> ## Documentation Index
> Fetch the complete documentation index at: https://docs.moorcheh.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Upload Vector Data

> Upload pre-computed vector embeddings to a namespace using the Python SDK

## vectors.upload

Uploads pre-computed vectors to a vector namespace. This is a synchronous operation.

### Parameters

<ParamField query="namespace_name" type="str" required>
  The name of the target vector namespace.
</ParamField>

<ParamField query="vectors" type="List[Dict]" required>
  A list of dictionaries. Each dict requires an id and a vector key.
</ParamField>

**Returns:** `Dict[str, Any]` - A dictionary confirming the upload status.

**Raises:** `NamespaceNotFound`, `InvalidInputError`.

### Example

```python Upload Vectors Example theme={null}
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)
```

### Vector Data Structure

For vector uploads, ensure your vectors match the namespace dimension:

```python Vector Structure theme={null}
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
    }
]
```

### Complete Example

```python Complete Vector Upload Example theme={null}
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")
```

## Important Notes

<Info>
  **Synchronous Processing**: Vector uploads are processed immediately and are available for search right away.
</Info>

<Warning>
  **Dimension Match**: Vectors must match the exact dimension specified when the namespace was created. All vectors in a batch must have the same dimension.
</Warning>

## Vector Requirements

* **Dimension Match**: Must match namespace dimension exactly
* **Common Dimensions**: 384, 768, 1536, 3072
* **Value Range**: Normalized vectors preferred (typically -1.0 to 1.0)
* **Batch Size**: Max 1000 vectors per request, Recommended 100-500
* **Precision**: Float32 precision, up to 7 decimal places

## Common Embedding Models

* **OpenAI text-embedding-3-large**: 3072 dimensions
* **OpenAI text-embedding-3-small**: 1536 dimensions
* **OpenAI text-embedding-ada-002**: 1536 dimensions
* **Sentence-BERT**: 384 or 768 dimensions
* **Universal Sentence Encoder**: 512 dimensions

## Best Practices

* Use high-quality, domain-appropriate embedding models
* Normalize vectors to unit length for cosine similarity
* Ensure consistent preprocessing and tokenization
* Test with sample searches before large uploads
* Upload 100-500 vectors per request for best performance
* Use meaningful IDs for easier management and updates
* Include original text when possible for result display

## Related Operations

* [Get Documents](/python-sdk/data/get-documents) - Retrieve uploaded vectors
* [Delete Data](/python-sdk/data/delete) - Remove specific vectors
* [Search](/python-sdk/search/query) - Search uploaded vectors