68 lines
1.8 KiB
Bash
68 lines
1.8 KiB
Bash
#!/bin/sh
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# Mnemosyne container entrypoint.
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#
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# The same image runs all three processes — the compose service supplies
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# `web`, `mcp`, `worker`, or `migrate` as CMD.
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set -e
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case "$1" in
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web)
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# Django REST API + admin (gunicorn → wsgi).
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exec gunicorn \
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--config /app/docker/gunicorn.conf.py \
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--bind 0.0.0.0:8000 \
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--workers "${GUNICORN_WORKERS:-3}" \
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--access-logfile - \
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--error-logfile - \
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mnemosyne.wsgi:application
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;;
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mcp)
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# FastMCP over Streamable HTTP at /mcp/, mounted by mnemosyne.asgi.
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exec uvicorn \
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--host 0.0.0.0 \
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--port 8001 \
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--workers "${UVICORN_WORKERS:-1}" \
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mnemosyne.asgi:app
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;;
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worker)
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# Celery worker covering embedding + ingest + batch + default queues.
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# In production you may want to split these onto separate worker
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# services for queue-level isolation; one process is fine to start.
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exec celery -A mnemosyne worker \
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--loglevel="${CELERY_LOG_LEVEL:-info}" \
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--queues="${CELERY_QUEUES:-celery,embedding,batch}" \
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--concurrency="${CELERY_CONCURRENCY:-2}"
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;;
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beat)
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# Celery scheduled tasks (only needed if/when periodic jobs are wired).
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exec celery -A mnemosyne beat \
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--loglevel="${CELERY_LOG_LEVEL:-info}"
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;;
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migrate)
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# One-shot DB migration runner — invoke before bringing services up
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# for the first time or after a deploy.
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exec python manage.py migrate --noinput
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;;
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setup)
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# One-shot init — Neo4j indexes + library_type seed data.
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python manage.py setup_neo4j_indexes
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python manage.py load_library_types
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;;
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shell)
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# Drop into the management shell for ad-hoc work.
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exec python manage.py shell
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;;
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*)
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# Fall through: run whatever was passed (e.g. `manage.py <cmd>`).
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exec "$@"
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;;
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esac
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