Train Index

Builds the index using the specified training configuration. Required before efficient querying. Prior to calling this, all queries will be conducted using encrypted exhaustive search. After, they will be conducted using encrypted ANN search.

def train(self, batch_size: int = 2048, max_iters: int = 100, tolerance: float = 1e-6, max_memory: int = 0)

Parameters

Parameter	Type	Default	Description
batch_size	int	2048	(Optional) Size of each batch for training.
max_iters	int	100	(Optional) Maximum number of iterations for training.
tolerance	float	1e-6	(Optional) Convergence tolerance for training.
max_memory	int	0	(Optional) Maximum memory to use for training. 0 sets no limit.

There must be at least 2 * n_lists vector embeddings in the index prior to to calling this function.

Exceptions

ValueError

• Raises an exception if the index was not created or loaded yet.

RuntimeError

• Raises an exception if there are not enough vector embeddings in the index for training (must be at least 2 * n_lists).
• Raises an exception if the index could not be trained.

Example Usage

# Load index
index = client.load_index(index_name=index_name, index_key=index_key)

# Train the index with custom settings
index.train(batch_size=128, max_iters=10, tolerance=1e-4)

# Train with default settings (auto-selected configuration)
index.train()