To create an encrypted index, you need to specify an index name (must be unique), an index key, and an index configuration. Here’s an example with an IVFFlat index type:

import cyborg_vector_search_py as cvs
import secrets

# Using `memory` storage for this example
index_location = cvs.DBConfig("memory") 
config_location = cvs.DBConfig("memory")

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

# Create an IVFFlat index config (can also be IVF/IVFPQ)
# Using an example vector dimension of 128, and number of lists of 1024
index_config = cvs.IndexIVFFlat(dimension=128, n_lists=1024)

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

# Create an encrypted index
index = client.create_index("my_index", index_key, index_config)

This creates a new encrypted index with the IVFFlat type. For more details on IVFFlat 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.

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Encrypted Index Caching

For improved query performance, you can enable encrypted index caching by setting a max_cache_size:

import cyborg_vector_search_py as cvs
import secrets

# Using `memory` storage for this example
index_location = cvs.DBConfig("memory") 
config_location = cvs.DBConfig("memory")

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

# Example index config
index_config = cvs.IndexIVFFlat(dimension=128, n_lists=1024)

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

# Set max cache size at 1MB
max_cache_size = 1000000

# Create an encrypted index
index = client.create_index("my_index", index_key, index_config, max_cache_size)

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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++

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