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Trains the encrypted index to optimize it for efficient similarity search queries. Training is essential for IVF-based indexes to achieve optimal query performance and accuracy.
In CyborgDB Service, training is typically handled automatically by the service. However, you can explicitly trigger training once enough vectors have been added.
index.train(
    n_lists=None,
    batch_size=None,
    max_iters=None,
    tolerance=None
)

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Parameters

ParameterTypeDefaultDescription
n_listsintNone(Optional) Number of inverted lists to use for the index. When None, auto-selects based on the dataset size
batch_sizeintNone(Optional) Number of vectors to process per training batch. When None, the server uses 2048
max_itersintNone(Optional) Maximum number of training iterations. When None, the server uses 100
tolerancefloatNone(Optional) Convergence tolerance for training completion. When None, the server uses 1e-6
Training is a compute-intensive operation that may take several seconds to minutes depending on the index size and configuration.

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Returns

None

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Exceptions

  • Throws if the API request fails due to network connectivity issues.
  • Throws if authentication fails (invalid API key).
  • Throws if the encryption key is invalid for the specified index.
  • Throws if there are insufficient resources to complete training.
  • Throws if the index has no vectors to train on.
  • Throws if the index configuration is incompatible with training.
  • Throws if training parameters are out of valid ranges.
  • Throws if training fails to converge within the specified parameters.

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Example Usage

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Basic Index Training

# Train the index after adding the data
index.train()

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Custom Training Parameters

# Train with custom parameters for large dataset
index.train(
    n_lists=100,        # Number of inverted lists
    batch_size=4096,    # Larger batches for better performance
    max_iters=200,      # More iterations for better convergence
    tolerance=1e-7      # Stricter convergence criteria
)