastembed>=0.4", "qdrant-client>=1.12", "neo4j>=5.24",
    "ollama>=0.4", "openai>=1.50",
    "fastapi>=0.115", "uvicorn[standard]>=0.32", "streamlit>=1.40",
    "ragas>=0.2", "datasets>=3.0",
]

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

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

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

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B"]
ignore = ["E501"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q"