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Hello Voyage AI Community,
As a developer and a fan of the rhythm game Friday Night Funkin' (FNF), I’ve been exploring ways to improve how fans discover new content. With the FNF modding community producing thousands of unique mods (on platforms like GameBanana), finding a specific mod based on "vibe," "music style," or "difficulty level" is becoming increasingly difficult with standard keyword search.
I am considering a project that uses Voyage AI’s embeddings (like voyage-large-2) to build a semantic search engine for FNF mods. Here is the concept:
The Challenge: FNF mods have rich metadata—descriptions of the music (jazz, metal, lo-fi), the character's backstory, and mechanical difficulty. A simple search for "fast songs" might miss mods described as "high BPM" or "intense speed."
The Solution with Voyage AI: By embedding the descriptions and tags of thousands of FNF mods, we can create a vector space where users can search using natural language (e.g., "A mod with a creepy atmosphere and challenging electronic music").
Potential Implementation: Using Voyage AI's high-quality rerankers to ensure that the most relevant mods appear at the top, even if the user's query doesn't match the exact title.
Why FNF?
FNF is open-source and has one of the most active creative communities. It’s a perfect "stress test" for embedding models because the language used in the community is very informal and niche-specific.
I would love to hear your thoughts on:
Which Voyage model would be best for handling gaming-related slang and technical music terms?
Has anyone tried building a recommendation engine for gaming assets using Voyage AI yet?
Looking forward to your insights!