Implements the HomeHunter request (REQUEST-geoclip-embeddings.md): two model ids
on the existing embeddings endpoint, both returning 512-d L2-normalised vectors in
ONE shared space so a property photo can be scored directly against candidate GPS
coordinates.

  geoclip           — image encoder (CLIP ViT-L/14 + projection MLP), on GPU.
                      Accepts the image data URI in EITHER `input` (per the request)
                      or the `image` field (like dinov2).
  geoclip-location  — GPS encoder (equal-earth + RFF MLP), pinned to CPU: it is a
                      tiny MLP, so CPU is fast, frees VRAM, and is bit-exact across
                      batch sizes (GPU reduction order otherwise perturbs a cached
                      coordinate by ~1e-7). Batches an array of "lat,lon" strings to
                      one vector per element, in input order.

Both sides load from the SAME bundled GeoCLIP checkpoint, so the spaces coincide by
construction — the mismatched-checkpoint failure the request warns about cannot
happen. Loaded as separate _EmbeddingModel('geoclip', ...) instances so each is
independently evictable.

transformers>=5 compat: CLIPModel.get_image_features now returns a
BaseModelOutputWithPooling (768-d projected features in pooler_output) instead of a
bare tensor, which breaks GeoCLIP's internal mlp() call. The image path extracts the
768-d tensor itself (tolerant of both the old tensor and the new object) before the
projection MLP.

geoclip (+geopy, geographiclib) added to requirements.txt; its heavy deps (torch,
transformers, Pillow, pandas, numpy) are already pinned, so no ML-stack churn.

Verified end-to-end: 512-d L2-normalised both sides; batching + index order; same
request bit-exact; both ids in GET /v1/models; image·location scoring varies by image.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LoSpEthysqmseCc6Geizty