curl https://api.hanzo.ai/v1/embeddings \
  -H "Authorization: Bearer $HANZO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zen-embedding",
    "input": "Zen LM is a family of frontier AI models"
  }'

from hanzoai import Hanzo

client = Hanzo(api_key="hk-your-api-key")

response = client.embeddings.create(
    model="zen-embedding",
    input=["Hello world", "Zen LM models"],
)

for embedding in response.data:
    print(f"Vector dim: {len(embedding.embedding)}")

from transformers import AutoModel, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-embedding")
model = AutoModel.from_pretrained("zenlm/zen-embedding")

inputs = tokenizer("Zen LM is a family of frontier AI models",
                   return_tensors="pt", truncation=True)
with torch.no_grad():
    outputs = model(**inputs)

# Mean pool over token embeddings
embeddings = outputs.last_hidden_state.mean(dim=1)
print(f"Embedding shape: {embeddings.shape}")  # [1, 3072]

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0152, "..."]
    }
  ],
  "model": "zen-embedding",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}