> ## 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, `'ivf'`, `'ivfpq'`, or `'ivfflat'`).

### Returns

`str`: The index type.

### Example Usage

```python theme={null}
index_type = index.index_type
print(f'Index type: {index_type}')
# Output: "ivf"

if index_type == 'ivfpq':
    print('Using IVFPQ index for memory efficiency')
elif index_type == 'ivfflat':
    print('Using IVFFlat index for high accuracy')
```

***

## is\_trained

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

***

## 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 (`'ivf'`, `'ivfflat'`, or `'ivfpq'`)   |
| `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 |

### Advanced Usage

```python theme={null}
# Check index capabilities based on configuration
def analyze_index_capabilities(index):
    config = index.index_config
    index_name = index.index_name
    index_type = index.index_type
    trained = index.is_trained()
    
    print(f"\nIndex Analysis: {index_name}")
    print("=" * 40)
    print(f"Type: {index_type}")
    print(f"Dimensions: {config.get('dimension')}")
    print(f"Metric: {config.get('metric')}")
    print(f"Lists: {config.get('n_lists')}")
    print(f"Trained: {'Yes' if trained else 'No'}")
    
    # Performance characteristics
    if index_type == 'ivfflat':
        print('\nCharacteristics:')
        print('- Highest accuracy')
        print('- Slower queries')
        print('- Higher memory usage')
    elif index_type == 'ivfpq':
        print('\nCharacteristics:')
        print('- Memory efficient')
        print('- Compressed vectors')
        print(f"- PQ dimension: {config.get('pq_dim')}")
        print(f"- Quantization bits: {config.get('pq_bits')}")
    else:
        print('\nCharacteristics:')
        print('- Balanced performance')
        print('- Good accuracy/speed tradeoff')
    
    # Recommendations
    print('\nRecommendations:')
    if not trained:
        print('- Train the index for optimal performance')
    if config.get('n_lists', 0) < 100:
        print('- Consider more lists for larger datasets')

# Usage
analyze_index_capabilities(index)
```

***

## generate\_key

Generate secure encryption keys:

```python theme={null}
from cyborgdb import generate_key

# Generate a cryptographically secure 32-byte key
index_key = generate_key()  # Returns bytes object
```