> ## 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.

# Getters

<Note>v0.17 replaces the v0.16 getters (`index_type`, `index_config`) with explicit properties for the single DiskIVF index type: `dimension`, `metric`, and `n_lists`. Most are lazily fetched and cached on first read.</Note>

## index\_name

```python theme={null}
@property
index_name: str
```

Returns the name of the encrypted index. Always available locally — no network call.

### Example Usage

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

***

## dimension

```python theme={null}
@property
dimension: int
```

Vector dimensionality.

* Returns the real dimension when `create_index` was called with an explicit `dimension`, or after the first upsert (auto-detect).
* Returns `0` if the index was created with auto-detect AND no upsert has happened yet.

Cached on first read; subsequent reads do not hit the server.

### Example Usage

```python theme={null}
print(f'Vector dimensionality: {index.dimension}')
# Output: 384
```

***

## metric

```python theme={null}
@property
metric: str
```

Distance metric: `"euclidean"`, `"squared_euclidean"`, or `"cosine"`. Cached on first read alongside `dimension`.

### Example Usage

```python theme={null}
print(f'Metric: {index.metric}')
# Output: "euclidean"
```

***

## n\_lists

```python theme={null}
@property
n_lists: int
```

Number of inverted lists in the IVF index.

* Returns `1` for untrained indexes.
* Returns the trained cluster count after `train()` completes.

Unlike `dimension` and `metric`, `n_lists` is **fetched fresh on every read** so callers reading immediately after training see the new value.

### Example Usage

```python theme={null}
print(f'Lists: {index.n_lists}')
# Output: 1024  (after training)
```

***

## is\_trained

```python theme={null}
def is_trained(self) -> bool
```

Returns whether the index has been trained. Training is required for optimal query performance on large indexes.

### Returns

`bool`: `True` if the index has been trained, `False` otherwise (or if the underlying describe call fails).

### Example Usage

```python theme={null}
if not index.is_trained():
    print('Index needs training for optimal performance')
    index.train()
```

***

## is\_training

```python theme={null}
def is_training(self) -> bool
```

Returns whether this index is currently in the process of training (either actively training or queued).

### Returns

`bool`: `True` if the index is currently training or queued, `False` otherwise.

### Example Usage

```python theme={null}
import time

index.train()  # Kicks off training

while index.is_training():
    print('Index is still training...')
    time.sleep(5)
print('Index training complete')
```