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
no one is saying everyone has to ask an LLM for movie recommendations
OP wrote a python script that call a llm to ask for a recommendation.
But you are right, op doesn't say that everyone shall do it
No, it also doesn't do that. It gets embeddings from an LLM and uses that to rank candidates.
I had to look up embeddings: so this is comparing the encoding of movies as a similarity test?
Which can work because the encoding methods can indicate closeness of meaning.
And that's why this isn't running an llm in any way.
Are you a trollm?
If not, I'm just too stupid to understand op.
If that's not the same, I don't know what is. Gotta go back to school, I guess.
It's not, I read the code. It's not merely asking the LLM for recommendations, it's using embeddings to compute scores based on similarities.
It's a lot closer to a more traditional natural language processing than to how my dad would use GPT to discuss philosophy.
I love how you actually did something instead of jumping on the generalization train. I hate that about Lemmy right now.