I Built a Python script that uses a local Ollama LLM to automatically find and add movies to Radarr.

It picks random films from your library, asks Ollama for similar suggestions based on theme and atmosphere, validates against OMDb, scores with plot embeddings, then adds the top results to Radarr automatically.

Examples:

  • Whiplash → La La Land, Birdman, All That Jazz
  • The Thing → In the Mouth of Madness, It Follows, The Descent
  • In Bruges → Seven Psychopaths, Dead Man’s Shoes

Features:

  • 100% local, no external AI API
  • –auto mode for daily cron/Task Scheduler
  • –genre “Horror” for themed movie nights
  • Persistent blacklist, configurable quality profile
  • Works on Windows, Linux, Mac

GitHub: https://github.com/nikodindon/radarr-movie-recommender

  • LiveLM@lemmy.zipEnglish
    2·
    4 hours ago

    One issue, if you want to call it that, is that our approach was deterministic. Enter the same movies, get the same results. I don’t think an LLM is as predictable for that

    Maybe lowering the temperature will help with this?
    Besides, a tinge of randomness could even be considered a fun feature.