> ## 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 an Encrypted Index
CyborgDB uses `IVF*` index types, which leverage clustering algorithms to segment the index into smaller sections for efficient querying. These clustering algorithms must be trained on the specific data being indexed in order to adequately represent that data.
In CyborgDB Service, training is handled automatically after 10,000 vectors have been upserted. However, you can explicitly trigger training once enough vectors have been added, if you wish to specify training parameters.
You can adjust the number of vectors that will trigger automatic training by setting the `RETRAIN_THRESHOLD` environment variable. See more in the [Environment Variables](./env-vars) guide.
```python Python SDK icon="python" theme={null}
# Train the encrypted index
index.train()
# Or train with a specific number of clusters
index.train(n_lists=128)
```
```javascript JavaScript SDK icon="js" theme={null}
// Train the encrypted index
await index.train();
// Or train with a specific number of clusters
await index.train({ nLists: 128 });
```
```typescript TypeScript SDK icon="code" theme={null}
// Train the encrypted index
await index.train();
// Or train with a specific number of clusters
await index.train({ nLists: 128 });
```
```go Go SDK icon="golang" theme={null}
// Train the encrypted index
err := index.Train(context.Background(), cyborgdb.TrainParams{})
// Or train with a specific number of clusters
nLists := int32(128)
err = index.Train(context.Background(), cyborgdb.TrainParams{
NLists: &nLists,
})
```
```bash cURL icon="rectangle-terminal" theme={null}
curl -X POST "http://localhost:8000/v1/indexes/train" \
-H "X-API-Key: your-api-key" \
-H "Content-Type: application/json" \
-d '{
"index_name": "my_index",
"index_key": "your_64_character_hex_key_here"
}'
```
You must have at least 10,000 or `2 * n_lists` number of vectors in the index (ingested via `upsert`) before you can call `train`.
## Training Parameters
Parameters are available to customize the training process:
| Parameter | Type | Default | Description |
| ------------ | ------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `n_lists` | `int` | `None` (auto) | *(Optional)* Number of inverted index lists to create in the index. When `None` or omitted, auto-determines based on the number of vectors in the index. |
| `batch_size` | `int` | `None` | *(Optional)* Number of vectors to process per training batch. When `None`, the server uses 2048. |
| `max_iters` | `int` | `None` | *(Optional)* Maximum number of training iterations. When `None`, the server uses 100. |
| `tolerance` | `float` | `None` | *(Optional)* Convergence tolerance for training completion. When `None`, the server uses 1e-6. |
| `max_memory` | `int` | `None` (0) | *(Optional)* Maximum memory usage in MB. When `None` or 0, no memory limit is applied. |
`n_lists` is the number of clusters into which each vector in the index can be categorized. Typically, the higher the value, the higher the recall (but also the slower the indexing process). As a good rule of thumbs, `n_lists` should be:
* A base-2 number (e.g., `2,048`, `4,096`). Not a requirement, but yields performance optimizations.
* Each cluster should have between `100` - `10,000` vectors; so `n_lists` should be roughly between `1/100` - `1/10,000` of the total number of items which will be indexed.
If not specified, CyborgDB will auto-determine the best `n_lists` value based on the number of vectors in the index.
## Warnings with Large Untrained Queries
While training is technically optional (you can use CyborgDB without ever calling `train`), it is recommended that you do so once you have a large number of vectors in the index (e.g., `> 50,000`). If you don't, and you call `query`, you will see a warning in the console, stating:
```
Warning: querying untrained index with more than 50000 indexed vectors.
```
## API Reference
For more information on training an encrypted index, refer to the API reference:
REST API reference for `/v1/indexes/train`
API reference for `train()` in Python
API reference for `train()` in JavaScript/TypeScript
API reference for `Train()` in Go