docs(readme): clarify embedding model seed order for Neo4j indexes
Document that the system embedding model must be seeded before running `setup_neo4j_indexes`, since vector index dimensions are read from the `llm_manager_llmmodel` row. Update Docker instructions to reflect the `init` sidecar behavior, which now runs migrations and library_type defaults automatically while deferring vector index creation.
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mnemosyne/docker/entrypoint.sh
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111
mnemosyne/docker/entrypoint.sh
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#!/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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init)
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# Bundled one-shot init run by the `init` sidecar on every
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# `docker compose up`. Idempotent: re-runs are no-ops unless
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# migrations or library-type seed data need to change.
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#
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# Vector-index creation intentionally runs in *best-effort* mode:
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# ``setup_neo4j_indexes`` requires a system embedding model with a
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# configured ``vector_dimensions`` value, and that model is data an
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# operator seeds via the admin UI after the stack comes up for the
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# first time. Blocking the whole stack on first boot would force
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# every new deployer through a manual dance with the init sidecar's
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# entrypoint; instead we log loudly and carry on, and the operator
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# runs the command once post-boot:
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#
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# docker compose exec app python manage.py setup_neo4j_indexes
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#
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# Full-text and neomodel constraint indexes are created by the same
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# command and are *not* dimension-sensitive, but they also only land
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# after the operator re-runs it — acceptable because search against
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# an empty graph is itself a no-op.
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set -e
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python manage.py migrate --noinput
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python manage.py load_library_types
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if ! python manage.py setup_neo4j_indexes; then
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echo ""
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echo "============================================================"
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echo "NOTICE: Neo4j index creation was skipped."
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echo ""
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echo "This is expected on a fresh deployment — vector indexes"
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echo "require a system embedding model with vector_dimensions set."
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echo ""
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echo "Seed the embedding model in the Django admin"
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echo " (/admin/llm_manager/llmmodel/, mark one row as"
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echo " is_system_embedding_model=True with vector_dimensions set),"
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echo "then run:"
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echo ""
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echo " docker compose exec app python manage.py setup_neo4j_indexes"
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echo ""
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echo "Search endpoints will return empty results until this is done."
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echo "============================================================"
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echo ""
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fi
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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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