Getters

index_name

@property
index_name: str

Returns the name of the encrypted index.

Returns

str: The unique name identifier of the index.

Example Usage

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

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index_type

@property
index_type: str

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

Returns

str: The index type.

Example Usage

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')

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is_trained

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

trained = index.is_trained()
print(f'Index trained: {trained}')

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

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index_config

@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

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

# 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)

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generate_key

Generate secure encryption keys:

from cyborgdb import generate_key

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