example_usage.py

import torch
from transformers import AutoTokenizer, AutoModel

def lorentz_dist(u: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
    """
    Computes the exact Hyperbolic distance between two batches of Lorentz vectors.
    """
    # Lorentz Metric signature (- + + ...)
    u_0, u_x = u[..., 0:1], u[..., 1:]
    v_0, v_x = v[..., 0:1], v[..., 1:]
    
    # Minkowski inner product
    inner_product = -u_0 * v_0 + (u_x * v_x).sum(dim=-1, keepdim=True)
    
    # Avoid numerical instability inside acosh for extremely close vectors
    inner_product = torch.min(inner_product, torch.tensor(-1.0, device=u.device))
    return torch.acosh(-inner_product).squeeze(-1)

def main():
    model_id = "YARlabs/v5_Embedding" # Ensure you have internet connection to fetch the model, or use a local path like "." if running locally
    
    print(f"Loading {model_id}...")
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
    model.eval()

    texts = [
        "What is the capital of France?",
        "Paris is the capital of France.",
        "Berlin is the capital of Germany."
    ]

    print("Tokenizing texts...")
    inputs = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")

    print("Generating Matryoshka Lorentz Embeddings with dimension 64...")
    with torch.no_grad():
        lorentz_vectors = model(**inputs, target_dim=64)
        
    print(f"Vectors shape: {lorentz_vectors.shape}")
    
    # Calculate distances
    dist_correct = lorentz_dist(lorentz_vectors[0], lorentz_vectors[1])
    dist_wrong = lorentz_dist(lorentz_vectors[0], lorentz_vectors[2])
    
    print(f"\nDistance (Question <-> Correct Answer): {dist_correct.item():.4f}")
    print(f"Distance (Question <-> Wrong Answer): {dist_wrong.item():.4f}")
    
    if dist_correct.item() < dist_wrong.item():
        print("\n✅ Semantic search successfully retrieved the closest context!")

if __name__ == "__main__":
    # If testing locally, you can change model_id to "."
    main()