The semantic index rejects embedding vectors larger than 1024 dimensions (MAX_DIMENSION). Several current models are bigger than that: OpenAI's text-embedding-3-large is 3072, and some local models are 4096. The openai_compatible and ollama backends from #11 let you point AFT at these models, but the index build fails as soon as the vectors come back larger than 1024.
Use case
If you run a larger embedding model through LM Studio, Ollama, or an OpenAI-compatible endpoint, you can't build a semantic index at all right now. Raising the cap to 4096 covers the common models and still keeps a fixed upper bound.
The semantic index rejects embedding vectors larger than 1024 dimensions (
MAX_DIMENSION). Several current models are bigger than that: OpenAI'stext-embedding-3-largeis 3072, and some local models are 4096. Theopenai_compatibleandollamabackends from #11 let you point AFT at these models, but the index build fails as soon as the vectors come back larger than 1024.Use case
If you run a larger embedding model through LM Studio, Ollama, or an OpenAI-compatible endpoint, you can't build a semantic index at all right now. Raising the cap to 4096 covers the common models and still keeps a fixed upper bound.