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Persistence for Ahnlich AI

Why need persistence?​

Ahnlich AI builds on DB by adding embeddings, vector similarity search, and model-driven queries. Without persistence:

  • AI stores and embeddings vanish after restarts.

  • Indices must be recomputed, delaying queries.

  • Models re-download weights on each startup.

Persistence ensures that:

  • Embeddings and indices survive container restarts.

  • Models remain cached locally for faster startup.

  • Production workloads avoid costly recomputation.

Configuring persistence from in-memory mode to disk​

Enable persistence for AI with the following configuration:

ahnlich_ai:
  image: ghcr.io/deven96/ahnlich-ai:latest
  command: >
    "ahnlich-ai run --db-host ahnlich_db --host 0.0.0.0 \
    --supported-models all-minilm-l6-v2,resnet-50 \
    --enable-persistence --persist-location /root/.ahnlich/data/ai.dat \
    --persistence-interval 300"
  ports:
    - "1370:1370"
  volumes:
    - "./ahnlich_ai_model_cache:/root/.ahnlich/models" # Model cache storage
    - "./data/:/root/.ahnlich/data" # Persistence Location

  • --enable-persistence → activates persistence for embeddings and AI stores.

  • --persist-location → file path for AI persistence (e.g., ai.dat).

  • --persistence-interval → interval (in seconds) for saving snapshots.

  • ./ahnlich_ai_model_cache → stores model weights persistently.

  • ./data/ → keeps AI persistence files safe.

Loading from persistence​

On restart, Ahnlich AI automatically restores state from ai.dat.

  • All embeddings, similarity indices, and AI stores are reloaded.

  • Cached models are loaded from ./ahnlich_ai_model_cache, avoiding redownloads.

  • Commands like LISTSTORES, GETSIMN, and GETPRED can confirm successful recovery.

  • Recovery from backups is as easy as restoring the persistence file and model cache.