> ## 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.

# 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.

```python theme={null}
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. |

<Tip>There must be at least `2 * n_lists` vector embeddings in the index prior to to calling this function.</Tip>

### Exceptions

<AccordionGroup>
  <Accordion title="ValueError">
    * Raises an exception if the index was not created or loaded yet.
  </Accordion>

  <Accordion title="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.
  </Accordion>
</AccordionGroup>

### Example Usage

```python theme={null}
# 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()
```