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mnemosyne/docker/entrypoint.sh
Robert Helewka 72bd4b381d
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#!/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="${CELERY_LOG_LEVEL:-info}"
;;
migrate)
# One-shot DB migration runner — invoke before bringing services up
# for the first time or after a deploy.
exec python manage.py migrate --noinput
;;
setup)
# One-shot init — Neo4j indexes + library_type seed data.
python manage.py setup_neo4j_indexes
python manage.py load_library_types
;;
shell)
# Drop into the management shell for ad-hoc work.
exec python manage.py shell
;;
*)
# Fall through: run whatever was passed (e.g. `manage.py <cmd>`).
exec "$@"
;;
esac