Optional AI visual descriptions via a pluggable inference fleet (local + cloud) — offer to implement

Optional AI visual descriptions via a pluggable inference fleet (local + cloud) — and an offer to build it Following up on #26690 and #12900. In #26690 the two concerns raised were that a VLM is *"too heavy for the majority of users"* and that descriptions would *"duplicate CLIP's semantic search."* Both are fair — so this proposal is designed specifically around them, and I'm offering to implement it and submit staged PRs matching whatever architectural decisions you prefer. The idea (opt-in, off by default) A Visual Description capability: a background job that, per asset, calls a configured inference provider and stores a human-readable description (and optionally tags) on the asset. Crucially, "inference" is just an endpoint — not a bundled model. On "too heavy" → it ships no default compute • Off by default. Zero impact unless an admin enables it. No new mandatory model, no new default job, no RAM/VRAM footprint. ̶…