#!/bin/sh
# Mnemosyne container entrypoint.
#
# The same image runs all three processes — the compose service supplies
# `web`, `mcp`, `worker`, or `migrate` as CMD.

set -e

case "$1" in
  web)
    # Django REST API + admin (gunicorn → wsgi).
    exec gunicorn \
      --config /app/docker/gunicorn.conf.py \
      --bind 0.0.0.0:8000 \
      --workers "${GUNICORN_WORKERS:-3}" \
      --access-logfile - \
      --error-logfile - \
      mnemosyne.wsgi:application
    ;;

  mcp)
    # FastMCP over Streamable HTTP at /mcp/, mounted by mnemosyne.asgi.
    exec uvicorn \
      --host 0.0.0.0 \
      --port 8001 \
      --workers "${UVICORN_WORKERS:-1}" \
      mnemosyne.asgi:app
    ;;

  worker)
    # Celery worker covering embedding + ingest + batch + default queues.
    # In production you may want to split these onto separate worker
    # services for queue-level isolation; one process is fine to start.
    exec celery -A mnemosyne worker \
      --loglevel="${CELERY_LOG_LEVEL:-info}" \
      --queues="${CELERY_QUEUES:-celery,embedding,batch}" \
      --concurrency="${CELERY_CONCURRENCY:-2}"
    ;;

  beat)
    # Celery scheduled tasks (only needed if/when periodic jobs are wired).
    exec celery -A mnemosyne beat \
      --loglevel="${CELERY_LOG_LEVEL:-info}"
    ;;

  migrate)
    # One-shot DB migration runner — invoke before bringing services up
    # for the first time or after a deploy.
    exec python manage.py migrate --noinput
    ;;

  setup)
    # One-shot init — Neo4j indexes + library_type seed data. Run this
    # manually after the system embedding model has been configured in the
    # admin (setup_neo4j_indexes reads vector dimensions from that row).
    python manage.py setup_neo4j_indexes
    python manage.py load_library_types
    ;;

  init)
    # Bundled one-shot init run by the `init` sidecar on every
    # `docker compose up`. Idempotent: re-runs are no-ops unless migrations
    # or library_type defaults need to change. A non-zero exit here blocks
    # `app`, `mcp`, and `worker` from starting.
    #
    # collectstatic copies the static files baked into the image at build
    # time (/app/staticfiles) into STATIC_ROOT (/mnt/static), which nginx
    # serves directly. --clear removes any stale files from the previous
    # deploy before copying, so deleted assets don't linger.
    #
    # Neo4j vector-index creation is *deliberately not* bundled here. That
    # command (``setup_neo4j_indexes``) requires a system embedding model
    # with a configured ``vector_dimensions`` value, and that model is
    # data an operator configures through the Django admin after first
    # boot. On a fresh stack there is no such row yet, so blocking the
    # whole stack on it would make the admin unreachable — a chicken-and-
    # egg. Operator bootstrap flow:
    #
    #   1. docker compose up           # init sidecar: migrate + collectstatic + load_library_types
    #   2. browse to admin, configure system embedding model
    #   3. docker compose exec app python manage.py setup_neo4j_indexes
    #
    # Until step 3 runs, vector search will return empty results — the
    # readiness check in library/apps.py logs a warning when indexes are
    # missing so this is visible, not silent.
    set -e
    python manage.py migrate --noinput
    python manage.py collectstatic --noinput --clear
    python manage.py load_library_types
    ;;


  shell)
    # Drop into the management shell for ad-hoc work.
    exec python manage.py shell
    ;;

  *)
    # Fall through: run whatever was passed (e.g. `manage.py <cmd>`).
    exec "$@"
    ;;
esac
