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[build-system]

requires = ["setuptools>=68", "wheel"]

build-backend = "setuptools.build_meta"

[project]

name = "engram-subnet"

version = "0.1.2"

description = "Decentralized IPFS-style vector database on Bittensor — the permanent semantic memory layer for AI."

readme = "README.md"

license = { text = "MIT" }

requires-python = ">=3.10"

authors = [{ name = "Engram Contributors" }]

keywords = ["bittensor", "vector-database", "embeddings", "decentralized", "RAG", "semantic-search"]

classifiers = [

"Development Status :: 3 - Alpha",

"Intended Audience :: Developers",

"Intended Audience :: Science/Research",

"License :: OSI Approved :: MIT License",

"Programming Language :: Python :: 3",

"Programming Language :: Python :: 3.10",

"Programming Language :: Python :: 3.11",

"Programming Language :: Python :: 3.12",

"Topic :: Scientific/Engineering :: Artificial Intelligence",

"Topic :: Database",

"Topic :: Internet :: WWW/HTTP",

]

# SDK-only deps — lightweight, no PyTorch, no Bittensor

# `pip install engram-subnet` gives you EngramClient only

dependencies = [

"numpy>=1.26.0",

"pydantic>=2.7.0",

"python-dotenv>=1.0.0",

"loguru>=0.7.2",

"cryptography>=42.0.0",

]

[project.optional-dependencies]

# Full miner + validator runtime (pulls bittensor, faiss, sentence-transformers)

node = [

"bittensor>=7.4.0",

"faiss-cpu>=1.8.0",

"sentence-transformers>=3.0.0",

"aiohttp>=3.9.0",

"rich>=13.7.0",

"typer>=0.12.0",

]

# Slim miner runtime for Docker — no sentence-transformers/PyTorch (uses OpenAI embeddings)

miner = [

"bittensor>=7.4.0",

"faiss-cpu>=1.8.0",

"aiohttp>=3.9.0",

"rich>=13.7.0",

"typer>=0.12.0",

]

# Production vector store

qdrant = ["qdrant-client>=1.9.0"]

# OpenAI embeddings

openai = ["openai>=1.30.0"]

# Local sentence-transformers embedder for SDK private namespace encryption

local-embedder = ["sentence-transformers>=3.0.0"]

# Arweave permanent storage upload

arweave = ["arweave-python-client>=1.0.0"]

# PDF ingestion via ingest_document()

pdf = ["pypdf>=4.0.0"]

# All media/ingestion extras bundled

media = [

"engram-subnet[arweave,pdf]",

]

# CLI (engram ingest / query / status / init)

cli = [

"rich>=13.7.0",

"typer>=0.12.0",

"python-dotenv>=1.0.0",

]

# Full dev toolchain

dev = [

"pytest>=8.0.0",

"pytest-asyncio>=0.23.0",

"pytest-cov>=5.0.0",

"ruff>=0.4.0",

"mypy>=1.10.0",

"maturin>=1.5.0",

]

# Everything — miners, validators, SDK, CLI

all = [

"engram-subnet[node,qdrant,openai,local-embedder,media,cli,dev]",

]

[project.urls]

Homepage = "https://github.com/Dipraise1/-Engram-"

Repository = "https://github.com/Dipraise1/-Engram-"

"Bug Tracker" = "https://github.com/Dipraise1/-Engram-/issues"

[project.scripts]

engram = "engram.cli:app"

[tool.setuptools.packages.find]

where = ["."]

include = ["engram*"]

[tool.ruff]

line-length = 100

target-version = "py310"

[tool.ruff.lint.per-file-ignores]

# miner.py must call load_dotenv() before other imports so config.py reads the

# correct EMBEDDING_DIM and other env vars at module-import time.

"neurons/miner.py" = ["E402"]

"neurons/validator.py" = ["E402"]

[tool.mypy]

python_version = "3.10"

ignore_missing_imports = true

[tool.pytest.ini_options]

asyncio_mode = "auto"

testpaths = ["tests"]