pyproject.toml

[project]
name = "recap"
version = "1.1.0"
description = "Local-first browsing-memory RAG - a Chrome extension plus a local FastAPI backend that indexes the pages you read and lets you search them in natural language."
readme = "README.md"
requires-python = ">=3.11,<3.14"
license = { text = "MIT" }

# Canonical dependency set for `uv` and Docker. requirements.txt mirrors this for
# the plain-pip path. Keep the two in sync when changing versions.
dependencies = [
    "fastapi==0.136.1",
    "uvicorn[standard]==0.42.0",
    "pydantic==2.12.5",
    "pydantic-settings==2.13.1",
    "python-dotenv>=1.0,<2",
    "sentence-transformers==5.0.0",
    "numpy>=1.24,<3",
    "torch==2.7.1",
    "lancedb==0.29.2",
    "pyarrow==21.0.0",
    "networkx==3.5",
    "openai==2.9.0",
    "spacy>=3.8,<3.9",
    # Entity-extraction model installed as a normal dependency, so `uv sync`
    # sets up the knowledge graph with no separate `spacy download` step.
    "en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl",
]

[tool.uv]
# This is an application, not a library - don't build/install the project itself,
# just resolve and install its dependencies into the managed virtual environment.
package = false

# Pull torch from PyPI's CPU-only index. RECAP is local-first and runs fine on CPU;
# the CPU wheel is a fraction of the CUDA wheel's size (much smaller Docker image
# and faster installs). GPU users can override torch's source for their platform.
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[tool.uv.sources]
torch = { index = "pytorch-cpu" }