> ## 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.

# Create Index

Creates and returns an `EncryptedIndex` instance based on the provided configuration.

```python theme={null}
client.create_index(
    index_name,
    index_key,
    index_config,
    embedding_model,
    max_cache_size
)
```

### Parameters

| Parameter         | Type          | Default | Description                             |
| ----------------- | ------------- | ------- | --------------------------------------- |
| `index_name`      | `str`         | -       | Unique name for the index               |
| `index_key`       | `bytes`       | -       | 32-byte encryption key                  |
| `index_config`    | `IndexConfig` | -       | Index configuration object              |
| `embedding_model` | `str`         | `None`  | *(Optional)* Embedding model identifier |

### Returns

An instance of `EncryptedIndex` configured with the provided parameters.

### Exceptions

<AccordionGroup>
  <Accordion title="Error">
    * Throws if the index name already exists on the server.
    * Throws if the index configuration is invalid or missing required parameters.
    * Throws if the encryption key is not exactly 32 bytes.
    * Throws if the embedding model is not supported by the server.
  </Accordion>

  <Accordion title="Network/API Errors">
    * Throws if the API request fails due to network issues.
    * Throws if the server returns an HTTP error status.
    * Throws if authentication fails (invalid API key).
  </Accordion>
</AccordionGroup>

### Example Usage

Basic usage of the `create_index` method to create an IVF-Flat index:

```python theme={null}
from cyborgdb import Client, IndexIVFFlat, generate_key

# Initialize client
client = Client('https://localhost:8000', 'your-api-key')

# Generate a secure encryption key
index_key = generate_key()

# Create a basic IVF-Flat index
config = IndexIVFFlat(dimension=384, n_lists=100, metric="cosine")
index = client.create_index('documents', index_key, config)
```

#### IVFPQ Index with Embedding Model

```python theme={null}
# Create index with embedding model and cache
config = IndexIVFPQ(dimension=768, n_lists=50, pq_bits=8, pq_dim=64, metric="cosine")
index = client.create_index(
    index_name='example_index_name',
    index_key=index_key,
    index_config=config,
    embedding_model='all-MiniLM-L6-v2'
)
```