Upload Vector Data

from moorcheh_sdk import MoorchehClient

with MoorchehClient() as client:
    vectors_to_upload = [
        {
            "id": "image_001.jpg",
            "vector": [0.12, -0.45, 0.23, 0.67, ...],  # Must match namespace dimension
            "source": "product_database",
            "category": "electronics"
        },
        {
            "id": "image_002.jpg",
            "vector": [-0.22, 0.81, -0.34, 0.12, ...],
            "source": "product_database",
            "category": "electronics"
        }
    ]

    status = client.vectors.upload(
        namespace_name="my-image-embeddings",
        vectors=vectors_to_upload
    )
    print(status)

vectors = [
    {
        "id": "embedding-1",
        "vector": [0.1, 0.2, 0.3, ...],  # Must match namespace dimension
        # Optional metadata
        "source": "image_database",
        "category": "product",
        "confidence": 0.95
    }
]

from moorcheh_sdk import MoorchehClient
import numpy as np

with MoorchehClient() as client:
    # Create a vector namespace with dimension 768
    client.namespaces.create(
        namespace_name="product-embeddings",
        type="vector",
        vector_dimension=768
    )
    
    # Generate or load your vectors (example with random vectors)
    vectors = []
    for i in range(10):
        # Your actual embedding generation code here
        vector = np.random.rand(768).tolist()  # Example: 768-dimensional vector
        vectors.append({
            "id": f"product_{i}",
            "vector": vector,
            "category": "electronics",
            "price": 99.99 + i * 10
        })
    
    # Upload vectors
    result = client.vectors.upload(
        namespace_name="product-embeddings",
        vectors=vectors
    )
    print(f"Uploaded {len(vectors)} vectors")