#+begin_src python :tangle ~/code/chroma_example/embeddings.py import torch from PIL import Image import torchvision.transforms as transforms import clip

device = “cuda” if torch.cuda.is_available() else “cpu” model, preprocess = clip.load(’ViT-B/32’, device=device)

def get_embeddings(path, model, preprocess): image = Image.open(path) pp = preprocess(image).unsqueeze(0).to(device) with torch.no_grad(): features = model.encode_image(pp) return features

def get_{cosinedifference}(a, b): return torch.nn.functional.cosine_similarity(a, b)

path_a = ’./images/a.jpg’ path_b = ’./images/b.jpg’ path_c = ’./images/c.jpg’

e_a = g