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Documentation Index

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index_name

@property
index_name: str
Returns the name of the encrypted index.

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Returns

str: The unique name identifier of the index.

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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').

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Returns

str: The index type.

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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.

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Returns

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

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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.

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Returns

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

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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')}")

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Configuration Properties

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Common Properties (All Index Types)

PropertyTypeDescription
typestrIndex type ('ivf', 'ivfflat', or 'ivfpq')
dimensionintVector dimensionality
metricstrDistance metric ('cosine', 'euclidean', etc.)
n_listsintNumber of inverted lists for clustering

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IVFPQ-Specific Properties

PropertyTypeDescription
pq_dimintProduct quantization dimension
pq_bitsintNumber of bits for quantization

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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