# KAL + GMS end-to-end demo — runtime dependencies.
#
# PRE-GA: these wheels are NOT on public PyPI yet. Install them from the
# knowlytix/gmsh repo (knowlytix-core / -knowledge / -harness) and the KAL
# repo (knowlytix-kal), and activate a knowlytix license. See README.md.
# `knowlytix-core` is commercial (EULA) and license-gated at import.

knowlytix-kal            # OSS — source aggregation -> KALTriples
knowlytix-knowledge      # OSS — GMSExpertStore, verify, query
knowlytix-harness        # OSS — geometric judge + DOE
knowlytix-core           # commercial (license) — geometry + training

python-dotenv            # load a single .env for GMS_LLM_* + provider keys
plotly                   # inline 3D geodesic route in the notebook
networkx                 # 2D force-directed layout for viz_graph's path diagrams
jupyterlab               # to run kal_gms_e2e_demo.ipynb (the primary surface)
fastapi                  # optional: the live three.js query server (server.py)
uvicorn                  # optional: ASGI server for server.py
# Phase H (phase_h_neo4j.py) only: the Neo4j driver + a running Neo4j
# (`docker compose up -d` starts one — see docker-compose.yml). Install the
# driver via `pip install knowlytix-kal[neo4j]`; the adapter lazy-imports it.
# neo4j>=5,<6
# Phase B MCP fetch (phase_b_mcp.py) only: the MCP SDK + an external MCP server.
# Install via `pip install knowlytix-kal[mcp]` and have node/npx on PATH.
# mcp>=1.10,<2.0
# torch is pulled transitively by knowlytix-core
