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

# Getters

## index\_name

```python theme={null}
@property
index_name: str
```

Returns the name of the encrypted index.

### Returns

`str`: The unique name identifier of the index.

### Example Usage

```python theme={null}
index_name = index.index_name
print(f'Index name: {index_name}')
# Output: "my-vectors"
```

***

## index\_type

```python theme={null}
@property
index_type: str
```

Returns the type of the index (for example, `'ivfflat'`, `'ivfpq'`, or `'ivfsq'`).

<Warning>`"ivf"` is a deprecated index type that may be returned by older indexes. New indexes should use `'ivfflat'`, `'ivfpq'`, or `'ivfsq'`.</Warning>

### Returns

`str`: The index type.

### Example Usage

```python theme={null}
index_type = index.index_type
print(f'Index type: {index_type}')
# Output: "ivfflat", "ivfpq", or "ivfsq"

if index_type == 'ivfsq':
    print('Using IVFSQ index for high accuracy and memory efficiency')
elif index_type == 'ivfpq':
    print('Using IVFPQ index for memory efficiency')
elif index_type == 'ivfflat':
    print('Using IVFFlat index for high accuracy')
else:
    print('Unknown index type')
```

***

## is\_trained

```python theme={null}
def is_trained(self) -> bool
```

Returns whether the index has been trained. Training is required for optimal query performance on IVF-based indexes.

### Returns

`bool`: `True` if the index has been trained, `False` otherwise.

### Example Usage

```python theme={null}
trained = index.is_trained()
print(f'Index trained: {trained}')

if not trained:
    print('Index needs training for optimal performance')
    index.train()
```

***

## is\_training

```python theme={null}
def is_training(self) -> bool
```

Returns whether the index is currently in the process of training.

### Returns

`bool`: `True` if the index is currently training, `False` otherwise.

### Example Usage

```python theme={null}
import time

index.train()  # Start training

while index.is_training():
    print('Index is still training...')
    time.sleep(5)  # Wait before checking again
print('Index training complete')

```

***

## index\_config

```python theme={null}
@property
index_config: Dict[str, Any]
```

Returns the index configuration object as a dictionary. The structure depends on the index type.

### Returns

`Dict[str, Any]`: Configuration dictionary containing index parameters.

### Example Usage

```python theme={null}
config = index.index_config
print(f'Index configuration: {config}')

# Access common properties
print(f"Dimension: {config.get('dimension')}")
print(f"Metric: {config.get('metric')}")
print(f"Number of lists: {config.get('n_lists')}")

# Type-specific properties
if config.get('type') == 'ivfpq':
    print(f"PQ dimension: {config.get('pq_dim')}")
    print(f"PQ bits: {config.get('pq_bits')}")
```

### Configuration Properties

#### Common Properties (All Index Types)

| Property    | Type  | Description                                       |
| ----------- | ----- | ------------------------------------------------- |
| `type`      | `str` | Index type (`'ivfflat'`, `'ivfpq'`, or `'ivfsq'`) |
| `dimension` | `int` | Vector dimensionality                             |
| `metric`    | `str` | Distance metric (`'cosine'`, `'euclidean'`, etc.) |
| `n_lists`   | `int` | Number of inverted lists for clustering           |

#### IVFPQ-Specific Properties

| Property  | Type  | Description                     |
| --------- | ----- | ------------------------------- |
| `pq_dim`  | `int` | Product quantization dimension  |
| `pq_bits` | `int` | Number of bits for quantization |