[0.7.0] - 2026-07-03

Added

• CLIPEncoder (in pyvisim.classic): a pretrained-CLIP image encoder built on
  open_clip. It maps an image straight to a CLIP embedding, so there's no feature
  extractor, clustering model, or learn step. Embeddings are L2-normalized by default,
  and it plugs into the usual similarity_score / save_to_disk / load_from_disk
  machinery.

  from pyvisim.classic import CLIPEncoder
  
  clip = CLIPEncoder(model_name="ViT-B-32", pretrained="laion2b_s34b_b79k")
  embeddings = clip.encode(images)

  Saving stores only the model identifiers (model_name, pretrained tag, etc.), not
  the weights, so .encoder files stay tiny and open_clip re-fetches the weights on load.

• nn optional extra (pip install "pyvisim[nn]") now pulls in the whole deep-learning
  stack: torch, torchvision, torchaudio and open_clip_torch. It covers
  DeepConvFeature (VGG16 deep features), CLIPEncoder, and the datasets and
  neural_networks modules. Everything is imported lazily, so importing pyvisim never
  requires it; you only hit the error (with an install hint) the first time you actually
  build one of these without it installed.

• search optional extra (pip install "pyvisim[search]") that pulls in faiss-cpu for
  the retrieval / image-store stack: InMemoryImageEmbeddingStore, ImageRetriever and
  the ImageIndex* classes. faiss is imported lazily too, so you only need it when you
  build a store or an index.

Breaking

• ⚠️ pip install pyvisim no longer installs torch or faiss. The base install now covers
  the SIFT/RootSIFT encoders only. Install [nn] for deep features and CLIP, [search]
  for the image store and retrieval, or pip install "pyvisim[nn,search]" for everything.
  Heads up: the VGG16 pretrained encoders (OXFORD102_K256_VGG16*) build a
  DeepConvFeature, so they now need the nn extra.