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To create an encrypted index, you need to specify an index name (must be unique) and an index key: 
import cyborgdb_core as cyborgdb
import secrets

# Using `threadsafememory` storage for this example
index_location = cyborgdb.DBConfig("threadsafememory") 
config_location = cyborgdb.DBConfig("threadsafememory")

# Get your API key
api_key = "your_api_key_here"  # Replace with your actual API key

# Create a client
client = cyborgdb.Client(
    api_key=api_key, 
    index_location=index_location, 
    config_location=config_location
)

# Generate an encryption key for the index
index_key = secrets.token_bytes(32)

# Create an encrypted index
index = client.create_index(
    index_name="my_index", 
    index_key=index_key
)
This creates a new encrypted index with the IVFSQ (16-bit) type by default. For more details on IVFSQ and other index options, see Configure an Encrypted Index.
The example above creates a random 32 byte (256-bit) index key. This is fine for evaluation purposes, but for production use, we recommend that you use an HSM or KMS solution. For more details, see Managing Encryption Keys.

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Automatic Embedding Generation

This feature is only available in Python. To use it, use pip install cyborgdb-core[embeddings]
In the Python version of CyborgDB, you can enable automatic embedding generation for the encrypted index by setting embedding_model in create_index():
Python
import cyborgdb_core as cyborgdb
import secrets

# Using `threadsafememory` storage for this example
index_location = cyborgdb.DBConfig("threadsafememory")
config_location = cyborgdb.DBConfig("threadsafememory")
items_location = cyborgdb.DBConfig("threadsafememory")

# Get your API key
api_key = "your_api_key_here"  # Replace with your actual API key

# Create a client
client = cyborgdb.Client(
    api_key=api_key, 
    index_location=index_location, 
    config_location=config_location, 
    items_location=items_location
)

# Generate an encryption key for the index
index_key = secrets.token_bytes(32)

# Set embedding model (from HuggingFace)
embedding_model = "all-MiniLM-L6-v2"

# Create an encrypted index with managed embedding generation
index = client.create_index(
    index_name="my_index", 
    index_key=index_key, 
    embedding_model=embedding_model
)

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API Reference

For more information on creating encrypted indexes, refer to the API reference:

Python API Reference

API reference for create_index() in Python

C++ API Reference

API reference for CreateIndex() in C++